ArticlePloS one2011
A protein domain co-occurrence network approach for predicting protein function and inferring species phylogeny.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Regulatory Genes as Beacons for Discovery and Prioritization of Biosynthetic Gene Clusters inBiochemistry · 2025Article
- Domain Architecture Based Methods for Comparative Functional Genomics Toward Therapeutic Drug Target Discovery.Journal of molecular evolution · 2023Review
- Identification of Enzymes-specific Protein Domain Based on DDE, and Convolutional Neural Network.Frontiers in genetics · 2021Article
- Discovery of Native Protein Complexes by Liquid Chromatography Followed by Quantitative Mass Spectrometry.Advances in experimental medicine and biology · 2021Article
- Prediction of Enzyme Function Based on Three Parallel Deep CNN and Amino Acid Mutation.International journal of molecular sciences · 2019Article
- Reconstructing high-resolution chromosome three-dimensional structures by Hi-C complex networks.BMC bioinformatics · 2018Article
- PANDA: Protein function prediction using domain architecture and affinity propagation.Scientific reports · 2018Article
- ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network.Molecules (Basel, Switzerland) · 2017Article
- Integrated protein function prediction by mining function associations, sequences, and protein-protein and gene-gene interaction networks.Methods (San Diego, Calif.) · 2016Article
- Exploring soybean metabolic pathways based on probabilistic graphical model and knowledge-based methods.EURASIP journal on bioinformatics & systems biology · 2015Article
- From Gigabyte to Kilobyte: A Bioinformatics Protocol for Mining Large RNA-Seq Transcriptomics Data.PloS one · 2015Article
- Proteome-wide remodeling of protein location and function by stress.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- NitroDIGE analysis reveals inhibition of protein S-nitrosylation by epigallocatechin gallates in lipopolysaccharide-stimulated microglial cells.Journal of neuroinflammation · 2014Article
- Transcriptomic analysis identifies gene networks regulated by estrogen receptor α (ERα) and ERβ that control distinct effects of different botanical estrogens.Nuclear receptor signaling · 2014Article
- Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data.BMC bioinformatics · 2013Article
- A novel function prediction approach using protein overlap networks.BMC systems biology · 2013Article
- Three-level prediction of protein function by combining profile-sequence search, profile-profile search, and domain co-occurrence networks.BMC bioinformatics · 2013Article
- The properties of genome conformation and spatial gene interaction and regulation networks of normal and malignant human cell types.PloS one · 2013Article
- Reconstructing differentially co-expressed gene modules and regulatory networks of soybean cells.BMC genomics · 2012Article
- Evolutionary dynamics of protein domain architecture in plants.BMC evolutionary biology · 2012Article
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6 authors.
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Abstract
Protein Domain Co-occurrence Network (DCN) is a biological network that has not been fully-studied. We analyzed the properties of the DCNs of H. sapiens, S. cerevisiae, C. elegans, D. melanogaster, and 15 plant genomes. These DCNs have the hallmark features of scale-free networks. We investigated the possibility of using DCNs to predict protein and domain functions. Based on our experiment conducted on 66 randomly selected proteins, the best of top 3 predictions made by our DCN-based aggregated neighbor-counting method achieved a semantic similarity score of 0.81 to the actual Gene Ontology terms of the proteins. Moreover, the top 3 predictions using neighbor-counting, χ(2), and a SVM-based method achieved an accuracy of 66%, 59%, and 61%, respectively, when used to predict specific Gene Ontology terms of human target domains. These predictions on average had a semantic similarity score of 0.82, 0.80, and 0.79 to the actual Gene Ontology terms, respectively. We also used DCNs to predict whether a domain is an enzyme domain, and our SVM-based and neighbor-inference method correctly classified 79% and 77% of the target domains, respectively. When using DCNs to classify a target domain into one of the six enzyme classes, we found that, as long as there is one EC number available in the neighboring domains, our SVM-based and neighboring-counting method correctly classified 92.4% and 91.9% of the target domains, respectively. Furthermore, we benchmarked the performance of using DCNs to infer species phylogenies on six different combinations of 398 single-chromosome prokaryotic genomes. The phylogenetic tree of 54 prokaryotic taxa generated by our DCNs-alignment-based method achieved a 93.45% similarity score compared to the Bergey's taxonomy. In summary, our studies show that genome-wide DCNs contain rich information that can be effectively used to decipher protein function and reveal the evolutionary relationship among species.
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