Conditional random fields for transmembrane helix prediction

Lior Lukov, Sanjay Chawla, W. Bret Church

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages155-161
Number of pages7
Volume3518 LNAI
Publication statusPublished - 2005
Externally publishedYes
Event9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005 - Hanoi
Duration: 18 May 200520 May 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3518 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005
CityHanoi
Period18/5/0520/5/05

Fingerprint

Membrane Protein
Conditional Random Fields
Helix
Membrane Proteins
Proteins
Membranes
Prediction
Genes
Statistical Models
Human Genome
Domain Knowledge
Protein Structure
Probabilistic Model
Drugs
Genome
High Resolution
Tissue
Gene
Target
Cell

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Lukov, L., Chawla, S., & Church, W. B. (2005). Conditional random fields for transmembrane helix prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3518 LNAI, pp. 155-161). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3518 LNAI).

Conditional random fields for transmembrane helix prediction. / Lukov, Lior; Chawla, Sanjay; Church, W. Bret.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3518 LNAI 2005. p. 155-161 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3518 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lukov, L, Chawla, S & Church, WB 2005, Conditional random fields for transmembrane helix prediction. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3518 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3518 LNAI, pp. 155-161, 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005, Hanoi, 18/5/05.
Lukov L, Chawla S, Church WB. Conditional random fields for transmembrane helix prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3518 LNAI. 2005. p. 155-161. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Lukov, Lior ; Chawla, Sanjay ; Church, W. Bret. / Conditional random fields for transmembrane helix prediction. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3518 LNAI 2005. pp. 155-161 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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