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Colson C, Wang Y, Atherton J, Su X. SLC45A4 encodes a mitochondrial putrescine transporter that promotes GABA de novo synthesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.23.604788. [PMID: 39091866 PMCID: PMC11291067 DOI: 10.1101/2024.07.23.604788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Solute carriers (SLC) are membrane proteins that facilitate the transportation of ions and metabolites across either the plasma membrane or the membrane of intracellular organelles. With more than 450 human genes annotated as SLCs, many of them are still orphan transporters without known biochemical functions. We developed a metabolomic-transcriptomic association analysis, and we found that the expression of SLC45A4 has a strong positive correlation with the cellular level of γ-aminobutyric acid (GABA). Using mass spectrometry and the stable isotope tracing approach, we demonstrated that SLC45A4 promotes GABA de novo synthesis through the Arginine/Ornithine/Putrescine (AOP) pathway. SLC45A4 functions as a putrescine transporter localized to the mitochondrial membrane to facilitate GABA production. Taken together, our results revealed a new biochemical mechanism where SLC45A4 controls GABA production.
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2
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Yang JJ, Goff A, Wild DJ, Ding Y, Annis A, Kerber R, Foote B, Passi A, Duerksen JL, London S, Puhl AC, Lane TR, Braunstein M, Waddell SJ, Ekins S. Computational drug repositioning identifies niclosamide and tribromsalan as inhibitors of Mycobacterium tuberculosis and Mycobacterium abscessus. Tuberculosis (Edinb) 2024; 146:102500. [PMID: 38432118 PMCID: PMC10978224 DOI: 10.1016/j.tube.2024.102500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/20/2024] [Accepted: 02/24/2024] [Indexed: 03/05/2024]
Abstract
Tuberculosis (TB) is still a major global health challenge, killing over 1.5 million people each year, and hence, there is a need to identify and develop novel treatments for Mycobacterium tuberculosis (M. tuberculosis). The prevalence of infections caused by nontuberculous mycobacteria (NTM) is also increasing and has overtaken TB cases in the United States and much of the developed world. Mycobacterium abscessus (M. abscessus) is one of the most frequently encountered NTM and is difficult to treat. We describe the use of drug-disease association using a semantic knowledge graph approach combined with machine learning models that has enabled the identification of several molecules for testing anti-mycobacterial activity. We established that niclosamide (M. tuberculosis IC90 2.95 μM; M. abscessus IC90 59.1 μM) and tribromsalan (M. tuberculosis IC90 76.92 μM; M. abscessus IC90 147.4 μM) inhibit M. tuberculosis and M. abscessus in vitro. To investigate the mode of action, we determined the transcriptional response of M. tuberculosis and M. abscessus to both compounds in axenic log phase, demonstrating a broad effect on gene expression that differed from known M. tuberculosis inhibitors. Both compounds elicited transcriptional responses indicative of respiratory pathway stress and the dysregulation of fatty acid metabolism.
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Affiliation(s)
- Jeremy J Yang
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA; Data2Discovery, Inc., Bloomington, IN, USA; Department of Internal Medicine Translational Informatics Division, University of New Mexico, Albuquerque, NM, USA
| | - Aaron Goff
- Department of Global Health and Infection, Brighton & Sussex Medical School, University of Sussex, UK
| | - David J Wild
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA; Data2Discovery, Inc., Bloomington, IN, USA
| | - Ying Ding
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA; Data2Discovery, Inc., Bloomington, IN, USA; School of Information, Dell Medical School, University of Texas, Austin, TX, USA
| | - Ayano Annis
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, NC, 27599, USA
| | | | | | - Anurag Passi
- Department of Pediatrics, UC San Diego, San Diego, CA, USA
| | | | | | - Ana C Puhl
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA
| | - Thomas R Lane
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA
| | - Miriam Braunstein
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, NC, 27599, USA
| | - Simon J Waddell
- Department of Global Health and Infection, Brighton & Sussex Medical School, University of Sussex, UK
| | - Sean Ekins
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA.
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3
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Soiland-Reyes S, Goble C, Groth P. Evaluating FAIR Digital Object and Linked Data as distributed object systems. PeerJ Comput Sci 2024; 10:e1781. [PMID: 38855229 PMCID: PMC11157569 DOI: 10.7717/peerj-cs.1781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 12/06/2023] [Indexed: 06/11/2024]
Abstract
FAIR Digital Object (FDO) is an emerging concept that is highlighted by European Open Science Cloud (EOSC) as a potential candidate for building an ecosystem of machine-actionable research outputs. In this work we systematically evaluate FDO and its implementations as a global distributed object system, by using five different conceptual frameworks that cover interoperability, middleware, FAIR principles, EOSC requirements and FDO guidelines themself. We compare the FDO approach with established Linked Data practices and the existing Web architecture, and provide a brief history of the Semantic Web while discussing why these technologies may have been difficult to adopt for FDO purposes. We conclude with recommendations for both Linked Data and FDO communities to further their adaptation and alignment.
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Affiliation(s)
- Stian Soiland-Reyes
- Department of Computer Science, The University of Manchester, Manchester, UK
- Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
| | - Carole Goble
- Department of Computer Science, The University of Manchester, Manchester, UK
| | - Paul Groth
- Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
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4
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Grandits M, Ecker GF. Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling. Curr Drug Res Rev 2024; 16:81-93. [PMID: 37157206 PMCID: PMC11340286 DOI: 10.2174/2589977515666230508123041] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 03/15/2023] [Accepted: 03/28/2023] [Indexed: 05/10/2023]
Abstract
The study of transporter proteins is key to understanding the mechanism behind multidrug resistance and drug-drug interactions causing severe side effects. While ATP-binding transporters are well-studied, solute carriers illustrate an understudied family with a high number of orphan proteins. To study these transporters, in silico methods can be used to shed light on the basic molecular machinery by studying protein-ligand interactions. Nowadays, computational methods are an integral part of the drug discovery and development process. In this short review, computational approaches, such as machine learning, are discussed, which try to tackle interactions between transport proteins and certain compounds to locate target proteins. Furthermore, a few cases of selected members of the ATP binding transporter and solute carrier family are covered, which are of high interest in clinical drug interaction studies, especially for regulatory agencies. The strengths and limitations of ligand-based and structure-based methods are discussed to highlight their applicability for different studies. Furthermore, the combination of multiple approaches can improve the information obtained to find crucial amino acids that explain important interactions of protein-ligand complexes in more detail. This allows the design of drug candidates with increased activity towards a target protein, which further helps to support future synthetic efforts.
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Affiliation(s)
- Melanie Grandits
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Gerhard F. Ecker
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
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5
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Niazi SK, Mariam Z. Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis. Pharmaceuticals (Basel) 2023; 17:22. [PMID: 38256856 PMCID: PMC10819513 DOI: 10.3390/ph17010022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 12/13/2023] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.
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Affiliation(s)
| | - Zamara Mariam
- Centre for Health and Life Sciences, Coventry University, Coventry City CV1 5FB, UK
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6
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Daza D, Alivanistos D, Mitra P, Pijnenburg T, Cochez M, Groth P. BioBLP: a modular framework for learning on multimodal biomedical knowledge graphs. J Biomed Semantics 2023; 14:20. [PMID: 38066573 PMCID: PMC10709903 DOI: 10.1186/s13326-023-00301-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Knowledge graphs (KGs) are an important tool for representing complex relationships between entities in the biomedical domain. Several methods have been proposed for learning embeddings that can be used to predict new links in such graphs. Some methods ignore valuable attribute data associated with entities in biomedical KGs, such as protein sequences, or molecular graphs. Other works incorporate such data, but assume that entities can be represented with the same data modality. This is not always the case for biomedical KGs, where entities exhibit heterogeneous modalities that are central to their representation in the subject domain. OBJECTIVE We aim to understand how to incorporate multimodal data into biomedical KG embeddings, and analyze the resulting performance in comparison with traditional methods. We propose a modular framework for learning embeddings in KGs with entity attributes, that allows encoding attribute data of different modalities while also supporting entities with missing attributes. We additionally propose an efficient pretraining strategy for reducing the required training runtime. We train models using a biomedical KG containing approximately 2 million triples, and evaluate the performance of the resulting entity embeddings on the tasks of link prediction, and drug-protein interaction prediction, comparing against methods that do not take attribute data into account. RESULTS In the standard link prediction evaluation, the proposed method results in competitive, yet lower performance than baselines that do not use attribute data. When evaluated in the task of drug-protein interaction prediction, the method compares favorably with the baselines. Further analyses show that incorporating attribute data does outperform baselines over entities below a certain node degree, comprising approximately 75% of the diseases in the graph. We also observe that optimizing attribute encoders is a challenging task that increases optimization costs. Our proposed pretraining strategy yields significantly higher performance while reducing the required training runtime. CONCLUSION BioBLP allows to investigate different ways of incorporating multimodal biomedical data for learning representations in KGs. With a particular implementation, we find that incorporating attribute data does not consistently outperform baselines, but improvements are obtained on a comparatively large subset of entities below a specific node-degree. Our results indicate a potential for improved performance in scientific discovery tasks where understudied areas of the KG would benefit from link prediction methods.
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Affiliation(s)
- Daniel Daza
- Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- University of Amsterdam, Amsterdam, The Netherlands.
- Discovery Lab, Elsevier, Amsterdam, The Netherlands.
| | - Dimitrios Alivanistos
- Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Discovery Lab, Elsevier, Amsterdam, The Netherlands.
| | | | | | - Michael Cochez
- Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Discovery Lab, Elsevier, Amsterdam, The Netherlands
| | - Paul Groth
- University of Amsterdam, Amsterdam, The Netherlands
- Discovery Lab, Elsevier, Amsterdam, The Netherlands
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7
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Pascazio L, Rihm S, Naseri A, Mosbach S, Akroyd J, Kraft M. Chemical Species Ontology for Data Integration and Knowledge Discovery. J Chem Inf Model 2023; 63:6569-6586. [PMID: 37883649 PMCID: PMC10647085 DOI: 10.1021/acs.jcim.3c00820] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 10/13/2023] [Accepted: 10/13/2023] [Indexed: 10/28/2023]
Abstract
Web ontologies are important tools in modern scientific research because they provide a standardized way to represent and manage web-scale amounts of complex data. In chemistry, a semantic database for chemical species is indispensable for its ability to interrelate and infer relationships, enabling a more precise analysis and prediction of chemical behavior. This paper presents OntoSpecies, a web ontology designed to represent chemical species and their properties. The ontology serves as a core component of The World Avatar knowledge graph chemistry domain and includes a wide range of identifiers, chemical and physical properties, chemical classifications and applications, and spectral information associated with each species. The ontology includes provenance and attribution metadata, ensuring the reliability and traceability of data. Most of the information about chemical species are sourced from PubChem and ChEBI data on the respective compound Web pages using a software agent, making OntoSpecies a comprehensive semantic database of chemical species able to solve novel types of problems in the field. Access to this reliable source of chemical data is provided through a SPARQL end point. The paper presents example use cases to demonstrate the contribution of OntoSpecies in solving complex tasks that require integrated semantically searchable chemical data. The approach presented in this paper represents a significant advancement in the field of chemical data management, offering a powerful tool for representing, navigating, and analyzing chemical information to support scientific research.
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Affiliation(s)
- Laura Pascazio
- CARES,
Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, CREATE Tower, #05-05, Singapore 138602, Singapore
| | - Simon Rihm
- CARES,
Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, CREATE Tower, #05-05, Singapore 138602, Singapore
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Ali Naseri
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Sebastian Mosbach
- CARES,
Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, CREATE Tower, #05-05, Singapore 138602, Singapore
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
- CMCL
Innovations, Sheraton
House, Castle Park, Cambridge CB3 0AX, U.K.
| | - Jethro Akroyd
- CARES,
Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, CREATE Tower, #05-05, Singapore 138602, Singapore
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
- CMCL
Innovations, Sheraton
House, Castle Park, Cambridge CB3 0AX, U.K.
| | - Markus Kraft
- CARES,
Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, CREATE Tower, #05-05, Singapore 138602, Singapore
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
- CMCL
Innovations, Sheraton
House, Castle Park, Cambridge CB3 0AX, U.K.
- School
of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore
- The
Alan Turing Institute, 96 Euston Rd., London NW1 2DB, U.K.
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8
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Martens M, Stierum R, Schymanski EL, Evelo CT, Aalizadeh R, Aladjov H, Arturi K, Audouze K, Babica P, Berka K, Bessems J, Blaha L, Bolton EE, Cases M, Damalas DΕ, Dave K, Dilger M, Exner T, Geerke DP, Grafström R, Gray A, Hancock JM, Hollert H, Jeliazkova N, Jennen D, Jourdan F, Kahlem P, Klanova J, Kleinjans J, Kondic T, Kone B, Lynch I, Maran U, Martinez Cuesta S, Ménager H, Neumann S, Nymark P, Oberacher H, Ramirez N, Remy S, Rocca-Serra P, Salek RM, Sallach B, Sansone SA, Sanz F, Sarimveis H, Sarntivijai S, Schulze T, Slobodnik J, Spjuth O, Tedds J, Thomaidis N, Weber RJ, van Westen GJ, Wheelock CE, Williams AJ, Witters H, Zdrazil B, Županič A, Willighagen EL. ELIXIR and Toxicology: a community in development. F1000Res 2023; 10:ELIXIR-1129. [PMID: 37842337 PMCID: PMC10568213 DOI: 10.12688/f1000research.74502.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/28/2023] [Indexed: 10/17/2023] Open
Abstract
Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.
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Affiliation(s)
- Marvin Martens
- Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, 6229 ER, The Netherlands
| | - Rob Stierum
- Risk Analysis for Products In Development (RAPID), Netherlands Organisation for applied scientific research TNO, Utrecht, 3584 CB, The Netherlands
| | - Emma L. Schymanski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, 4367, Luxembourg
| | - Chris T. Evelo
- Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, 6229 ER, The Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6229 EN, The Netherlands
| | - Reza Aalizadeh
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens, 15771, Greece
| | - Hristo Aladjov
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, 1113, Bulgaria
| | - Kasia Arturi
- Department Environmental Chemistry, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, 8600, Switzerland
| | | | - Pavel Babica
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Karel Berka
- Department of Physical Chemistry, Palacky University Olomouc, Olomouc, 77146, Czech Republic
| | | | - Ludek Blaha
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Evan E. Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | | | - Dimitrios Ε. Damalas
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens, 15771, Greece
| | - Kirtan Dave
- School of Science, GSFC University, Gujarat, 391750, India
| | - Marco Dilger
- Forschungs- und Beratungsinstitut Gefahrstoffe (FoBiG) GmbH, Freiburg im Breisgau, 79106, Germany
| | | | - Daan P. Geerke
- AIMMS Division of Molecular Toxicology, Vrije Universiteit, Amsterdam, 1081 HZ, The Netherlands
| | - Roland Grafström
- Department of Toxicology, Misvik Biology, Turku, 20520, Finland
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, 17177, Sweden
| | - Alasdair Gray
- Department of Computer Science, Heriot-Watt University, Edinburgh, UK
| | | | - Henner Hollert
- Department Evolutionary Ecology & Environmental Toxicology (E3T), Goethe-University, Frankfurt, D-60438, Germany
| | | | - Danyel Jennen
- Department of Toxicogenomics, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Fabien Jourdan
- MetaboHUB, French metabolomics infrastructure in Metabolomics and Fluxomics, Toulouse, France
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, Toulouse, France
| | - Pascal Kahlem
- Scientific Network Management SL, Barcelona, 08015, Spain
| | - Jana Klanova
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Jos Kleinjans
- Department of Toxicogenomics, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Todor Kondic
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, 4367, Luxembourg
| | - Boï Kone
- Faculty of Pharmacy, Malaria Research and Training Center, Bamako, BP:1805, Mali
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, UK, Birmingham, B15 2TT, UK
| | - Uko Maran
- Institute of Chemistry, University of Tartu, Tartu, 50411, Estonia
| | | | - Hervé Ménager
- Institut Français de Bioinformatique, Evry, F-91000, France
- Bioinformatics and Biostatistics Hub, Institut Pasteur, Paris, F-75015, France
| | - Steffen Neumann
- Research group Bioinformatics and Scientific Data, Leibniz Institute of Plant Biochemistry, Halle, 06120, Germany
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, 17177, Sweden
| | - Herbert Oberacher
- Institute of Legal Medicine and Core Facility Metabolomics, Medical University of Innsbruck, Innsbruck, A-6020, Austria
| | - Noelia Ramirez
- Institut d'Investigacio Sanitaria Pere Virgili-Universitat Rovira i Virgili, Tarragona, 43007, Spain
| | | | - Philippe Rocca-Serra
- Data Readiness Group, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Reza M. Salek
- International Agency for Research on Cancer, World Health Organisation, Lyon, 69372, France
| | - Brett Sallach
- Department of Environment and Geography, University of York, UK, York, YO10 5NG, UK
| | | | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona, 08003, Spain
| | | | | | - Tobias Schulze
- Helmholtz Centre for Environmental Research - UFZ, Leipzig, 04318, Germany
| | | | - Ola Spjuth
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, SE-75124, Sweden
| | - Jonathan Tedds
- ELIXIR Hub, Wellcome Genome Campus, Cambridge, CB10 1SD, UK
| | - Nikolaos Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens, 15771, Greece
| | - Ralf J.M. Weber
- School of Biosciences, University of Birmingham, UK, Birmingham, B15 2TT, UK
| | - Gerard J.P. van Westen
- Division of Drug Discovery and Safety, Leiden Academic Center for Drug Research, Leiden, 2333 CC, The Netherlands
| | - Craig E. Wheelock
- Department of Respiratory Medicine and Allergy, Karolinska University Hospital, Stockholm SE-141-86, Sweden
- Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, 17177, Sweden
| | - Antony J. Williams
- Center for Computational Toxicology and Exposure, United States Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | | | - Barbara Zdrazil
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, 1090, Austria
| | - Anže Županič
- Department Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, 1000, Slovenia
| | - Egon L. Willighagen
- Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, 6229 ER, The Netherlands
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9
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van Rijn J, Afantitis A, Culha M, Dusinska M, Exner TE, Jeliazkova N, Longhin EM, Lynch I, Melagraki G, Nymark P, Papadiamantis AG, Winkler DA, Yilmaz H, Willighagen E. European Registry of Materials: global, unique identifiers for (undisclosed) nanomaterials. J Cheminform 2022; 14:57. [PMID: 36002868 PMCID: PMC9400299 DOI: 10.1186/s13321-022-00614-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 05/21/2022] [Indexed: 11/25/2022] Open
Abstract
Management of nanomaterials and nanosafety data needs to operate under the FAIR (findability, accessibility, interoperability, and reusability) principles and this requires a unique, global identifier for each nanomaterial. Existing identifiers may not always be applicable or sufficient to definitively identify the specific nanomaterial used in a particular study, resulting in the use of textual descriptions in research project communications and reporting. To ensure that internal project documentation can later be linked to publicly released data and knowledge for the specific nanomaterials, or even to specific batches and variants of nanomaterials utilised in that project, a new identifier is proposed: the European Registry of Materials Identifier. We here describe the background to this new identifier, including FAIR interoperability as defined by FAIRSharing, identifiers.org, Bioregistry, and the CHEMINF ontology, and show how it complements other identifiers such as CAS numbers and the ongoing efforts to extend the InChI identifier to cover nanomaterials. We provide examples of its use in various H2020-funded nanosafety projects.
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Affiliation(s)
- Jeaphianne van Rijn
- Department of Bioinformatics-BiGCaT, NUTRIM, FHML, Maastricht University, Maastricht, The Netherlands.
| | | | - Mustafa Culha
- Sabanci University Nanotechnology Research and Application Center (SUNUM), Tuzla, 34956, Istanbul, Turkey
| | - Maria Dusinska
- Health Effects Laboratory, Department of Environmental Chemistry, Norwegian Institute for Air Research, 2007, Kjeller, Norway
| | | | | | - Eleonora Marta Longhin
- Health Effects Laboratory, Department of Environmental Chemistry, Norwegian Institute for Air Research, 2007, Kjeller, Norway
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, B15 2TT, UK
| | | | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Anastasios G Papadiamantis
- NovaMechanics Ltd., 1070, Nicosia, Cyprus.,School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, B15 2TT, UK
| | - David A Winkler
- School of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Bundoora, Australia.,Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Australia.,School of Pharmacy, University of Nottingham, Nottingham, UK
| | - Hulya Yilmaz
- Sabanci University Nanotechnology Research and Application Center (SUNUM), Tuzla, 34956, Istanbul, Turkey
| | - Egon Willighagen
- Department of Bioinformatics-BiGCaT, NUTRIM, FHML, Maastricht University, Maastricht, The Netherlands
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10
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Abstract
ComptoxAI is a new data infrastructure for computational and artificial intelligence research in predictive toxicology. Here, we describe and showcase ComptoxAI's graph-structured knowledge base in the context of three real-world use-cases, demonstrating that it can rapidly answer complex questions about toxicology that are infeasible using previous technologies and data resources. These use-cases each demonstrate a tool for information retrieval from the knowledge base being used to solve a specific task: The "shortest path" module is used to identify mechanistic links between perfluorooctanoic acid (PFOA) exposure and nonalcoholic fatty liver disease; the "expand network" module identifies communities that are linked to dioxin toxicity; and the quantitative structure-activity relationship (QSAR) dataset generator predicts pregnane X receptor agonism in a set of 4,021 pesticide ingredients. The contents of ComptoxAI's source data are rigorously aggregated from a diverse array of public third-party databases, and ComptoxAI is designed as a free, public, and open-source toolkit to enable diverse classes of users including biomedical researchers, public health and regulatory officials, and the general public to predict toxicology of unknowns and modes of action.
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Affiliation(s)
- Joseph D Romano
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Center of Excellence in Environmental Toxicology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Yun Hao
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California 90069, United States
| | - Trevor M Penning
- Center of Excellence in Environmental Toxicology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Department of Systems Pharmacology & Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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11
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Charting Past, Present, and Future Research in the Semantic Web and Interoperability. FUTURE INTERNET 2022. [DOI: 10.3390/fi14060161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Huge advances in peer-to-peer systems and attempts to develop the semantic web have revealed a critical issue in information systems across multiple domains: the absence of semantic interoperability. Today, businesses operating in a digital environment require increased supply-chain automation, interoperability, and data governance. While research on the semantic web and interoperability has recently received much attention, a dearth of studies investigates the relationship between these two concepts in depth. To address this knowledge gap, the objective of this study is to conduct a review and bibliometric analysis of 3511 Scopus-registered papers on the semantic web and interoperability published over the past two decades. In addition, the publications were analyzed using a variety of bibliometric indicators, such as publication year, journal, authors, countries, and institutions. Keyword co-occurrence and co-citation networks were utilized to identify the primary research hotspots and group the relevant literature. The findings of the review and bibliometric analysis indicate the dominance of conference papers as a means of disseminating knowledge and the substantial contribution of developed nations to the semantic web field. In addition, the keyword co-occurrence network analysis reveals a significant emphasis on semantic web languages, sensors and computing, graphs and models, and linking and integration techniques. Based on the co-citation clustering, the Internet of Things, semantic web services, ontology mapping, building information modeling, bioinformatics, education and e-learning, and semantic web languages were identified as the primary themes contributing to the flow of knowledge and the growth of the semantic web and interoperability field. Overall, this review substantially contributes to the literature and increases scholars’ and practitioners’ awareness of the current knowledge composition and future research directions of the semantic web field.
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12
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Wang H, Du H, Qi G, Chen H, Hu W, Chen Z. Construction of A Linked Dataset of COVID-19 Knowledge Graphs: Development and Applications. JMIR Med Inform 2022; 10:e37215. [PMID: 35476822 PMCID: PMC9109781 DOI: 10.2196/37215] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/23/2022] [Accepted: 04/26/2022] [Indexed: 01/22/2023] Open
Abstract
Background With the continuous spread of COVID-19, information about the worldwide pandemic is exploding. Therefore, it is necessary and significant to organize such a large amount of information. As the key branch of artificial intelligence, a knowledge graph (KG) is helpful to structure, reason, and understand data. Objective To improve the utilization value of the information and effectively aid researchers to combat COVID-19, we have constructed and successively released a unified linked data set named OpenKG-COVID19, which is one of the largest existing KGs related to COVID-19. OpenKG-COVID19 includes 10 interlinked COVID-19 subgraphs covering the topics of encyclopedia, concept, medical, research, event, health, epidemiology, goods, prevention, and character. Methods In this paper, we introduce the key techniques exploited in building COVID-19 KGs in a top-down manner. First, the schema of the modeling process for each KG in OpenKG-COVID19 is described. Second, we propose different methods for extracting knowledge from open government sites, professional texts, public domain–specific sources, and public encyclopedia sites. The curated 10 COVID-19 KGs are further linked together at both the schema and data levels. In addition, we present the naming convention for OpenKG-COVID19. Results OpenKG-COVID19 has more than 2572 concepts, 329,600 entities, 513 properties, and 2,687,329 facts, and the data set will be updated continuously. Each COVID-19 KG was evaluated, and the average precision was found to be above 93%. We have developed search and browse interfaces and a SPARQL endpoint to improve user access. Possible intelligent applications based on OpenKG-COVID19 for further development are also described. Conclusions A KG is useful for intelligent question-answering, semantic searches, recommendation systems, visualization analysis, and decision-making support. Research related to COVID-19, biomedicine, and many other communities can benefit from OpenKG-COVID19. Furthermore, the 10 KGs will be continuously updated to ensure that the public will have access to sufficient and up-to-date knowledge.
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Affiliation(s)
- Haofen Wang
- College of Design and Innovation, Tongji University, No. 281 Fuxin Road, Yangpu District, Shanghai, CN
| | - Huifang Du
- College of Design and Innovation, Tongji University, No. 281 Fuxin Road, Yangpu District, Shanghai, CN
| | - Guilin Qi
- School of Computer Science and Engineering, Southeast University, Nanjing, CN
| | - Huajun Chen
- College of Computer Science and Technology, Zhejiang University, Hangzhou, CN
| | - Wei Hu
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, CN
| | - Zhuo Chen
- College of Computer Science and Technology, Zhejiang University, Hangzhou, CN
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13
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Extracting and Measuring Uncertain Biomedical Knowledge from Scientific Statements. JOURNAL OF DATA AND INFORMATION SCIENCE 2022. [DOI: 10.2478/jdis-2022-0008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Abstract
Purpose
Given the information overload of scientific literature, there is an increasing need for computable biomedical knowledge buried in free text. This study aimed to develop a novel approach to extracting and measuring uncertain biomedical knowledge from scientific statements.
Design/methodology/approach
Taking cardiovascular research publications in China as a sample, we extracted subject–predicate–object triples (SPO triples) as knowledge units and unknown/hedging/conflicting uncertainties as the knowledge context. We introduced information entropy (IE) as potential metric to quantify the uncertainty of epistemic status of scientific knowledge represented at subject-object pairs (SO pairs) levels.
Findings
The results indicated an extraordinary growth of cardiovascular publications in China while only a modest growth of the novel SPO triples. After evaluating the uncertainty of biomedical knowledge with IE, we identified the Top 10 SO pairs with highest IE, which implied the epistemic status pluralism. Visual presentation of the SO pairs overlaid with uncertainty provided a comprehensive overview of clusters of biomedical knowledge and contending topics in cardiovascular research.
Research limitations
The current methods didn’t distinguish the specificity and probabilities of uncertainty cue words. The number of sentences surrounding a given triple may also influence the value of IE.
Practical implications
Our approach identified major uncertain knowledge areas such as diagnostic biomarkers, genetic polymorphism and co-existing risk factors related to cardiovascular diseases in China. These areas are suggested to be prioritized; new hypotheses need to be verified, while disputes, conflicts, and contradictions need to be settled.
Originality/value
We provided a novel approach by combining natural language processing and computational linguistics with informetric methods to extract and measure uncertain knowledge from scientific statements.
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14
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Alshahrani M, Almansour A, Alkhaldi A, Thafar MA, Uludag M, Essack M, Hoehndorf R. Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications. PeerJ 2022; 10:e13061. [PMID: 35402106 PMCID: PMC8988936 DOI: 10.7717/peerj.13061] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 02/13/2022] [Indexed: 01/11/2023] Open
Abstract
Biomedical knowledge is represented in structured databases and published in biomedical literature, and different computational approaches have been developed to exploit each type of information in predictive models. However, the information in structured databases and literature is often complementary. We developed a machine learning method that combines information from literature and databases to predict drug targets and indications. To effectively utilize information in published literature, we integrate knowledge graphs and published literature using named entity recognition and normalization before applying a machine learning model that utilizes the combination of graph and literature. We then use supervised machine learning to show the effects of combining features from biomedical knowledge and published literature on the prediction of drug targets and drug indications. We demonstrate that our approach using datasets for drug-target interactions and drug indications is scalable to large graphs and can be used to improve the ranking of targets and indications by exploiting features from either structure or unstructured information alone.
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Affiliation(s)
- Mona Alshahrani
- National Center for Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh, Saudi Arabia
| | - Abdullah Almansour
- National Center for Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh, Saudi Arabia
| | - Asma Alkhaldi
- National Center for Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh, Saudi Arabia
| | - Maha A. Thafar
- College of Computers and Information Technology, Taif University, Taif, Saudi Arabia,Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Mahmut Uludag
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Robert Hoehndorf
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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15
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Gurbuz O, Alanis-Lobato G, Picart-Armada S, Sun M, Haslinger C, Lawless N, Fernandez-Albert F. Knowledge Graphs for Indication Expansion: An Explainable Target-Disease Prediction Method. Front Genet 2022; 13:814093. [PMID: 35360842 PMCID: PMC8963915 DOI: 10.3389/fgene.2022.814093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 01/28/2022] [Indexed: 11/19/2022] Open
Abstract
Indication expansion aims to find new indications for existing targets in order to accelerate the process of launching a new drug for a disease on the market. The rapid increase in data types and data sources for computational drug discovery has fostered the use of semantic knowledge graphs (KGs) for indication expansion through target centric approaches, or in other words, target repositioning. Previously, we developed a novel method to construct a KG for indication expansion studies, with the aim of finding and justifying alternative indications for a target gene of interest. In contrast to other KGs, ours combines human-curated full-text literature and gene expression data from biomedical databases to encode relationships between genes, diseases, and tissues. Here, we assessed the suitability of our KG for explainable target-disease link prediction using a glass-box approach. To evaluate the predictive power of our KG, we applied shortest path with tissue information- and embedding-based prediction methods to a graph constructed with information published before or during 2010. We also obtained random baselines by applying the shortest path predictive methods to KGs with randomly shuffled node labels. Then, we evaluated the accuracy of the top predictions using gene-disease links reported after 2010. In addition, we investigated the contribution of the KG’s tissue expression entity to the prediction performance. Our experiments showed that shortest path-based methods significantly outperform the random baselines and embedding-based methods outperform the shortest path predictions. Importantly, removing the tissue expression entity from the KG severely impacts the quality of the predictions, especially those produced by the embedding approaches. Finally, since the interpretability of the predictions is crucial in indication expansion, we highlight the advantages of our glass-box model through the examination of example candidate target-disease predictions.
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Affiliation(s)
- Ozge Gurbuz
- Discovery Research Coordination Germany, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
- *Correspondence: Ozge Gurbuz, ; Francesc Fernandez-Albert,
| | - Gregorio Alanis-Lobato
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Sergio Picart-Armada
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Miao Sun
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Christian Haslinger
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Nathan Lawless
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Francesc Fernandez-Albert
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
- *Correspondence: Ozge Gurbuz, ; Francesc Fernandez-Albert,
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16
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Willighagen E, Kutmon M, Martens M, Slenter D. BridgeDb and Wikidata: a powerful combination generating interoperable open research (BridgeDb). RESEARCH IDEAS AND OUTCOMES 2022. [DOI: 10.3897/rio.8.e83031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Like humans have a unique social security number and different phone numbers from various providers, so do proteins and metabolites have a unique structure but different identifiers from various databases. BridgeDb is an interoperability platform that allows combining these databases, by matching database-specific identifiers. These matches are called identifier mappings, and they are indispensable when combining experimental (omics) data with knowledge in reference databases. BridgeDb takes care of this interoperability between gene, protein, metabolite, and other databases, thus enabling seamless integration of many knowledge bases and wet-lab results. Since databases get updated continuously, so should the Open Science BridgeDb project.
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17
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Tyagi R, Singh A, Chaudhary KK, Yadav MK. Pharmacophore modeling and its applications. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00009-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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18
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Leipzig J, Nüst D, Hoyt CT, Ram K, Greenberg J. The role of metadata in reproducible computational research. PATTERNS (NEW YORK, N.Y.) 2021; 2:100322. [PMID: 34553169 PMCID: PMC8441584 DOI: 10.1016/j.patter.2021.100322] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, improving the reproducibility of scientific studies can accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting reproducibility. Metadata provide context and provenance to raw data and methods and are essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described how metadata enable reproducible computational research. This review employs a functional content analysis to identify metadata standards that support reproducibility across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our review provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.
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Affiliation(s)
- Jeremy Leipzig
- Metadata Research Center, College of Computing and Informatics, Drexel University, Philadelphia, PA, USA
| | - Daniel Nüst
- Institute for Geoinformatics, University of Münster, Münster, Germany
| | | | - Karthik Ram
- Berkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USA
| | - Jane Greenberg
- Metadata Research Center, College of Computing and Informatics, Drexel University, Philadelphia, PA, USA
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19
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Delmas M, Filangi O, Paulhe N, Vinson F, Duperier C, Garrier W, Saunier PE, Pitarch Y, Jourdan F, Giacomoni F, Frainay C. FORUM: Building a Knowledge Graph from public databases and scientific literature to extract associations between chemicals and diseases. Bioinformatics 2021; 37:3896-3904. [PMID: 34478489 PMCID: PMC8570811 DOI: 10.1093/bioinformatics/btab627] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 08/16/2021] [Accepted: 09/01/2021] [Indexed: 11/22/2022] Open
Abstract
Motivation Metabolomics studies aim at reporting a metabolic signature (list of metabolites) related to a particular experimental condition. These signatures are instrumental in the identification of biomarkers or classification of individuals, however their biological and physiological interpretation remains a challenge. To support this task, we introduce FORUM: a Knowledge Graph (KG) providing a semantic representation of relations between chemicals and biomedical concepts, built from a federation of life science databases and scientific literature repositories. Results The use of a Semantic Web framework on biological data allows us to apply ontological-based reasoning to infer new relations between entities. We show that these new relations provide different levels of abstraction and could open the path to new hypotheses. We estimate the statistical relevance of each extracted relation, explicit or inferred, using an enrichment analysis, and instantiate them as new knowledge in the KG to support results interpretation/further inquiries. Availability and implementation A web interface to browse and download the extracted relations, as well as a SPARQL endpoint to directly probe the whole FORUM KG, are available at https://forum-webapp.semantic-metabolomics.fr. The code needed to reproduce the triplestore is available at https://github.com/eMetaboHUB/Forum-DiseasesChem. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- M Delmas
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, 31300, France
| | - O Filangi
- IGEPP, INRAE, Institut Agro, Université de Rennes, Domaine de la Motte, Le Rheu, 35653, France
| | - N Paulhe
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, F-63000, France
| | - F Vinson
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, 31300, France
| | - C Duperier
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, F-63000, France
| | - W Garrier
- ISIMA, Campus des Cézeaux, Aubière, 63177, France
| | - P-E Saunier
- ISIMA, Campus des Cézeaux, Aubière, 63177, France
| | - Y Pitarch
- IRIT, Université de Toulouse, Cours Rose Dieng-Kuntz, Toulouse, 31400, France
| | - F Jourdan
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, 31300, France
| | - F Giacomoni
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, F-63000, France
| | - C Frainay
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, 31300, France
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20
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Bertoni M, Duran-Frigola M, Badia-I-Mompel P, Pauls E, Orozco-Ruiz M, Guitart-Pla O, Alcalde V, Diaz VM, Berenguer-Llergo A, Brun-Heath I, Villegas N, de Herreros AG, Aloy P. Bioactivity descriptors for uncharacterized chemical compounds. Nat Commun 2021; 12:3932. [PMID: 34168145 PMCID: PMC8225676 DOI: 10.1038/s41467-021-24150-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 05/27/2021] [Indexed: 01/20/2023] Open
Abstract
Chemical descriptors encode the physicochemical and structural properties of small molecules, and they are at the core of chemoinformatics. The broad release of bioactivity data has prompted enriched representations of compounds, reaching beyond chemical structures and capturing their known biological properties. Unfortunately, bioactivity descriptors are not available for most small molecules, which limits their applicability to a few thousand well characterized compounds. Here we present a collection of deep neural networks able to infer bioactivity signatures for any compound of interest, even when little or no experimental information is available for them. Our signaturizers relate to bioactivities of 25 different types (including target profiles, cellular response and clinical outcomes) and can be used as drop-in replacements for chemical descriptors in day-to-day chemoinformatics tasks. Indeed, we illustrate how inferred bioactivity signatures are useful to navigate the chemical space in a biologically relevant manner, unveiling higher-order organization in natural product collections, and to enrich mostly uncharacterized chemical libraries for activity against the drug-orphan target Snail1. Moreover, we implement a battery of signature-activity relationship (SigAR) models and show a substantial improvement in performance, with respect to chemistry-based classifiers, across a series of biophysics and physiology activity prediction benchmarks.
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Affiliation(s)
- Martino Bertoni
- Joint IRB-BSC-CRG Programme in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Miquel Duran-Frigola
- Joint IRB-BSC-CRG Programme in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
- Ersilia Open Source Initiative, Cambridge, UK.
| | - Pau Badia-I-Mompel
- Joint IRB-BSC-CRG Programme in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Eduardo Pauls
- Joint IRB-BSC-CRG Programme in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Modesto Orozco-Ruiz
- Joint IRB-BSC-CRG Programme in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Oriol Guitart-Pla
- Joint IRB-BSC-CRG Programme in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Víctor Alcalde
- Joint IRB-BSC-CRG Programme in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Víctor M Diaz
- Programa de Recerca en Càncer, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM) and Departament de Ciències de la Salut, Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain
- Faculty of Medicine and Health Sciences, International University of Catalonia, Barcelona, Catalonia, Spain
| | - Antoni Berenguer-Llergo
- Joint IRB-BSC-CRG Programme in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Isabelle Brun-Heath
- Joint IRB-BSC-CRG Programme in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Núria Villegas
- Joint IRB-BSC-CRG Programme in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Antonio García de Herreros
- Programa de Recerca en Càncer, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM) and Departament de Ciències de la Salut, Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain
| | - Patrick Aloy
- Joint IRB-BSC-CRG Programme in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
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21
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Galgonek J, Vondrášek J. IDSM ChemWebRDF: SPARQLing small-molecule datasets. J Cheminform 2021; 13:38. [PMID: 33980298 PMCID: PMC8117646 DOI: 10.1186/s13321-021-00515-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 04/23/2021] [Indexed: 11/12/2022] Open
Abstract
The Resource Description Framework (RDF), together with well-defined ontologies, significantly increases data interoperability and usability. The SPARQL query language was introduced to retrieve requested RDF data and to explore links between them. Among other useful features, SPARQL supports federated queries that combine multiple independent data source endpoints. This allows users to obtain insights that are not possible using only a single data source. Owing to all of these useful features, many biological and chemical databases present their data in RDF, and support SPARQL querying. In our project, we primary focused on PubChem, ChEMBL and ChEBI small-molecule datasets. These datasets are already being exported to RDF by their creators. However, none of them has an official and currently supported SPARQL endpoint. This omission makes it difficult to construct complex or federated queries that could access all of the datasets, thus underutilising the main advantage of the availability of RDF data. Our goal is to address this gap by integrating the datasets into one database called the Integrated Database of Small Molecules (IDSM) that will be accessible through a SPARQL endpoint. Beyond that, we will also focus on increasing mutual interoperability of the datasets. To realise the endpoint, we decided to implement an in-house developed SPARQL engine based on the PostgreSQL relational database for data storage. In our approach, data are stored in the traditional relational form, and the SPARQL engine translates incoming SPARQL queries into equivalent SQL queries. An important feature of the engine is that it optimises the resulting SQL queries. Together with optimisations performed by PostgreSQL, this allows efficient evaluations of SPARQL queries. The endpoint provides not only querying in the dataset, but also the compound substructure and similarity search supported by our Sachem project. Although the endpoint is accessible from an internet browser, it is mainly intended to be used for programmatic access by other services, for example as a part of federated queries. For regular users, we offer a rich web application called ChemWebRDF using the endpoint. The application is publicly available at https://idsm.elixir-czech.cz/chemweb/.
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Affiliation(s)
- Jakub Galgonek
- Institute of Organic Chemistry and Biochemistry of the CAS, Flemingovo náměstí 2, 166 10, Prague 6, Czech Republic.
| | - Jiří Vondrášek
- Institute of Organic Chemistry and Biochemistry of the CAS, Flemingovo náměstí 2, 166 10, Prague 6, Czech Republic
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22
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Davis AM, Engkvist O, Fairclough RJ, Feierberg I, Freeman A, Iyer P. Public-Private Partnerships: Compound and Data Sharing in Drug Discovery and Development. SLAS DISCOVERY 2021; 26:604-619. [PMID: 33586501 DOI: 10.1177/2472555220982268] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Collaborative efforts between public and private entities such as academic institutions, governments, and pharmaceutical companies form an integral part of scientific research, and notable instances of such initiatives have been created within the life science community. Several examples of alliances exist with the broad goal of collaborating toward scientific advancement and improved public welfare. Such collaborations can be essential in catalyzing breaking areas of science within high-risk or global public health strategies that may have otherwise not progressed. A common term used to describe these alliances is public-private partnership (PPP). This review discusses different aspects of such partnerships in drug discovery/development and provides example applications as well as successful case studies. Specific areas that are covered include PPPs for sharing compounds at various phases of the drug discovery process-from compound collections for hit identification to sharing clinical candidates. Instances of PPPs to support better data integration and build better machine learning models are also discussed. The review also provides examples of PPPs that address the gap in knowledge or resources among involved parties and advance drug discovery, especially in disease areas with unfulfilled and/or social needs, like neurological disorders, cancer, and neglected and rare diseases.
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Affiliation(s)
- Andrew M Davis
- Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Rebecca J Fairclough
- Emerging Innovations Unit, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Isabella Feierberg
- Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Boston, USA
| | - Adrian Freeman
- Emerging Innovations Unit, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Preeti Iyer
- Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
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23
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A public-private partnership to enrich the development of in silico predictive models for pharmacokinetic and cardiotoxic properties. Drug Discov Today 2021; 26:1275-1283. [PMID: 33516857 DOI: 10.1016/j.drudis.2021.01.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 08/21/2020] [Accepted: 01/22/2021] [Indexed: 12/12/2022]
Abstract
A novel framework for a public-private (PP) partnership was established by a national initiative of the Development of a Drug Discovery Informatics System, supported by the Japan Agency for Medical Research and Development (AMED). This informatics PP partnership consortium comprised private and public sectors. A database of pharmacokinetic (PK) and cardiotoxic properties was developed, with considerable expansion after integrating proprietary data from private-sector members. This database led to robust in silico prediction models with higher performance than those from the original database. This partnership is a unique example worldwide and could substantially strengthen drug discovery capabilities in both sectors.
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24
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Kamdar MR, Musen MA. An empirical meta-analysis of the life sciences linked open data on the web. Sci Data 2021; 8:24. [PMID: 33479214 PMCID: PMC7819992 DOI: 10.1038/s41597-021-00797-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 12/04/2020] [Indexed: 01/29/2023] Open
Abstract
While the biomedical community has published several "open data" sources in the last decade, most researchers still endure severe logistical and technical challenges to discover, query, and integrate heterogeneous data and knowledge from multiple sources. To tackle these challenges, the community has experimented with Semantic Web and linked data technologies to create the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we extract schemas from more than 80 biomedical linked open data sources into an LSLOD schema graph and conduct an empirical meta-analysis to evaluate the extent of semantic heterogeneity across the LSLOD cloud. We observe that several LSLOD sources exist as stand-alone data sources that are not inter-linked with other sources, use unpublished schemas with minimal reuse or mappings, and have elements that are not useful for data integration from a biomedical perspective. We envision that the LSLOD schema graph and the findings from this research will aid researchers who wish to query and integrate data and knowledge from multiple biomedical sources simultaneously on the Web.
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Affiliation(s)
- Maulik R Kamdar
- Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
- Elsevier Health Markets, Philadelphia, PA, USA.
| | - Mark A Musen
- Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA
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25
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Yeung AWK, Atanasov AG, Sheridan H, Klager E, Eibensteiner F, Völkl-Kernsock S, Kletecka-Pulker M, Willschke H, Schaden E. Open Innovation in Medical and Pharmaceutical Research: A Literature Landscape Analysis. Front Pharmacol 2021; 11:587526. [PMID: 33519448 PMCID: PMC7840485 DOI: 10.3389/fphar.2020.587526] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 11/16/2020] [Indexed: 12/12/2022] Open
Abstract
Open innovation in medical and pharmaceutical research has grown steadily over the last decade. However, the performance of the published literature in terms of the scientific impact and gaining social media attention remains largely unexplored. The scientific literature of open innovation was examined by means of bibliometric analyses to identify the most prolific authors, organizations, countries, journals, research areas, and recurring terms. By accessing the Web of Science Core Collection and Altmetric electronic databases, citation-related and Altmetric data were evaluated. Public-private partnerships and a selection of newly introduced potential novel drugs in the analyzed publications were identified. North America and Europe were the major literature contributors. Research outputs were mainly published in journals focused on business and economics, pharmacology and pharmacy, and engineering. Many pharmaceutical and biotechnological companies contributed to the analyzed publications, with higher mean citation counts and social media attention (Altmetric score) than nonindustry articles. Public-private partnerships fostered financial support, sharing of expertise and intellectual property, and research collaborations. In summary, open innovation might serve as a powerful strategy to both benefit the involved industry entities and accelerate the development of solutions and products for the betterment of human health.
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Affiliation(s)
- Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China.,Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Atanas G Atanasov
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.,Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Magdalenka, Poland.,Institute of Neurobiology, Bulgarian Academy of Sciences, Sofia, Bulgaria.,Department of Pharmacognosy, University of Vienna, Vienna, Austria
| | - Helen Sheridan
- NatPro Centre. School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland
| | - Elisabeth Klager
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Fabian Eibensteiner
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.,Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Sabine Völkl-Kernsock
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Maria Kletecka-Pulker
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Harald Willschke
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.,Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Medical University Vienna, Vienna, Austria
| | - Eva Schaden
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.,Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Medical University Vienna, Vienna, Austria
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26
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Kanza S, Graham Frey J. Semantic Technologies in Drug Discovery. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11520-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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27
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Sarwar DM, Nickerson DP. CellML Model Discovery with the Physiome Model Repository. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11681-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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28
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Bento AP, Hersey A, Félix E, Landrum G, Gaulton A, Atkinson F, Bellis LJ, De Veij M, Leach AR. An open source chemical structure curation pipeline using RDKit. J Cheminform 2020; 12:51. [PMID: 33431044 PMCID: PMC7458899 DOI: 10.1186/s13321-020-00456-1] [Citation(s) in RCA: 144] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 08/24/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The ChEMBL database is one of a number of public databases that contain bioactivity data on small molecule compounds curated from diverse sources. Incoming compounds are typically not standardised according to consistent rules. In order to maintain the quality of the final database and to easily compare and integrate data on the same compound from different sources it is necessary for the chemical structures in the database to be appropriately standardised. RESULTS A chemical curation pipeline has been developed using the open source toolkit RDKit. It comprises three components: a Checker to test the validity of chemical structures and flag any serious errors; a Standardizer which formats compounds according to defined rules and conventions and a GetParent component that removes any salts and solvents from the compound to create its parent. This pipeline has been applied to the latest version of the ChEMBL database as well as uncurated datasets from other sources to test the robustness of the process and to identify common issues in database molecular structures. CONCLUSION All the components of the structure pipeline have been made freely available for other researchers to use and adapt for their own use. The code is available in a GitHub repository and it can also be accessed via the ChEMBL Beaker webservices. It has been used successfully to standardise the nearly 2 million compounds in the ChEMBL database and the compound validity checker has been used to identify compounds with the most serious issues so that they can be prioritised for manual curation.
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Affiliation(s)
- A Patrícia Bento
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridgeshire, UK
| | - Anne Hersey
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridgeshire, UK
| | - Eloy Félix
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridgeshire, UK
| | | | - Anna Gaulton
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridgeshire, UK
| | - Francis Atkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridgeshire, UK
- The Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK
| | - Louisa J Bellis
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridgeshire, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Marleen De Veij
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridgeshire, UK
| | - Andrew R Leach
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridgeshire, UK.
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29
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Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker. Nat Biotechnol 2020; 38:1087-1096. [PMID: 32440005 DOI: 10.1038/s41587-020-0502-7] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 03/27/2020] [Indexed: 02/07/2023]
Abstract
Small molecules are usually compared by their chemical structure, but there is no unified analytic framework for representing and comparing their biological activity. We present the Chemical Checker (CC), which provides processed, harmonized and integrated bioactivity data on ~800,000 small molecules. The CC divides data into five levels of increasing complexity, from the chemical properties of compounds to their clinical outcomes. In between, it includes targets, off-targets, networks and cell-level information, such as omics data, growth inhibition and morphology. Bioactivity data are expressed in a vector format, extending the concept of chemical similarity to similarity between bioactivity signatures. We show how CC signatures can aid drug discovery tasks, including target identification and library characterization. We also demonstrate the discovery of compounds that reverse and mimic biological signatures of disease models and genetic perturbations in cases that could not be addressed using chemical information alone. Overall, the CC signatures facilitate the conversion of bioactivity data to a format that is readily amenable to machine learning methods.
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30
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Radovnikovic A, Geiss O, Kephalopoulos S, Reina V, Barrero J, Dalla Costa S, Verile M, Mantica E. Assessment of the opportunities for increasing the availability of EU data on consumer product-related injuries. Inj Prev 2020; 27:172-183. [PMID: 32371468 PMCID: PMC8005795 DOI: 10.1136/injuryprev-2020-043677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 03/12/2020] [Accepted: 03/14/2020] [Indexed: 11/03/2022]
Abstract
The availability of data on consumer products-related accidents and injuries is of interest to a wide range of stakeholders, such as consumer product safety and injury prevention policymakers, market surveillance authorities, consumer organisations, standardisation organisations, manufacturers and the public. While the amount of information available and potentially of use for product safety is considerable in some European Union (EU) countries, its usability at EU level is difficult due to high fragmentation of the data sources, the diversity of data collection methods and increasing data protection concerns. To satisfy the policy need for more timely information on consumer product-related incidents, apart from injury data that have been historically collected by the public health sector, a number of 'alternative' data sources were assessed as potential sources of interest. This study explores the opportunities for enhancing the availability of data of consumer product-related injuries, arising from selected existing and 'alternative' data sources, widely present in Europe, such as firefighters' and poison centres' records, mortality statistics, consumer complaints, insurance companies' registers, manufacturers' incident registers and online news sources. These data sources, coupled with the use of IT technologies, such as interlinking by remote data access, could fill in the existing information gap. Strengths and weaknesses of selected data sources, with a view to support a common data platform, are evaluated and presented. Conducting the study relied on the literature review, extensive use of the surveys, interviews, workshops with experts and online data-mining pilot study.
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Affiliation(s)
| | - Otmar Geiss
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | | | - Vittorio Reina
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Josefa Barrero
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | | | - Marco Verile
- European Commission, Joint Research Centre (JRC), Ispra, Italy
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31
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Vos RA, Katayama T, Mishima H, Kawano S, Kawashima S, Kim JD, Moriya Y, Tokimatsu T, Yamaguchi A, Yamamoto Y, Wu H, Amstutz P, Antezana E, Aoki NP, Arakawa K, Bolleman JT, Bolton E, Bonnal RJP, Bono H, Burger K, Chiba H, Cohen KB, Deutsch EW, Fernández-Breis JT, Fu G, Fujisawa T, Fukushima A, García A, Goto N, Groza T, Hercus C, Hoehndorf R, Itaya K, Juty N, Kawashima T, Kim JH, Kinjo AR, Kotera M, Kozaki K, Kumagai S, Kushida T, Lütteke T, Matsubara M, Miyamoto J, Mohsen A, Mori H, Naito Y, Nakazato T, Nguyen-Xuan J, Nishida K, Nishida N, Nishide H, Ogishima S, Ohta T, Okuda S, Paten B, Perret JL, Prathipati P, Prins P, Queralt-Rosinach N, Shinmachi D, Suzuki S, Tabata T, Takatsuki T, Taylor K, Thompson M, Uchiyama I, Vieira B, Wei CH, Wilkinson M, Yamada I, Yamanaka R, Yoshitake K, Yoshizawa AC, Dumontier M, Kosaki K, Takagi T. BioHackathon 2015: Semantics of data for life sciences and reproducible research. F1000Res 2020; 9:136. [PMID: 32308977 PMCID: PMC7141167 DOI: 10.12688/f1000research.18236.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/05/2020] [Indexed: 01/08/2023] Open
Abstract
We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.
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Affiliation(s)
- Rutger A. Vos
- Institute of Biology Leiden, Leiden University, Leiden, The Netherlands
- Naturalis Biodiversity Center, Leiden, The Netherlands
| | | | - Hiroyuki Mishima
- Department of Human Genetics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Shin Kawano
- Database Center for Life Science, Tokyo, Japan
| | | | | | - Yuki Moriya
- Database Center for Life Science, Tokyo, Japan
| | | | | | | | - Hongyan Wu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | | | - Erick Antezana
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Nobuyuki P. Aoki
- Faculty of Science and Engineering, SOKA University, Tokyo, Japan
| | - Kazuharu Arakawa
- Institute for Advanced Biosciences, Keio University, Tokyo, Japan
| | - Jerven T. Bolleman
- SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, Lausanne, Switzerland
| | - Evan Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | - Raoul J. P. Bonnal
- Istituto Nazionale Genetica Molecolare, Romeo ed Enrica Invernizzi, Milan, Italy
| | | | - Kees Burger
- Dutch Techcentre for Life Sciences, Utrecht, The Netherlands
| | - Hirokazu Chiba
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Kevin B. Cohen
- Computational Bioscience Program, University of Colorado School of Medicine, Denver, USA
- Université Paris-Saclay, LIMSI, CNRS, Paris, France
| | | | | | - Gang Fu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | | | | | | | - Naohisa Goto
- Research Institute for Microbial Diseases, Osaka University, Osaka, Japan
| | - Tudor Groza
- St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Darlinghurst, Australia
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Darlinghurst, Australia
| | - Colin Hercus
- Novocraft Technologies Sdn. Bhd., Selangor, Malaysia
| | - Robert Hoehndorf
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Kotone Itaya
- Institute for Advanced Biosciences, Keio University, Tokyo, Japan
| | - Nick Juty
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | | | - Jee-Hyub Kim
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Akira R. Kinjo
- Institute for Protein Research, Osaka University, Osaka, Japan
| | - Masaaki Kotera
- School of Life Science and Technology, Tokyo Institute of Technology, Tokyo, Japan
| | - Kouji Kozaki
- The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan
| | | | - Tatsuya Kushida
- National Bioscience Database Center, Japan Science and Technology Agency, Tokyo, Japan
| | - Thomas Lütteke
- Institute of Veterinary Physiology and Biochemistry, Justus-Liebig University Giessen, Giessen, Germany
- Gesellschaft für innovative Personalwirtschaftssysteme mbH (GIP GmbH), Offenbach, Germany
| | | | | | - Attayeb Mohsen
- National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
| | - Hiroshi Mori
- Center for Information Biology, National Institute of Genetics, Mishima, Japan
| | - Yuki Naito
- Database Center for Life Science, Tokyo, Japan
| | | | | | | | - Naoki Nishida
- Department of Systems Science, Osaka University, Osaka, Japan
| | - Hiroyo Nishide
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Soichi Ogishima
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Tazro Ohta
- Database Center for Life Science, Tokyo, Japan
| | - Shujiro Okuda
- Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, USA
| | | | - Philip Prathipati
- National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
| | - Pjotr Prins
- University Medical Center Utrecht, Utrecht, The Netherlands
- University of Tennessee Health Science Center, Memphis, USA
| | - Núria Queralt-Rosinach
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Shinya Suzuki
- School of Life Science and Technology, Tokyo Institute of Technology, Tokyo, Japan
| | - Tsuyosi Tabata
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
| | | | - Kieron Taylor
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Mark Thompson
- Leiden University Medical Center, Leiden, The Netherlands
| | - Ikuo Uchiyama
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Bruno Vieira
- WurmLab, School of Biological & Chemical Sciences, Queen Mary University of London, London, UK
| | - Chih-Hsuan Wei
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | - Mark Wilkinson
- Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Madrid, Spain
| | | | | | - Kazutoshi Yoshitake
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | | | - Michel Dumontier
- Institute of Data Science, Maastricht University, Maastricht, The Netherlands
| | - Kenjiro Kosaki
- Center for Medical Genetics, Keio University School of Medicine, Tokyo, Japan
| | - Toshihisa Takagi
- National Bioscience Database Center, Japan Science and Technology Agency, Tokyo, Japan
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
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32
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Juty N, Wimalaratne SM, Soiland-Reyes S, Kunze J, Goble CA, Clark T. Unique, Persistent, Resolvable: Identifiers as the Foundation of FAIR. DATA INTELLIGENCE 2020. [DOI: 10.1162/dint_a_00025] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
The FAIR principles describe characteristics intended to support access to and reuse of digital artifacts in the scientific research ecosystem. Persistent, globally unique identifiers, resolvable on the Web, and associated with a set of additional descriptive metadata, are foundational to FAIR data. Here we describe some basic principles and exemplars for their design, use and orchestration with other system elements to achieve FAIRness for digital research objects.
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Affiliation(s)
- Nick Juty
- Department of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
| | | | - Stian Soiland-Reyes
- Department of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
| | - John Kunze
- California Digital Library, Oakland, California 94612-2901, USA
| | - Carole A. Goble
- Department of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
| | - Tim Clark
- Data Science Institute, University of Virginia, Charlottesville, VA 22903-1738, USA
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33
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van Vlijmen H, Mons A, Waalkens A, Franke W, Baak A, Ruiter G, Kirkpatrick C, da Silva Santos LOB, Meerman B, Jellema R, Arts D, Kersloot M, Knijnenburg S, Lusher S, Verbeeck R, Neefs JM. The Need of Industry to Go FAIR. DATA INTELLIGENCE 2020. [DOI: 10.1162/dint_a_00050] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The industry sector is a very large producer and consumer of data, and many companies traditionally focused on production or manufacturing are now relying on the analysis of large amounts of data to develop new products and services. As many of the data sources needed are distributed and outside the company, FAIR data will have a major impact, both by reducing the existing internal data silos and by enabling the efficient integration with external (public and commercial) data. Many companies are still in the early phases of internal data “FAIRification”, providing opportunities for SMEs and academics to apply and develop their expertise on FAIR data in collaborations and public-private partnerships. For a global Internet of FAIR Data & Services to thrive, also involving industry, professional tools and services are essential. FAIR metrics and certifications on individuals, data, organizations, and software, must ensure that data producers and consumers have independent quality metrics on their data. In this opinion article we reflect on some industry specific challenges of FAIR implementation to be dealt with when choices are made regarding “Industry GOing FAIR”.
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Affiliation(s)
| | | | - Arne Waalkens
- Accenture, Gustav Mahlerplein 90, 1082 MA Amsterdam, The Netherlands
| | - Wouter Franke
- Zorg Instituut Nederland, Willem Dudokhof 1, 1112 ZA Diemen, The Netherlands
| | - Arie Baak
- Euretos, Yalelaan 1, 3584 CL Utrecht, The Netherlands
| | - Gerbrand Ruiter
- Mobiquity, Tommaso Albinonistraat 9, 1083 HM Amsterdam, The Netherlands
| | | | | | - Bert Meerman
- GO FAIR Foundation, Rijnsburgerweg 10, 2333 AA Leiden, The Netherlands
| | - Renger Jellema
- DSM Biotechnology Center, Alexander Fleminglaan 1, 2613 AX Delft, The Netherlands
| | - Derk Arts
- Castor, Paasheuvelweg 25, Vleugel 5D, 1105 BP Amsterdam, The Netherlands
| | - Martijn Kersloot
- Castor, Paasheuvelweg 25, Vleugel 5D, 1105 BP Amsterdam, The Netherlands
| | | | - Scott Lusher
- Janssen Pharmaceuticals, Antwerpseweg 15, 2340 Beerse, Belgium
| | - Rudi Verbeeck
- Janssen Pharmaceuticals, Antwerpseweg 15, 2340 Beerse, Belgium
| | - Jean-Marc Neefs
- Janssen Pharmaceuticals, Antwerpseweg 15, 2340 Beerse, Belgium
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34
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Abstract
The transport of materials across membranes is a vital process for all aspects of cellular function, including growth, metabolism, and communication. Protein transporters are the molecular gates that control this movement and serve as key points of regulation for these processes, thus representing an attractive class of therapeutic targets. With more than 400 members, the solute carrier (SLC) membrane transport proteins are the largest family of transporters, yet, they are pharmacologically underexploited relative to other protein families and many of the available chemical tools possess suboptimal selectivity and efficacy. Fortuitously, there is increased interest in elucidating the physiological roles of SLCs as well as growing recognition of their therapeutic potential. This Perspective provides an overview of the SLC superfamily, including their biochemical and functional features, as well as their roles in various human diseases. In particular, we explore efforts and associated challenges toward drugging SLCs, as well as highlight opportunities for future drug discovery.
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Affiliation(s)
- Wesley Wei Wang
- Department of Chemistry, The Scripps Research Institute, Jupiter, Florida 33458, United States
| | - Leandro Gallo
- Department of Chemistry, The Scripps Research Institute, Jupiter, Florida 33458, United States
| | - Appaso Jadhav
- Department of Chemistry, The Scripps Research Institute, Jupiter, Florida 33458, United States
| | - Richard Hawkins
- Department of Chemistry, The Scripps Research Institute, Jupiter, Florida 33458, United States
| | - Christopher G Parker
- Department of Chemistry, The Scripps Research Institute, Jupiter, Florida 33458, United States.,Department of Chemistry, The Scripps Research Institute, La Jolla, California 92037, United States
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Sima AC, Dessimoz C, Stockinger K, Zahn-Zabal M, Mendes de Farias T. A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL. F1000Res 2019; 8:1822. [PMID: 32612807 PMCID: PMC7324951 DOI: 10.12688/f1000research.21027.2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/09/2020] [Indexed: 11/20/2022] Open
Abstract
The increasing use of Semantic Web technologies in the life sciences, in particular the use of the Resource Description Framework (RDF) and the RDF query language SPARQL, opens the path for novel integrative analyses, combining information from multiple data sources. However, analyzing evolutionary data in RDF is not trivial, due to the steep learning curve required to understand both the data models adopted by different RDF data sources, as well as the equivalent SPARQL constructs required to benefit from this data - in particular, recursive property paths. In this article, we provide a hands-on introduction to querying evolutionary data across several data sources that publish orthology information in RDF, namely: The Orthologous MAtrix (OMA), the European Bioinformatics Institute (EBI) RDF platform, the Database of Orthologous Groups (OrthoDB) and the Microbial Genome Database (MBGD). We present four protocols in increasing order of complexity. In these protocols, we demonstrate through SPARQL queries how to retrieve pairwise orthologs, homologous groups, and hierarchical orthologous groups. Finally, we show how orthology information in different data sources can be compared, through the use of federated SPARQL queries.
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Affiliation(s)
- Ana Claudia Sima
- ZHAW Zurich University of Applied Sciences, Winterthur, Zurich, Switzerland.,Department of Computational Biology, University of Lausanne, Lausanne, Vaud, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Vaud, Switzerland
| | - Christophe Dessimoz
- Department of Computational Biology, University of Lausanne, Lausanne, Vaud, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Vaud, Switzerland.,Center for Integrative Genomics, University of Lausanne, Lausanne, Vaud, Switzerland.,Department of Computer Science, University College London, London, UK.,Department of Genetics, Evolution, and Environment, University College London, London, UK
| | - Kurt Stockinger
- ZHAW Zurich University of Applied Sciences, Winterthur, Zurich, Switzerland
| | - Monique Zahn-Zabal
- Department of Computational Biology, University of Lausanne, Lausanne, Vaud, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Vaud, Switzerland
| | - Tarcisio Mendes de Farias
- Department of Computational Biology, University of Lausanne, Lausanne, Vaud, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Vaud, Switzerland.,Center for Integrative Genomics, University of Lausanne, Lausanne, Vaud, Switzerland.,Department of Ecology and Evolution, University of Lausanne, Lausanne, Vaud, Switzerland
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Sima AC, Dessimoz C, Stockinger K, Zahn-Zabal M, Mendes de Farias T. A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL. F1000Res 2019; 8:1822. [PMID: 32612807 PMCID: PMC7324951 DOI: 10.12688/f1000research.21027.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/22/2019] [Indexed: 08/01/2024] Open
Abstract
The increasing use of Semantic Web technologies in the life sciences, in particular the use of the Resource Description Framework (RDF) and the RDF query language SPARQL, opens the path for novel integrative analyses, combining information from multiple sources. However, analyzing evolutionary data in RDF is not trivial, due to the steep learning curve required to understand both the data models adopted by different RDF data sources, as well as the SPARQL query language. In this article, we provide a hands-on introduction to querying evolutionary data across multiple sources that publish orthology information in RDF, namely: The Orthologous MAtrix (OMA), the European Bioinformatics Institute (EBI) RDF platform, the Database of Orthologous Groups (OrthoDB) and the Microbial Genome Database (MBGD). We present four protocols in increasing order of complexity. In these protocols, we demonstrate through SPARQL queries how to retrieve pairwise orthologs, homologous groups, and hierarchical orthologous groups. Finally, we show how orthology information in different sources can be compared, through the use of federated SPARQL queries.
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Affiliation(s)
- Ana Claudia Sima
- ZHAW Zurich University of Applied Sciences, Winterthur, Zurich, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Vaud, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Vaud, Switzerland
| | - Christophe Dessimoz
- Department of Computational Biology, University of Lausanne, Lausanne, Vaud, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Vaud, Switzerland
- Center for Integrative Genomics, University of Lausanne, Lausanne, Vaud, Switzerland
- Department of Computer Science, University College London, London, UK
- Department of Genetics, Evolution, and Environment, University College London, London, UK
| | - Kurt Stockinger
- ZHAW Zurich University of Applied Sciences, Winterthur, Zurich, Switzerland
| | - Monique Zahn-Zabal
- Department of Computational Biology, University of Lausanne, Lausanne, Vaud, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Vaud, Switzerland
| | - Tarcisio Mendes de Farias
- Department of Computational Biology, University of Lausanne, Lausanne, Vaud, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Vaud, Switzerland
- Center for Integrative Genomics, University of Lausanne, Lausanne, Vaud, Switzerland
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Vaud, Switzerland
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37
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The use or generation of biomedical data and existing medicines to discover and establish new treatments for patients with rare diseases - recommendations of the IRDiRC Data Mining and Repurposing Task Force. Orphanet J Rare Dis 2019; 14:225. [PMID: 31615551 PMCID: PMC6794821 DOI: 10.1186/s13023-019-1193-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 09/04/2019] [Indexed: 12/12/2022] Open
Abstract
The number of available therapies for rare diseases remains low, as fewer than 6% of rare diseases have an approved treatment option. The International Rare Diseases Research Consortium (IRDiRC) set up the multi-stakeholder Data Mining and Repurposing (DMR) Task Force to examine the potential of applying biomedical data mining strategies to identify new opportunities to use existing pharmaceutical compounds in new ways and to accelerate the pace of drug development for rare disease patients. In reviewing past successes of data mining for drug repurposing, and planning for future biomedical research capacity, the DMR Task Force identified four strategic infrastructure investment areas to focus on in order to accelerate rare disease research productivity and drug development: (1) improving the capture and sharing of self-reported patient data, (2) better integration of existing research data, (3) increasing experimental testing capacity, and (4) sharing of rare disease research and development expertise. Additionally, the DMR Task Force also recommended a number of strategies to increase data mining and repurposing opportunities for rare diseases research as well as the development of individualized and precision medicine strategies.
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Kamdar MR, Fernández JD, Polleres A, Tudorache T, Musen MA. Enabling Web-scale data integration in biomedicine through Linked Open Data. NPJ Digit Med 2019; 2:90. [PMID: 31531395 PMCID: PMC6736878 DOI: 10.1038/s41746-019-0162-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 08/06/2019] [Indexed: 01/17/2023] Open
Abstract
The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems.
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Affiliation(s)
- Maulik R. Kamdar
- Center for Biomedical Informatics Research, Stanford University, Stanford, CA USA
| | - Javier D. Fernández
- Vienna University of Economics & Business, Vienna, Austria
- Complexity Science Hub Vienna, Vienna, Austria
| | - Axel Polleres
- Vienna University of Economics & Business, Vienna, Austria
- Complexity Science Hub Vienna, Vienna, Austria
| | - Tania Tudorache
- Center for Biomedical Informatics Research, Stanford University, Stanford, CA USA
| | - Mark A. Musen
- Center for Biomedical Informatics Research, Stanford University, Stanford, CA USA
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39
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Sarwar DM, Kalbasi R, Gennari JH, Carlson BE, Neal ML, Bono BD, Atalag K, Hunter PJ, Nickerson DP. Model annotation and discovery with the Physiome Model Repository. BMC Bioinformatics 2019; 20:457. [PMID: 31492098 PMCID: PMC6731580 DOI: 10.1186/s12859-019-2987-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2019] [Accepted: 07/09/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Mathematics and Phy sics-based simulation models have the potential to help interpret and encapsulate biological phenomena in a computable and reproducible form. Similarly, comprehensive descriptions of such models help to ensure that such models are accessible, discoverable, and reusable. To this end, researchers have developed tools and standards to encode mathematical models of biological systems enabling reproducibility and reuse, tools and guidelines to facilitate semantic description of mathematical models, and repositories in which to archive, share, and discover models. Scientists can leverage these resources to investigate specific questions and hypotheses in a more efficient manner. RESULTS We have comprehensively annotated a cohort of models with biological semantics. These annotated models are freely available in the Physiome Model Repository (PMR). To demonstrate the benefits of this approach, we have developed a web-based tool which enables users to discover models relevant to their work, with a particular focus on epithelial transport. Based on a semantic query, this tool will help users discover relevant models, suggesting similar or alternative models that the user may wish to explore or use. CONCLUSION The semantic annotation and the web tool we have developed is a new contribution enabling scientists to discover relevant models in the PMR as candidates for reuse in their own scientific endeavours. This approach demonstrates how semantic web technologies and methodologies can contribute to biomedical and clinical research. The source code and links to the web tool are available at https://github.com/dewancse/model-discovery-tool.
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Affiliation(s)
- Dewan M Sarwar
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Reza Kalbasi
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - John H Gennari
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Brian E Carlson
- Molecular & Integrative Physiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Maxwell L Neal
- Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, Washington, USA
| | - Bernard de Bono
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Koray Atalag
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Peter J Hunter
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - David P Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
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40
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Ontology mapping for semantically enabled applications. Drug Discov Today 2019; 24:2068-2075. [PMID: 31158512 DOI: 10.1016/j.drudis.2019.05.020] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Revised: 04/12/2019] [Accepted: 05/28/2019] [Indexed: 12/14/2022]
Abstract
In this review, we provide a summary of recent progress in ontology mapping (OM) at a crucial time when biomedical research is under a deluge of an increasing amount and variety of data. This is particularly important for realising the full potential of semantically enabled or enriched applications and for meaningful insights, such as drug discovery, using machine-learning technologies. We discuss challenges and solutions for better ontology mappings, as well as how to select ontologies before their application. In addition, we describe tools and algorithms for ontology mapping, including evaluation of tool capability and quality of mappings. Finally, we outline the requirements for an ontology mapping service (OMS) and the progress being made towards implementation of such sustainable services.
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41
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Navigating in vitro bioactivity data by investigating available resources using model compounds. Sci Data 2019; 6:45. [PMID: 31036807 PMCID: PMC6488657 DOI: 10.1038/s41597-019-0046-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 03/06/2019] [Indexed: 11/08/2022] Open
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Saqi M, Lysenko A, Guo YK, Tsunoda T, Auffray C. Navigating the disease landscape: knowledge representations for contextualizing molecular signatures. Brief Bioinform 2019; 20:609-623. [PMID: 29684165 PMCID: PMC6556902 DOI: 10.1093/bib/bby025] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 02/05/2018] [Indexed: 12/14/2022] Open
Abstract
Large amounts of data emerging from experiments in molecular medicine are leading to the identification of molecular signatures associated with disease subtypes. The contextualization of these patterns is important for obtaining mechanistic insight into the aberrant processes associated with a disease, and this typically involves the integration of multiple heterogeneous types of data. In this review, we discuss knowledge representations that can be useful to explore the biological context of molecular signatures, in particular three main approaches, namely, pathway mapping approaches, molecular network centric approaches and approaches that represent biological statements as knowledge graphs. We discuss the utility of each of these paradigms, illustrate how they can be leveraged with selected practical examples and identify ongoing challenges for this field of research.
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Affiliation(s)
- Mansoor Saqi
- Mansoor Saqi Data Science Institute, Imperial College London, UK
| | - Artem Lysenko
- Artem Lysenko Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yi-Ke Guo
- Yi-Ke Guo Data Science Institute, Imperial College London, UK
| | - Tatsuhiko Tsunoda
- Tatsuhiko Tsunoda Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan CREST, JST, Tokyo, Japan Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | - Charles Auffray
- Charles Auffray European Institute for Systems Biology and Medicine, Lyon, France
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43
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Kanza S, Frey JG. A new wave of innovation in Semantic web tools for drug discovery. Expert Opin Drug Discov 2019; 14:433-444. [DOI: 10.1080/17460441.2019.1586880] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Samantha Kanza
- Department of Chemistry, Highfield Campus, University of Southampton, Southampton, UK
| | - Jeremy Graham Frey
- Department of Chemistry, Highfield Campus, University of Southampton, Southampton, UK
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Vijayan V, Rouillard AD, Rajpal DK, Agarwal P. Could advances in representation learning in Artificial Intelligence provide the new paradigm for data integration in drug discovery? Expert Opin Drug Discov 2019; 14:191-194. [PMID: 30696299 DOI: 10.1080/17460441.2019.1573811] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Affiliation(s)
- Vinaya Vijayan
- a Computational Biology , GlaxoSmithKline R&D , Collegeville , PA , USA
| | | | - Deepak K Rajpal
- a Computational Biology , GlaxoSmithKline R&D , Collegeville , PA , USA
| | - Pankaj Agarwal
- a Computational Biology , GlaxoSmithKline R&D , Collegeville , PA , USA
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45
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Wise J, de Barron AG, Splendiani A, Balali-Mood B, Vasant D, Little E, Mellino G, Harrow I, Smith I, Taubert J, van Bochove K, Romacker M, Walgemoed P, Jimenez RC, Winnenburg R, Plasterer T, Gupta V, Hedley V. Implementation and relevance of FAIR data principles in biopharmaceutical R&D. Drug Discov Today 2019; 24:933-938. [PMID: 30690198 DOI: 10.1016/j.drudis.2019.01.008] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2018] [Revised: 12/21/2018] [Accepted: 01/20/2019] [Indexed: 10/27/2022]
Abstract
Biopharmaceutical industry R&D, and indeed other life sciences R&D such as biomedical, environmental, agricultural and food production, is becoming increasingly data-driven and can significantly improve its efficiency and effectiveness by implementing the FAIR (findable, accessible, interoperable, reusable) guiding principles for scientific data management and stewardship. By so doing, the plethora of new and powerful analytical tools such as artificial intelligence and machine learning will be able, automatically and at scale, to access the data from which they learn, and on which they thrive. FAIR is a fundamental enabler for digital transformation.
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Hoyt CT, Domingo-Fernández D, Aldisi R, Xu L, Kolpeja K, Spalek S, Wollert E, Bachman J, Gyori BM, Greene P, Hofmann-Apitius M. Re-curation and rational enrichment of knowledge graphs in Biological Expression Language. Database (Oxford) 2019; 2019:baz068. [PMID: 31225582 PMCID: PMC6587072 DOI: 10.1093/database/baz068] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 04/03/2019] [Accepted: 04/29/2019] [Indexed: 12/23/2022]
Abstract
The rapid accumulation of new biomedical literature not only causes curated knowledge graphs (KGs) to become outdated and incomplete, but also makes manual curation an impractical and unsustainable solution. Automated or semi-automated workflows are necessary to assist in prioritizing and curating the literature to update and enrich KGs. We have developed two workflows: one for re-curating a given KG to assure its syntactic and semantic quality and another for rationally enriching it by manually revising automatically extracted relations for nodes with low information density. We applied these workflows to the KGs encoded in Biological Expression Language from the NeuroMMSig database using content that was pre-extracted from MEDLINE abstracts and PubMed Central full-text articles using text mining output integrated by INDRA. We have made this workflow freely available at https://github.com/bel-enrichment/bel-enrichment.
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Affiliation(s)
- Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Rana Aldisi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Lingling Xu
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Kristian Kolpeja
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Sandra Spalek
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Esther Wollert
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - John Bachman
- Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Ave, Boston, MA, USA
| | - Benjamin M Gyori
- Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Ave, Boston, MA, USA
| | - Patrick Greene
- Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Ave, Boston, MA, USA
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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Venkatesan A, Tagny Ngompe G, Hassouni NE, Chentli I, Guignon V, Jonquet C, Ruiz M, Larmande P. Agronomic Linked Data (AgroLD): A knowledge-based system to enable integrative biology in agronomy. PLoS One 2018; 13:e0198270. [PMID: 30500839 PMCID: PMC6269127 DOI: 10.1371/journal.pone.0198270] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 09/03/2018] [Indexed: 12/22/2022] Open
Abstract
Recent advances in high-throughput technologies have resulted in a tremendous increase in the amount of omics data produced in plant science. This increase, in conjunction with the heterogeneity and variability of the data, presents a major challenge to adopt an integrative research approach. We are facing an urgent need to effectively integrate and assimilate complementary datasets to understand the biological system as a whole. The Semantic Web offers technologies for the integration of heterogeneous data and their transformation into explicit knowledge thanks to ontologies. We have developed the Agronomic Linked Data (AgroLD- www.agrold.org), a knowledge-based system relying on Semantic Web technologies and exploiting standard domain ontologies, to integrate data about plant species of high interest for the plant science community e.g., rice, wheat, arabidopsis. We present some integration results of the project, which initially focused on genomics, proteomics and phenomics. AgroLD is now an RDF (Resource Description Format) knowledge base of 100M triples created by annotating and integrating more than 50 datasets coming from 10 data sources-such as Gramene.org and TropGeneDB-with 10 ontologies-such as the Gene Ontology and Plant Trait Ontology. Our evaluation results show users appreciate the multiple query modes which support different use cases. AgroLD's objective is to offer a domain specific knowledge platform to solve complex biological and agronomical questions related to the implication of genes/proteins in, for instances, plant disease resistance or high yield traits. We expect the resolution of these questions to facilitate the formulation of new scientific hypotheses to be validated with a knowledge-oriented approach.
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Affiliation(s)
- Aravind Venkatesan
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- LIRMM, Univ. of Montpellier & CNRS, Montpellier, France
| | - Gildas Tagny Ngompe
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- LIRMM, Univ. of Montpellier & CNRS, Montpellier, France
| | - Nordine El Hassouni
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- UMR AGAP, CIRAD, Montpellier, France
- South Green Bioinformatics Platform, Montpellier, France
| | - Imene Chentli
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- LIRMM, Univ. of Montpellier & CNRS, Montpellier, France
| | - Valentin Guignon
- South Green Bioinformatics Platform, Montpellier, France
- Bioversity International, Montpellier, France
| | - Clement Jonquet
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- LIRMM, Univ. of Montpellier & CNRS, Montpellier, France
| | - Manuel Ruiz
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- UMR AGAP, CIRAD, Montpellier, France
- South Green Bioinformatics Platform, Montpellier, France
- AGAP, Univ. of Montpellier, CIRAD, INRA, INRIA, SupAgro, Montpellier, France
| | - Pierre Larmande
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- LIRMM, Univ. of Montpellier & CNRS, Montpellier, France
- South Green Bioinformatics Platform, Montpellier, France
- DIADE, IRD, Univ. of Montpellier, Montpellier, France
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48
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Miller RA, Woollard P, Willighagen EL, Digles D, Kutmon M, Loizou A, Waagmeester A, Senger S, Evelo CT. Explicit interaction information from WikiPathways in RDF facilitates drug discovery in the Open PHACTS Discovery Platform. F1000Res 2018; 7:75. [PMID: 30416713 PMCID: PMC6206606 DOI: 10.12688/f1000research.13197.2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/24/2018] [Indexed: 12/11/2022] Open
Abstract
Open PHACTS is a pre-competitive project to answer scientific questions developed recently by the pharmaceutical industry. Having high quality biological interaction information in the Open PHACTS Discovery Platform is needed to answer multiple pathway related questions. To address this, updated WikiPathways data has been added to the platform. This data includes information about biological interactions, such as stimulation and inhibition. The platform's Application Programming Interface (API) was extended with appropriate calls to reference these interactions. These new methods of the Open PHACTS API are available now.
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Affiliation(s)
- Ryan A Miller
- Department of Bioinformatics (BiGCaT), Maastricht University, Maastricht, The Netherlands
| | | | - Egon L Willighagen
- Department of Bioinformatics (BiGCaT), Maastricht University, Maastricht, The Netherlands
| | - Daniela Digles
- Pharmacoinformatics Research Group, Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria
| | - Martina Kutmon
- Department of Bioinformatics (BiGCaT), Maastricht University, Maastricht, The Netherlands.,Maastricht Center for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
| | | | - Andra Waagmeester
- Department of Bioinformatics (BiGCaT), Maastricht University, Maastricht, The Netherlands.,Micelio, Antwerp, Belgium
| | | | - Chris T Evelo
- Department of Bioinformatics (BiGCaT), Maastricht University, Maastricht, The Netherlands.,Maastricht Center for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.,Open PHACTS Foundation, Science Park, Cambridge, UK
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49
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Yella JK, Yaddanapudi S, Wang Y, Jegga AG. Changing Trends in Computational Drug Repositioning. Pharmaceuticals (Basel) 2018; 11:E57. [PMID: 29874824 PMCID: PMC6027196 DOI: 10.3390/ph11020057] [Citation(s) in RCA: 105] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 06/01/2018] [Accepted: 06/02/2018] [Indexed: 12/12/2022] Open
Abstract
Efforts to maximize the indications potential and revenue from drugs that are already marketed are largely motivated by what Sir James Black, a Nobel Prize-winning pharmacologist advocated-"The most fruitful basis for the discovery of a new drug is to start with an old drug". However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Hence, most of the successfully repositioned drugs are the result of "serendipity", discovered during late phase clinical studies of unexpected but beneficial findings. The connections between drug candidates and their potential adverse drug reactions or new applications are often difficult to foresee because the underlying mechanism associating them is largely unknown, complex, or dispersed and buried in silos of information. Discovery of such multi-domain pharmacomodules-pharmacologically relevant sub-networks of biomolecules and/or pathways-from collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning.
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Affiliation(s)
- Jaswanth K Yella
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 240 Albert Sabin Way MLC 7024, Cincinnati, OH 45229, USA.
| | - Suryanarayana Yaddanapudi
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 240 Albert Sabin Way MLC 7024, Cincinnati, OH 45229, USA.
| | - Yunguan Wang
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 240 Albert Sabin Way MLC 7024, Cincinnati, OH 45229, USA.
| | - Anil G Jegga
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 240 Albert Sabin Way MLC 7024, Cincinnati, OH 45229, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA.
- Department of Computer Science, University of Cincinnati College of Engineering, Cincinnati, OH 45219, USA.
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50
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Schuhmacher A, Gassmann O, McCracken N, Hinder M. Open innovation and external sources of innovation. An opportunity to fuel the R&D pipeline and enhance decision making? J Transl Med 2018; 16:119. [PMID: 29739427 PMCID: PMC5941640 DOI: 10.1186/s12967-018-1499-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Accepted: 04/30/2018] [Indexed: 12/28/2022] Open
Abstract
Historically, research and development (R&D) in the pharmaceutical sector has predominantly been an in-house activity. To enable investments for game changing late-stage assets and to enable better and less costly go/no-go decisions, most companies have employed a fail early paradigm through the implementation of clinical proof-of-concept organizations. To fuel their pipelines, some pioneers started to complement their internal R&D efforts through collaborations as early as the 1990s. In recent years, multiple extrinsic and intrinsic factors induced an opening for external sources of innovation and resulted in new models for open innovation, such as open sourcing, crowdsourcing, public–private partnerships, innovations centres, and the virtualization of R&D. Three factors seem to determine the breadth and depth regarding how companies approach external innovation: (1) the company’s legacy, (2) the company’s willingness and ability to take risks and (3) the company’s need to control IP and competitors. In addition, these factors often constitute the major hurdles to effectively leveraging external opportunities and assets. Conscious and differential choices of the R&D and business models for different companies and different divisions in the same company seem to best allow a company to fully exploit the potential of both internal and external innovations.
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Affiliation(s)
| | - Oliver Gassmann
- Institute for Technology Management, University of St. Gallen, Dufourstrasse 40a, 9000, St. Gallen, Switzerland
| | - Nigel McCracken
- Debiopharm International S.A., Chemin Messidor 5-7, 1002, Lausanne, Switzerland
| | - Markus Hinder
- Novartis Institutes for BioMedical Research, Postfach, Forum 1, 4002, Basel, Switzerland
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