Conditional Random Fields for Transmembrane Helix Prediction

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.

• Type: Conference
• Year: 2005
• Where: PAKDD
• Authors: Lior Lukov, Sanjay Chawla, W. Bret Church
• Keywords: Data Mining, Helix Prediction, IMPs, Membrane Protein, PAKDD 2005