Application of nonnegative matrix factorization to improve profile-profile alignment features for fold recognition and remote ho

Background: Nonnegative matrix factorization (NMF) is a feature extraction method that has the property of intuitive part-based representation of the original features. This unique ability makes NMF a potentially promising method for biological sequence analysis. Here, we apply NMF to fold recognition and remote homolog detection problems. Recent studies have shown that combining support vector machines (SVM) with profile-profile alignments improves performance of fold recognition and remote homolog detection remarkably. However, it is not clear which parts of sequences are essential for the performance improvement. Results: The performance of fold recognition and remote homolog detection using NMF features is compared to that of the unmodified profile-profile alignment (PPA) features by estimating Receiver Operating Characteristic (ROC) scores. The overall performance is noticeably improved. For fold recognition at the fold level, SVM with NMF features recognize 30% of homolog protei...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Inkyung Jung, Jaehyung Lee, Soo-Young Lee, Dongsup Kim
• Keywords: BMCBI 2008, Homolog Detection, NMF Basis Vectors, Original Ppa