ArticleMolecular genetics and genomics : MGG2016
Identification of compound-protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds.
Article in Molecular genetics and genomics : MGG, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- The potential role of ANXA11/ANXA3 protein level ratio in predicting antidepressant treatment outcomes.European archives of psychiatry and clinical neuroscience · 2026Article
- Identifying pathological myopia associated genes with GenePlexus in protein-protein interaction network.Frontiers in genetics · 2025Article
- Integrative ATAC-seq and RNA-seq analyses of IPEC-J2 cells reveals porcine transcription and chromatin accessibility changes associated withFrontiers in microbiology · 2023Article
- CCDC103 as a Prognostic Biomarker Correlated with Tumor Progression and Immune Infiltration in Glioma.OncoTargets and therapy · 2023Article
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- Biclique extension as an effective approach to identify missing links in metabolic compound-protein interaction networks.Bioinformatics advances · 2022Article
- A review on machine learning approaches and trends in drug discovery.Computational and structural biotechnology journal · 2021Review
- Use of a Network-Based Method to Identify Latent Genes Associated with Hearing Loss in Children.Frontiers in cell and developmental biology · 2021Article
- Identification of Common Genes and Pathways in Eight Fibrosis Diseases.Frontiers in genetics · 2020Article
- Inferring novel genes related to oral cancer with a network embedding method and one-class learning algorithms.Gene therapy · 2019Article
- Inferring novel genes related to colorectal cancer via random walk with restart algorithm.Gene therapy · 2019Article
- The transcriptome difference between colorectal tumor and normal tissues revealed by single-cell sequencing.Journal of Cancer · 2019Article
- Analysis of Protein-Protein Functional Associations by Using Gene Ontology and KEGG Pathway.BioMed research international · 2019Article
- Classification of Widely and Rarely Expressed Genes with Recurrent Neural Network.Computational and structural biotechnology journal · 2019Article
- Tissue Expression Difference between mRNAs and lncRNAs.International journal of molecular sciences · 2018Article
- Inferring Novel Tumor Suppressor Genes with a Protein-Protein Interaction Network and Network Diffusion Algorithms.Molecular therapy. Methods & clinical development · 2018Article
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- Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia.Molecular therapy. Nucleic acids · 2018Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Compound-protein interactions play important roles in every cell via the recognition and regulation of specific functional proteins. The correct identification of compound-protein interactions can lead to a good comprehension of this complicated system and provide useful input for the investigation of various attributes of compounds and proteins. In this study, we attempted to understand this system by extracting properties from both proteins and compounds, in which proteins were represented by gene ontology and KEGG pathway enrichment scores and compounds were represented by molecular fragments. Advanced feature selection methods, including minimum redundancy maximum relevance, incremental feature selection, and the basic machine learning algorithm random forest, were used to analyze these properties and extract core factors for the determination of actual compound-protein interactions. Compound-protein interactions reported in The Binding Databases were used as positive samples. To improve the reliability of the results, the analytic procedure was executed five times using different negative samples. Simultaneously, five optimal prediction methods based on a random forest and yielding maximum MCCs of approximately 77.55 % were constructed and may be useful tools for the prediction of compound-protein interactions. This work provides new clues to understanding the system of compound-protein interactions by analyzing extracted core features. Our results indicate that compound-protein interactions are related to biological processes involving immune, developmental and hormone-associated pathways.
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