The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Accurate Prediction of Protein Disordered Regions by Mining Protein Structure Data
Data Mining and Knowledge Discovery
Prediction of DNA-binding residues from sequence
Bioinformatics
Bioinformatics
TOPTMH: Topology Predictor for Transmembrane α-Helices
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Hi-index | 0.00 |
Over the last decade several prediction methods have been developed for determining structural and functional properties of individual protein residues using sequence and sequence-derived information. Most of these methods are based on support vector machines as they provide accurate and generalizable prediction models. We developed a general purpose protein residue annotation toolkit (Pro SAT ) to allow biologists to formulate residue-wise prediction problems. Pro SAT formulates annotation problem as a classification or regression problem using support vector machines. For every residue Pro SAT captures local information (any sequence-derived information) around the reside to create fixed length feature vectors. Pro SAT implements accurate and fast kernel functions, and also introduces a flexible window-based encoding scheme that allows better capture of signals for certain prediction problems. In this work we evaluate the performance of Pro SAT on the disorder prediction and contact order estimation problems, studying the effect of the different kernels introduced here. Pro SAT shows better or at least comparable performance to state-of-the-art prediction systems. In particular Pro SAT has proven to be the best performing transmembrane-helix predictor on an independent blind benchmark. Availability: http://bio.dtc.umn.edu/prosat