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Open accessAbstract
Rattus norvegicus, or the rat, has been widely used as animal models for a diversity of human diseases in the last 150 years. The rat, as a disease model, has the advantage of relatively large body size and highly similar physiology to humans. In drug discovery, rat models are routinely used in drug efficacy and toxicity assessments. To facilitate molecular pharmacology studies in rats, we present the predicted rat interactome database (PRID), which is a database of high-quality predicted functional gene interactions with balanced sensitivity and specificity. PRID integrates functional gene association data from 10 public databases and infers 305 939 putative functional associations, which are expected to include 13.02% of all rat protein interactions, and 52.59% of these function associations may represent protein interactions. This set of functional interactions may not only facilitate hypothesis formulation in molecular mechanism studies, but also serve as a reference interactome for users to perform gene set linkage analysis (GSLA), which is a web-based tool to infer the potential functional impacts of a set of changed genes observed in transcriptomics analyses. In a case study, we show that GSLA based on PRID may provide more precise and informative annotations for investigators to understand the physiological mechanisms underlying a phenotype and lead investigators to testable hypotheses for further studies. Widely used functional annotation tools such as Gene Ontology (GO) analysis, and Database for Annotation, Visualization and Integrated Discovery (DAVID) did not provide similar insights. Database URL: http://rat.biomedtzc.cn.
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This improvement facilitates a deeper understanding of the internal structure and function of various organisms.
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From Large language models in medical and healthcare fields: applications, advances, and challenges (2024) · cites this paper as Tao et al. 2020
LLMs can enhance image quality and resolution by reconstructing high-quality images from raw data obtained during medical imaging procedures.
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From Large language models in medical and healthcare fields: applications, advances, and challenges (2024) · cites this paper as Tao et al. 2020
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Large language models in medical and healthcare fields: applications, advances, and challenges
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- Authors
- Tao, Y.-T., Ding, X.-B., Jin, J., Zhang, H.-B., Guo, W.-P., Ruan, L., Yang, Q.-L., Chen, P.-C., Yao, H., & Chen, X.
- Affiliations
- Taizhou University (CN)
- Zhejiang University (CN)
- University of Toronto (CA)
- Venue
- Database
- Publisher
- Oxford University Press (OUP)
- Published
- 2020
- Volume
- 2020
- ISSN
- 1758-0463 (ISSN-L)
- PMC
- PMC7678787
- OpenAlex
- W3105909779
- Cited by
- 5
- References
- 46
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 1 · 2022: 1
- FWCI
- 0.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Database (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- protein-protein interaction, functional annotation, drug discovery, molecular pharmacology, interactome database
- MeSH terms
- Animals, Gene Expression Profiling, Databases, Genetic, Rats, Gene Ontology
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