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- W2104460324 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." @default.
- W2104460324 created "2016-06-24" @default.
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- W2104460324 date "2005-01-01" @default.
- W2104460324 modified "2023-09-27" @default.
- W2104460324 title "Conditional Random Fields for Transmembrane Helix Prediction" @default.
- W2104460324 cites W1507028917 @default.
- W2104460324 cites W2019187626 @default.
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- W2104460324 doi "https://doi.org/10.1007/11430919_20" @default.
- W2104460324 hasPublicationYear "2005" @default.
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