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Bachman JA, Gyori BM, Sorger PK. Automated assembly of molecular mechanisms at scale from text mining and curated databases. Mol Syst Biol 2023; 19:e11325. [PMID: 36938926 PMCID: PMC10167483 DOI: 10.15252/msb.202211325] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 02/24/2023] [Accepted: 02/27/2023] [Indexed: 03/21/2023] Open
Abstract
The analysis of omic data depends on machine-readable information about protein interactions, modifications, and activities as found in protein interaction networks, databases of post-translational modifications, and curated models of gene and protein function. These resources typically depend heavily on human curation. Natural language processing systems that read the primary literature have the potential to substantially extend knowledge resources while reducing the burden on human curators. However, machine-reading systems are limited by high error rates and commonly generate fragmentary and redundant information. Here, we describe an approach to precisely assemble molecular mechanisms at scale using multiple natural language processing systems and the Integrated Network and Dynamical Reasoning Assembler (INDRA). INDRA identifies full and partial overlaps in information extracted from published papers and pathway databases, uses predictive models to improve the reliability of machine reading, and thereby assembles individual pieces of information into non-redundant and broadly usable mechanistic knowledge. Using INDRA to create high-quality corpora of causal knowledge we show it is possible to extend protein-protein interaction databases and explain co-dependencies in the Cancer Dependency Map.
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Affiliation(s)
- John A Bachman
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Benjamin M Gyori
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA.,Department of Systems Biology, Harvard Medical School, Boston, MA, USA
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Subtypes and Mechanisms of Hypertrophic Cardiomyopathy Proposed by Machine Learning Algorithms. LIFE (BASEL, SWITZERLAND) 2022; 12:life12101566. [PMID: 36294999 PMCID: PMC9605444 DOI: 10.3390/life12101566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 09/26/2022] [Accepted: 09/30/2022] [Indexed: 11/06/2022]
Abstract
Hypertrophic cardiomyopathy (HCM) is a relatively common inherited cardiac disease that results in left ventricular hypertrophy. Machine learning uses algorithms to study patterns in data and develop models able to make predictions. The aim of this study is to identify HCM subtypes and examine the mechanisms of HCM using machine learning algorithms. Clinical and laboratory findings of 143 adult patients with a confirmed diagnosis of nonobstructive HCM are analyzed; HCM subtypes are determined by clustering, while the presence of different HCM features is predicted in classification machine learning tasks. Four clusters are determined as the optimal number of clusters for this dataset. Models that can predict the presence of particular HCM features from other genotypic and phenotypic information are generated, and subsets of features sufficient to predict the presence of other features of HCM are determined. This research proposes four subtypes of HCM assessed by machine learning algorithms and based on the overall phenotypic expression of the participants of the study. The identified subsets of features sufficient to determine the presence of particular HCM aspects could provide deeper insights into the mechanisms of HCM.
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Wood EC, Glen AK, Kvarfordt LG, Womack F, Acevedo L, Yoon TS, Ma C, Flores V, Sinha M, Chodpathumwan Y, Termehchy A, Roach JC, Mendoza L, Hoffman AS, Deutsch EW, Koslicki D, Ramsey SA. RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine. BMC Bioinformatics 2022; 23:400. [PMID: 36175836 PMCID: PMC9520835 DOI: 10.1186/s12859-022-04932-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 09/14/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Biomedical translational science is increasingly using computational reasoning on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome, DrugBank, and SMPDB in order to facilitate discovery of new therapeutic targets and modalities. The NCATS Biomedical Data Translator project is working to federate autonomous reasoning agents and knowledge providers within a distributed system for answering translational questions. Within that project and the broader field, there is a need for a framework that can efficiently and reproducibly build an integrated, standards-compliant, and comprehensive biomedical knowledge graph that can be downloaded in standard serialized form or queried via a public application programming interface (API). RESULTS To create a knowledge provider system within the Translator project, we have developed RTX-KG2, an open-source software system for building-and hosting a web API for querying-a biomedical knowledge graph that uses an Extract-Transform-Load approach to integrate 70 knowledge sources (including the aforementioned core six sources) into a knowledge graph with provenance information including (where available) citations. The semantic layer and schema for RTX-KG2 follow the standard Biolink model to maximize interoperability. RTX-KG2 is currently being used by multiple Translator reasoning agents, both in its downloadable form and via its SmartAPI-registered interface. Serializations of RTX-KG2 are available for download in both the pre-canonicalized form and in canonicalized form (in which synonyms are merged). The current canonicalized version (KG2.7.3) of RTX-KG2 contains 6.4M nodes and 39.3M edges with a hierarchy of 77 relationship types from Biolink. CONCLUSION RTX-KG2 is the first knowledge graph that integrates UMLS, SemMedDB, ChEMBL, DrugBank, Reactome, SMPDB, and 64 additional knowledge sources within a knowledge graph that conforms to the Biolink standard for its semantic layer and schema. RTX-KG2 is publicly available for querying via its API at arax.rtx.ai/api/rtxkg2/v1.2/openapi.json . The code to build RTX-KG2 is publicly available at github:RTXteam/RTX-KG2 .
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Affiliation(s)
- E C Wood
- School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA
| | - Amy K Glen
- School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA.
| | - Lindsey G Kvarfordt
- School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA
| | - Finn Womack
- Computer Science and Engineering, Penn State University, State College, PA, USA
| | - Liliana Acevedo
- School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA
| | - Timothy S Yoon
- School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA
| | - Chunyu Ma
- Huck Institutes of the Life Sciences, Penn State University, State College, PA, USA
| | - Veronica Flores
- School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA
| | - Meghamala Sinha
- School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA
| | | | - Arash Termehchy
- School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA
| | | | | | - Andrew S Hoffman
- Interdisciplinary Hub for Digitalization and Society, Radboud University, Nijmegen, The Netherlands
| | | | - David Koslicki
- Computer Science and Engineering, Penn State University, State College, PA, USA.,Huck Institutes of the Life Sciences, Penn State University, State College, PA, USA.,Department of Biology, Penn State University, State College, PA, USA
| | - Stephen A Ramsey
- School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA.,Department of Biomedical Sciences, Oregon State University, Corvallis, OR, USA
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Scholten B, Simón LG, Krishnan S, Vermeulen R, Pronk A, Gyori BM, Bachman JA, Vlaanderen J, Stierum R. Automated Network Assembly of Mechanistic Literature for Informed Evidence Identification to Support Cancer Risk Assessment. ENVIRONMENTAL HEALTH PERSPECTIVES 2022; 130:37002. [PMID: 35238605 PMCID: PMC8893280 DOI: 10.1289/ehp9112] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 12/23/2021] [Accepted: 02/15/2022] [Indexed: 05/20/2023]
Abstract
BACKGROUND Mechanistic data is increasingly used in hazard identification of chemicals. However, the volume of data is large, challenging the efficient identification and clustering of relevant data. OBJECTIVES We investigated whether evidence identification for hazard assessment can become more efficient and informed through an automated approach that combines machine reading of publications with network visualization tools. METHODS We chose 13 chemicals that were evaluated by the International Agency for Research on Cancer (IARC) Monographs program incorporating the key characteristics of carcinogens (KCCs) approach. Using established literature search terms for KCCs, we retrieved and analyzed literature using Integrated Network and Dynamical Reasoning Assembler (INDRA). INDRA combines large-scale literature processing with pathway databases and extracts relationships between biomolecules, bioprocesses, and chemicals into statements (e.g., "benzene activates DNA damage"). These statements were subsequently assembled into networks and compared with the KCC evaluation by the IARC, to evaluate the informativeness of our approach. RESULTS We found, in general, larger networks for those chemicals which the IARC has evaluated the evidence to be strong for KCC induction. Larger networks were not directly linked to publication count, given that we retrieved small networks for several chemicals with little support for KCC activation according to the IARC, despite the significant volume of literature for these specific chemicals. In addition, interpreting networks for genotoxicity and DNA repair showed concordance with the IARC KCC evaluation. DISCUSSION Our method is an automated approach to condense mechanistic literature into searchable and interpretable networks based on an a priori ontology. The approach is no replacement of expert evaluation but, instead, provides an informed structure for experts to quickly identify which statements are made in which papers and how these could connect. We focused on the KCCs because these are supported by well-described search terms. The method needs to be tested in other frameworks as well to demonstrate its generalizability. https://doi.org/10.1289/EHP9112.
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Affiliation(s)
- Bernice Scholten
- Research Group Risk Analysis for Products in Development, The Netherlands Organisation for applied scientific research, Utrecht, Netherlands
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands
| | - Laura Guerrero Simón
- Research Group Risk Analysis for Products in Development, The Netherlands Organisation for applied scientific research, Utrecht, Netherlands
| | - Shaji Krishnan
- Research Group Risk Analysis for Products in Development, The Netherlands Organisation for applied scientific research, Utrecht, Netherlands
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands
| | - Anjoeka Pronk
- Research Group Risk Analysis for Products in Development, The Netherlands Organisation for applied scientific research, Utrecht, Netherlands
| | - Benjamin M. Gyori
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, Massachusetts, USA
| | - John A. Bachman
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, Massachusetts, USA
| | - Jelle Vlaanderen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands
| | - Rob Stierum
- Research Group Risk Analysis for Products in Development, The Netherlands Organisation for applied scientific research, Utrecht, Netherlands
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