ArticleMethods (San Diego, Calif.)2016
Integrated protein function prediction by mining function associations, sequences, and protein-protein and gene-gene interaction networks.
Article in Methods (San Diego, Calif.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Improving protein function prediction by learning and integrating representations of protein sequences and function labels.Bioinformatics advances · 2024Article
- RecGOBD: accurate recognition of gene ontology related brain development protein functions through multi-feature fusion and attention mechanisms.Bioinformatics advances · 2024Article
- A tensor-based bi-random walks model for protein function prediction.BMC bioinformatics · 2022Article
- Multiple Profile Models Extract Features from Protein Sequence Data and Resolve Functional Diversity of Very Different Protein Families.Molecular biology and evolution · 2022Article
- Article
- A computational framework for identifying the transcription factors involved in enhancer-promoter loop formation.Molecular therapy. Nucleic acids · 2021Article
- DNSS2: Improved ab initio protein secondary structure prediction using advanced deep learning architectures.Proteins · 2021Article
- PASS: Protein Annotation Surveillance Site for Protein Annotation Using Homologous Clusters, NLP, and Sequence Similarity Networks.Frontiers in bioinformatics · 2021Article
- Review
- Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning.Briefings in bioinformatics · 2020Article
- Article
- Prediction of protein group function by iterative classification on functional relevance network.Bioinformatics (Oxford, England) · 2019Article
- Improving protein function prediction using protein sequence and GO-term similarities.Bioinformatics (Oxford, England) · 2019Article
- Function Prediction for G Protein-Coupled Receptors through Text Mining and Induction Matrix Completion.ACS omega · 2019Article
- A Multi-Label Supervised Topic Model Conditioned on Arbitrary Features for Gene Function Prediction.Genes · 2019Article
- Article
- Identification of Phage Viral Proteins With Hybrid Sequence Features.Frontiers in microbiology · 2019Article
- A partially function-to-topic model for protein function prediction.BMC genomics · 2018Article
- HECIL: A Hybrid Error Correction Algorithm for Long Reads with Iterative Learning.Scientific reports · 2018Article
- A novel methodology on distributed representations of proteins using their interacting ligands.Bioinformatics (Oxford, England) · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
motivationsProtein function prediction is an important and challenging problem in bioinformatics and computational biology. Functionally relevant biological information such as protein sequences, gene expression, and protein-protein interactions has been used mostly separately for protein function prediction. One of the major challenges is how to effectively integrate multiple sources of both traditional and new information such as spatial gene-gene interaction networks generated from chromosomal conformation data together to improve protein function prediction.
resultsIn this work, we developed three different probabilistic scores (MIS, SEQ, and NET score) to combine protein sequence, function associations, and protein-protein interaction and spatial gene-gene interaction networks for protein function prediction. The MIS score is mainly generated from homologous proteins found by PSI-BLAST search, and also association rules between Gene Ontology terms, which are learned by mining the Swiss-Prot database. The SEQ score is generated from protein sequences. The NET score is generated from protein-protein interaction and spatial gene-gene interaction networks. These three scores were combined in a new Statistical Multiple Integrative Scoring System (SMISS) to predict protein function. We tested SMISS on the data set of 2011 Critical Assessment of Function Annotation (CAFA). The method performed substantially better than three base-line methods and an advanced method based on protein profile-sequence comparison, profile-profile comparison, and domain co-occurrence networks according to the maximum F-measure.
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