Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks
| Autor: | Zhong-Hui Duan, Mark R. Dalman, Joseph S. Haddad, Anthony Deeter |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Cell signaling Muscle Physiology Physiology Gene Identification and Analysis lcsh:Medicine Datasets as Topic Genetic Networks Signal transduction Bioinformatics Biochemistry Bayes' theorem Immune Physiology Protein Interaction Mapping Medicine and Health Sciences lcsh:Science Musculoskeletal System media_common Innate Immune System Multidisciplinary Protein Kinase Signaling Cascade Muscles Signaling cascades STAT signaling Cytokines Anatomy Network Analysis Research Article Muscle Contraction PubMed Cell biology Computer and Information Sciences media_common.quotation_subject Immunology Computational biology Biology Set (abstract data type) Gene product 03 medical and health sciences Ingenuity Genetics KEGG Protein Interactions Cardiac Muscles Biology and life sciences lcsh:R Experimental data Bayesian network Proteins Bayes Theorem Molecular Development 030104 developmental biology JAK-STAT signaling cascade Immune System lcsh:Q Developmental Biology |
| Zdroj: | PLoS ONE PLoS ONE, Vol 12, Iss 10, p e0186004 (2017) |
| ISSN: | 1932-6203 |
| Popis: | The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways. |
| Databáze: | OpenAIRE |
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