Conditional random fields for transmembrane helix prediction

Lior Lukov, Sanjay Chawla, W. Bret Church

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 9th Pacific-Asia Conference, PAKDD 2005, Proceedings
PublisherSpringer Verlag
Number of pages7
ISBN (Print)3540260765, 9783540260769
StatePublished - 2005
Externally publishedYes
Event9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005 - Hanoi, Viet Nam
Duration: May 18 2005May 20 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3518 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005
Country/TerritoryViet Nam


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