The Journal of Machine Learning Research
Accurate Prediction of Protein Disordered Regions by Mining Protein Structure Data
Data Mining and Knowledge Discovery
Meta-DP: domain prediction meta-server
Bioinformatics
SPlitSSI-SVM: An algorithm to reduce the misleading and increase the strength of domain signal
Computers in Biology and Medicine
Predicting protective bacterial antigens using random forest classifiers
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
PPM-Dom: A novel method for domain position prediction
Computational Biology and Chemistry
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Protein domains are the structural and functional units of proteins. The ability to parse protein chains into different domains is important for protein classification and for understanding protein structure, function, and evolution. Here we use machine learning algorithms, in the form of recursive neural networks, to develop a protein domain predictor called DOMpro. DOMpro predicts protein domains using a combination of evolutionary information in the form of profiles, predicted secondary structure, and predicted relative solvent accessibility. DOMpro is trained and tested on a curated dataset derived from the CATH database. DOMpro correctly predicts the number of domains for 69% of the combined dataset of single and multi-domain chains. DOMpro achieves a sensitivity of 76% and specificity of 85% with respect to the single-domain proteins and sensitivity of 59% and specificity of 38% with respect to the two-domain proteins. DOMpro also achieved a sensitivity and specificity of 71% and 71% respectively in the Critical Assessment of Fully Automated Structure Prediction 4 (CAFASP-4) (Fisher et al., 1999; Saini and Fischer, 2005) and was ranked among the top ab initio domain predictors. The DOMpro server, software, and dataset are available at http://www.igb.uci.edu/servers/psss.html.