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» Conditional Random Fields for Transmembrane Helix Prediction
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PAKDD
2005
ACM
180views Data Mining» more  PAKDD 2005»
13 years 10 months ago
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 event...
Lior Lukov, Sanjay Chawla, W. Bret Church
CSB
2005
IEEE
124views Bioinformatics» more  CSB 2005»
13 years 10 months ago
Multi-Scale Hierarchical Structure Prediction of Helical Transmembrane Proteins
As the first step toward a multi-scale, hierarchical computational approach for membrane protein structure prediction, the packing of transmembrane helices was modeled at the resi...
Zhong Chen, Ying Xu
RECOMB
2005
Springer
14 years 4 months ago
Segmentation Conditional Random Fields (SCRFs): A New Approach for Protein Fold Recognition
Abstract. Protein fold recognition is an important step towards understanding protein three-dimensional structures and their functions. A conditional graphical model, i.e. segmenta...
Yan Liu, Jaime G. Carbonell, Peter Weigele, Vanath...
JCB
2006
215views more  JCB 2006»
13 years 4 months ago
Protein Fold Recognition Using Segmentation Conditional Random Fields (SCRFs)
Protein fold recognition is an important step towards understanding protein three-dimensional structures and their functions. A conditional graphical model, i.e., segmentation con...
Yan Liu 0002, Jaime G. Carbonell, Peter Weigele, V...
JMLR
2010
160views more  JMLR 2010»
12 years 11 months ago
Neural conditional random fields
We propose a non-linear graphical model for structured prediction. It combines the power of deep neural networks to extract high level features with the graphical framework of Mar...
Trinh Minh Tri Do, Thierry Artières