Conditional random fields for transmembrane helix prediction

Authors:
Lior Lukov;Sanjay Chawla;W. Bret Church
Affiliations:
School of Information Technologies, Sydney University, NSW, Australia;School of Information Technologies, Sydney University, NSW, Australia;Department of Physiology and Pharmacology, University of New South Wales, NSW, Australia
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2005

Citing 3
Cited 0

Markov random field modeling in computer vision

Markov random field modeling in computer vision
Protein Structure Prediction: A Practical Approach

Protein Structure Prediction: A Practical Approach
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning

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Abstract

It is estimated that 20% of genes in the human genome encode for integral membrane proteins (IMPs) and some estimates are much higher. IMPs control a broad range of events essential to the proper functioning of cells, tissues and organisms and are the most common target of clinically useful drugs [1]. However there is a dearth of high-resolution 3D structural information on the IMPs. Therefore good prediction methods of IMPs structures are to be highly valued. In this paper we apply Conditional Random Fields (CRFs) to build a probabilistic model to solve the membrane protein helix prediction problem. The advantage of CRFs is that it allows seamless and principled integration of biological domain knowledge into the model. Our results show that the CRF model outperforms other well known helix prediction approaches on several important measures.