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May JC, McLean JA. Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2016; 9:387-409. [PMID: 27306312 PMCID: PMC5763907 DOI: 10.1146/annurev-anchem-071015-041734] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Hybrid analytical instrumentation constructed around mass spectrometry (MS) is becoming the preferred technique for addressing many grand challenges in science and medicine. From the omics sciences to drug discovery and synthetic biology, multidimensional separations based on MS provide the high peak capacity and high measurement throughput necessary to obtain large-scale measurements used to infer systems-level information. In this article, we describe multidimensional MS configurations as technologies that are big data drivers and review some new and emerging strategies for mining information from large-scale datasets. We discuss the information content that can be obtained from individual dimensions, as well as the unique information that can be derived by comparing different levels of data. Finally, we summarize some emerging data visualization strategies that seek to make highly dimensional datasets both accessible and comprehensible.
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Affiliation(s)
- Jody C May
- Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute for Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, Tennessee 37235;
| | - John A McLean
- Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute for Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, Tennessee 37235;
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52
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Determining the Degree of Promiscuity of Extensively Assayed Compounds. PLoS One 2016; 11:e0153873. [PMID: 27082988 PMCID: PMC4833426 DOI: 10.1371/journal.pone.0153873] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Accepted: 04/05/2016] [Indexed: 11/21/2022] Open
Abstract
In the context of polypharmacology, an emerging concept in drug discovery, promiscuity is rationalized as the ability of compounds to specifically interact with multiple targets. Promiscuity of drugs and bioactive compounds has thus far been analyzed computationally on the basis of activity annotations, without taking assay frequencies or inactivity records into account. Most recent estimates have indicated that bioactive compounds interact on average with only one to two targets, whereas drugs interact with six or more. In this study, we have further extended promiscuity analysis by identifying the most extensively assayed public domain compounds and systematically determining their promiscuity. These compounds were tested in hundreds of assays against hundreds of targets. In our analysis, assay promiscuity was distinguished from target promiscuity and separately analyzed for primary and confirmatory assays. Differences between the degree of assay and target promiscuity were surprisingly small and average and median degrees of target promiscuity of 2.6 to 3.4 and 2.0 were determined, respectively. Thus, target promiscuity remained at a low level even for most extensively tested active compounds. These findings provide further evidence that bioactive compounds are less promiscuous than drugs and have implications for pharmaceutical research. In addition to a possible explanation that drugs are more extensively tested for additional targets, the results would also support a “promiscuity enrichment model” according to which promiscuous compounds might be preferentially selected for therapeutic efficacy during clinical evaluation to ultimately become drugs.
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53
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Activity and property landscape modeling is at the interface of chemoinformatics and medicinal chemistry. Future Med Chem 2016; 7:1197-211. [PMID: 26132526 DOI: 10.4155/fmc.15.51] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Property landscape modeling (PLM) methods are at the interface of experimental sciences and computational chemistry. PLM are becoming a common strategy to describe systematically structure-property relationships of datasets. Thus far, PLM have been used mainly in medicinal chemistry and drug discovery. Herein, we survey advances on key topics on PLM with emphasis on questions often raised regarding the outcomes of the property landscape studies. We also emphasize on concepts of PLM that are being extended to other experimental areas beyond drug discovery. Topics discussed in this paper include applications of PLM to further characterize protein-ligand interactions, the utility of PLM as a quantitative and descriptive approach, and the statistical validation of property cliffs.
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54
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Cho M, So I, Chun JN, Jeon JH. The antitumor effects of geraniol: Modulation of cancer hallmark pathways (Review). Int J Oncol 2016; 48:1772-82. [PMID: 26983575 PMCID: PMC4809657 DOI: 10.3892/ijo.2016.3427] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Accepted: 02/18/2016] [Indexed: 12/30/2022] Open
Abstract
Geraniol is a dietary monoterpene alcohol that is found in the essential oils of aromatic plants. To date, experimental evidence supports the therapeutic or preventive effects of geraniol on different types of cancer, such as breast, lung, colon, prostate, pancreatic, and hepatic cancer, and has revealed the mechanistic basis for its pharmacological actions. In addition, geraniol sensitizes tumor cells to commonly used chemotherapy agents. Geraniol controls a variety of signaling molecules and pathways that represent tumor hallmarks; these actions of geraniol constrain the ability of tumor cells to acquire adaptive resistance against anticancer drugs. In the present review, we emphasize that geraniol is a promising compound or chemical moiety for the development of a safe and effective multi-targeted anticancer agent. We summarize the current knowledge of the effects of geraniol on target molecules and pathways in cancer cells. Our review provides novel insight into the challenges and perspectives with regard to geraniol research and to its application in future clinical investigation.
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Affiliation(s)
- Minsoo Cho
- Undergraduate Research Program, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Insuk So
- Department of Physiology and Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Jung Nyeo Chun
- Department of Physiology and Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Ju-Hong Jeon
- Department of Physiology and Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
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55
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Jasial S, Hu Y, Bajorath J. Assessing the Growth of Bioactive Compounds and Scaffolds over Time: Implications for Lead Discovery and Scaffold Hopping. J Chem Inf Model 2016; 56:300-7. [PMID: 26838127 DOI: 10.1021/acs.jcim.5b00713] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The increase in compounds with activity against five major therapeutic target families has been quantified on a time scale and investigated employing a compound-scaffold-cyclic skeleton (CSK) hierarchy. The analysis was designed to better understand possible reasons for target-dependent growth of bioactive compounds. There was strong correlation between compound and scaffold growth across all target families. Active compounds becoming available over time were mostly represented by new scaffolds. On the basis of scaffold-to-compound ratios, new active compounds were structurally diverse and, on the basis of CSK-to-scaffold ratios, often had previously unobserved topologies. In addition, novel targets emerged that complemented major families. The analysis revealed that compound growth is associated with increasing chemical diversity and that current pharmaceutical targets are capable of recognizing many structurally different compounds, which provides a rationale for the rapid increase in the number of bioactive compounds over the past decade. In light of these findings, it is likely that new chemical entities will be discovered for many small molecule targets including relatively unexplored ones as well as for popular and well-studied therapeutic targets. Moreover, given the wealth of new "active scaffolds" that have been increasingly identified for many targets over time, computational scaffold-hopping exercises should generally have a high likelihood of success.
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Affiliation(s)
- Swarit Jasial
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität , Dahlmannstr. 2, D-53113 Bonn, Germany
| | - Ye Hu
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität , Dahlmannstr. 2, D-53113 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität , Dahlmannstr. 2, D-53113 Bonn, Germany
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56
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Hu Y, Stumpfe D, Bajorath J. Computational Exploration of Molecular Scaffolds in Medicinal Chemistry. J Med Chem 2016; 59:4062-76. [DOI: 10.1021/acs.jmedchem.5b01746] [Citation(s) in RCA: 79] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Ye Hu
- Department of Life Science
Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal
Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Dagmar Stumpfe
- Department of Life Science
Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal
Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science
Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal
Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
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57
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Gawehn E, Hiss JA, Schneider G. Deep Learning in Drug Discovery. Mol Inform 2015; 35:3-14. [PMID: 27491648 DOI: 10.1002/minf.201501008] [Citation(s) in RCA: 309] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2015] [Accepted: 12/01/2015] [Indexed: 12/18/2022]
Abstract
Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks.
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Affiliation(s)
- Erik Gawehn
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland, Fax: +41 44 633 13 79, Tel: +41 44 633 74 38
| | - Jan A Hiss
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland, Fax: +41 44 633 13 79, Tel: +41 44 633 74 38
| | - Gisbert Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland, Fax: +41 44 633 13 79, Tel: +41 44 633 74 38.
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58
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Pushing the boundaries of computational approaches: special focus issue on computational chemistry and computer-aided drug discovery. Future Med Chem 2015; 7:2415-7. [PMID: 26653118 DOI: 10.4155/fmc.15.157] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
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59
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Abstract
In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.
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60
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Abstract
Computational approaches are an integral part of interdisciplinary drug discovery research. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true impact on drug discovery at different levels. If applied in a scientifically meaningful way, computational methods improve the ability to identify and evaluate potential drug molecules, but there remain weaknesses in the methods that preclude naïve applications. Herein, current trends in computer-aided drug discovery are reviewed, and selected computational areas are discussed. Approaches are highlighted that aid in the identification and optimization of new drug candidates. Emphasis is put on the presentation and discussion of computational concepts and methods, rather than case studies or application examples. As such, this contribution aims to provide an overview of the current methodological spectrum of computational drug discovery for a broad audience.
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Affiliation(s)
- Jürgen Bajorath
- Department of Life Science Informatics, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, Bonn, D-53113, Germany
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61
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Finding the right approach to big data-driven medicinal chemistry. Future Med Chem 2015; 7:1213-6. [DOI: 10.4155/fmc.15.58] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
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Abstract
The
F1000Research publishing platform offers the opportunity to launch themed article collections as a part of its dynamic publication environment. The idea of article collections is further expanded through the generation of publication channels that focus on specific scientific areas or disciplines. This editorial introduces the
Chemical Information Science channel of
F1000Research designed to collate high-quality publications and foster a culture of open peer review. Articles will be selected by guest editor(s) and a group of experts, the channel Editorial Board, and subjected to open peer review.
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Affiliation(s)
- Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, Bonn, D-53113, Germany
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63
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Osolodkin DI, Radchenko EV, Orlov AA, Voronkov AE, Palyulin VA, Zefirov NS. Progress in visual representations of chemical space. Expert Opin Drug Discov 2015; 10:959-73. [DOI: 10.1517/17460441.2015.1060216] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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64
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Nussbaumer P. Medicinal Chemists of the 21stCentury-Who Are We and Where to Go? ChemMedChem 2015; 10:1133-9. [DOI: 10.1002/cmdc.201500133] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Indexed: 12/11/2022]
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65
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Stahl M, Baier S. How Many Molecules Does It Take to Tell a Story? Case Studies, Language, and an Epistemic View of Medicinal Chemistry. ChemMedChem 2015; 10:949-56. [DOI: 10.1002/cmdc.201500091] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Indexed: 12/26/2022]
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66
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Gaspar HA, Baskin II, Marcou G, Horvath D, Varnek A. Chemical data visualization and analysis with incremental generative topographic mapping: big data challenge. J Chem Inf Model 2014; 55:84-94. [PMID: 25423612 DOI: 10.1021/ci500575y] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
This paper is devoted to the analysis and visualization in 2-dimensional space of large data sets of millions of compounds using the incremental version of generative topographic mapping (iGTM). The iGTM algorithm implemented in the in-house ISIDA-GTM program was applied to a database of more than 2 million compounds combining data sets of 36 chemicals suppliers and the NCI collection, encoded either by MOE descriptors or by MACCS keys. Taking advantage of the probabilistic nature of GTM, several approaches to data analysis were proposed. The chemical space coverage was evaluated using the normalized Shannon entropy. Different views of the data (property landscapes) were obtained by mapping various physical and chemical properties (molecular weight, aqueous solubility, LogP, etc.) onto the iGTM map. The superposition of these views helped to identify the regions in the chemical space populated by compounds with desirable physicochemical profiles and the suppliers providing them. The data sets similarity in the latent space was assessed by applying several metrics (Euclidean distance, Tanimoto and Bhattacharyya coefficients) to data probability distributions based on cumulated responsibility vectors. As a complementary approach, data sets were compared by considering them as individual objects on a meta-GTM map, built on cumulated responsibility vectors or property landscapes produced with iGTM. We believe that the iGTM methodology described in this article represents a fast and reliable way to analyze and visualize large chemical databases.
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Affiliation(s)
- Héléna A Gaspar
- Laboratory of Chemoinformatics, University of Strasbourg , 67081 Strasbourg, France
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67
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Medina-Franco JL, Méndez-Lucio O, Dueñas-González A, Yoo J. Discovery and development of DNA methyltransferase inhibitors using in silico approaches. Drug Discov Today 2014; 20:569-77. [PMID: 25526932 DOI: 10.1016/j.drudis.2014.12.007] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2014] [Revised: 11/19/2014] [Accepted: 12/10/2014] [Indexed: 01/08/2023]
Abstract
Multiple strategies have evolved during the past few years to advance epigenetic compounds targeting DNA methyltransferases (DNMTs). Significant progress has been made in HTS, lead optimization and determination of 3D structures of DNMTs. In light of the emerging concept of epi-informatics, computational approaches are employed to accelerate the development of DNMT inhibitors helping to screen chemical databases, mine the DNMT-relevant chemical space, uncover SAR and design focused libraries. Computational methods also synergize with natural-product-based drug discovery and drug repurposing. Herein, we survey the latest developments of in silico approaches to advance epigenetic drug and probe discovery targeting DNMTs.
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Affiliation(s)
- José L Medina-Franco
- Facultad de Química, Departamento de Farmacia, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico.
| | - Oscar Méndez-Lucio
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Alfonso Dueñas-González
- Unidad de Investigación Biomédica en Cáncer, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México and Instituto Nacional de Cancerología, Av. San Fernando 22, Mexico City 14080, Mexico
| | - Jakyung Yoo
- Life Science Research Institute, Daewoong Pharmaceutical Co. Ltd., 72 Dugye-Ro, Pogok-Eup, Gyeonggi-do 449-814, Republic of Korea
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68
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Big Data Literature Search. BIG DATA 2014; 2:230-232. [PMID: 27442757 DOI: 10.1089/big.2014.1526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
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69
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Wassermann AM, Lounkine E, Davies JW, Glick M, Camargo LM. The opportunities of mining historical and collective data in drug discovery. Drug Discov Today 2014; 20:422-34. [PMID: 25463034 DOI: 10.1016/j.drudis.2014.11.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2014] [Revised: 10/21/2014] [Accepted: 11/10/2014] [Indexed: 12/26/2022]
Abstract
Vast amounts of bioactivity data have been generated for small molecules across public and corporate domains. Biological signatures, either derived from systematic profiling efforts or from existing historical assay data, have been successfully employed for small molecule mechanism-of-action elucidation, drug repositioning, hit expansion and screening subset design. This article reviews different types of biological descriptors and applications, and we demonstrate how biological data can outlive the original purpose or project for which it was generated. By comparing 150 HTS campaigns run at Novartis over the past decade on the basis of their active and inactive chemical matter, we highlight the opportunities and challenges associated with cross-project learning in drug discovery.
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Affiliation(s)
- Anne Mai Wassermann
- In Silico Lead Discovery, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA 02139, USA.
| | - Eugen Lounkine
- In Silico Lead Discovery, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - John W Davies
- In Silico Lead Discovery, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Meir Glick
- In Silico Lead Discovery, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - L Miguel Camargo
- In Silico Lead Discovery, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA 02139, USA.
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70
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Abstract
OBJECTIVES To summarise current research that takes advantage of "Big Data" in health and biomedical informatics applications. METHODS Survey of trends in this work, and exploration of literature describing how large-scale structured and unstructured data sources are being used to support applications from clinical decision making and health policy, to drug design and pharmacovigilance, and further to systems biology and genetics. RESULTS The survey highlights ongoing development of powerful new methods for turning that large-scale, and often complex, data into information that provides new insights into human health, in a range of different areas. Consideration of this body of work identifies several important paradigm shifts that are facilitated by Big Data resources and methods: in clinical and translational research, from hypothesis-driven research to data-driven research, and in medicine, from evidence-based practice to practice-based evidence. CONCLUSIONS The increasing scale and availability of large quantities of health data require strategies for data management, data linkage, and data integration beyond the limits of many existing information systems, and substantial effort is underway to meet those needs. As our ability to make sense of that data improves, the value of the data will continue to increase. Health systems, genetics and genomics, population and public health; all areas of biomedicine stand to benefit from Big Data and the associated technologies.
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Affiliation(s)
- F Martin-Sanchez
- Fernando Martin-Sanchez, Health and Biomedical Informatics Centre, The University of Melbourne, Parkville VIC 3010, Australia, E-mail:
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71
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Computational prediction of microRNA networks incorporating environmental toxicity and disease etiology. Sci Rep 2014; 4:5576. [PMID: 24992957 PMCID: PMC4081875 DOI: 10.1038/srep05576] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2014] [Accepted: 06/17/2014] [Indexed: 12/25/2022] Open
Abstract
MicroRNAs (miRNAs) play important roles in multiple biological processes and have attracted much scientific attention recently. Their expression can be altered by environmental factors (EFs), which are associated with many diseases. Identification of the phenotype-genotype relationships among miRNAs, EFs, and diseases at the network level will help us to better understand toxicology mechanisms and disease etiologies. In this study, we developed a computational systems toxicology framework to predict new associations among EFs, miRNAs and diseases by integrating EF structure similarity and disease phenotypic similarity. Specifically, three comprehensive bipartite networks: EF-miRNA, EF-disease and miRNA-disease associations, were constructed to build predictive models. The areas under the receiver operating characteristic curves using 10-fold cross validation ranged from 0.686 to 0.910. Furthermore, we successfully inferred novel EF-miRNA-disease networks in two case studies for breast cancer and cigarette smoke. Collectively, our methods provide a reliable and useful tool for the study of chemical risk assessment and disease etiology involving miRNAs.
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