SVM-Hustle - An iterative semi-supervised machine learning approach for pairwise protein remote homology detection

Journal Article · Sat Mar 15 04:00:00 EDT 2008 · Bioinformatics, 24(6):783-790

DOI:https://doi.org/10.1093/bioinformatics/btn028· OSTI ID:985035

Shah, Anuj R; Oehmen, Chris S; Webb-Robertson, Bobbie-Jo M

Motivation: As the amount of biological sequence data continues to grow exponentially we face the increasing challenge of assigning function to this enormous molecular ‘parts list’. The most popular approaches to this challenge make use of the simplifying assumption that similar functional molecules, or proteins, sometimes have similar composition, or sequence. However, these algorithms often fail to identify remote homologs (proteins with similar function but dissimilar sequence) which often are a significant fraction of the total homolog collection for a given sequence. We introduce a Support Vector Machine (SVM)-based tool to detect Homology Using Semisupervised iTerative LEarning (SVM-HUSTLE) that detects significantly more remote homologs than current state-of-the-art sequence or cluster-based methods. As opposed to building profiles or position specific scoring matrices, SVM-HUSTLE builds an SVM classifier for a query sequence by training on a collection of representative highconfidence training sets. SVM-HUSTLE combines principles of semi-supervised learning theory with statistical sampling to create many concurrent classifiers to iteratively detect and refine on-the-fly patterns indicating homology. Results: When compared against existing methods for identifying protein homologs (BLASTp, PSI-BLAST, RANKPROP, MOTIFPROP and their variants) on the SCOP 1.59 benchmark dataset consisting of 7329 protein sequences, SVM-HUSTLE significantly outperforms each of the above methods using the most stringent ROC1 statistic with p-values less than 1e-20.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (US), Environmental Molecular Sciences Laboratory (EMSL)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 985035
• Report Number(s): PNNL-SA-56589; 20905; KJ0101030
• Journal Information: Bioinformatics, 24(6):783-790, Journal Name: Bioinformatics, 24(6):783-790 Journal Issue: 6 Vol. 24; ISSN 1460-2059; ISSN 1367-4803
• Country of Publication: United States
• Language: English

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
99 GENERAL AND MISCELLANEOUS
ALGORITHMS
BENCHMARKS
DETECTION
Environmental Molecular Sciences Laboratory
FUNCTIONALS
LEARNING
MATRICES
PROTEINS
SAMPLING
STATISTICS
TRAINING
VECTORS