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Machine Learning approaches to protein ranking: discriminative, semi-supervised, scalable algorithms

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84311

Weston,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Weston, J., Leslie C, Elisseeff, A., & Noble, W.(2003). Machine Learning approaches to protein ranking: discriminative, semi-supervised, scalable algorithms (111).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-DC53-8
Abstract
A key tool in protein function discovery is the ability to rank databases of proteins given a query amino acid sequence. The most successful method so far is a web-based tool called PSI-BLAST which uses heuristic alignment of a profile built using the large unlabeled database. It has been shown that such use of global information via an unlabeled data improves over a local measure derived from a basic pairwise alignment such as performed by PSI-BLAST's predecessor, BLAST. In this article we look at ways of leveraging techniques from the field of machine learning for the problem of ranking. We show how clustering and semi-supervised learning techniques, which aim to capture global structure in data, can significantly improve over PSI-BLAST.