ReviewInternational journal of proteomics2014
A survey of computational intelligence techniques in protein function prediction.
Review in International journal of proteomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- MFH-LPI: based on multi-view similarity networks fusion and hypergraph learning for long non-coding RNA-protein interactions prediction.BMC genomics · 2025Article
- Numerical stability of DeepGOPlus inference.PloS one · 2024Article
- LncRNA-protein interaction prediction with reweighted feature selection.BMC bioinformatics · 2023Article
- Gene function finding through cross-organism ensemble learning.BioData mining · 2021Article
- On transformative adaptive activation functions in neural networks for gene expression inference.PloS one · 2021Article
- Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning.Briefings in bioinformatics · 2020Article
- A Literature Review of Gene Function Prediction by Modeling Gene Ontology.Frontiers in genetics · 2020Review
- DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks.Scientific reports · 2019Article
- Constructing Genetic Networks using Biomedical Literature and Rare Event Classification.Scientific reports · 2017Article
- Protein-RNA interactions: structural biology and computational modeling techniques.Biophysical reviews · 2016Review
- De-novo protein function prediction using DNA binding and RNA binding proteins as a test case.Nature communications · 2016Article
- Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics.PLoS computational biology · 2016Article
- DNA-binding protein prediction using plant specific support vector machines: validation and application of a new genome annotation tool.Nucleic acids research · 2015Article
- Functional prediction of hypothetical proteins in human adenoviruses.Bioinformation · 2015Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.