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» Predicting Nucleolar Proteins Using Support-Vector Machines
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136
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BMCBI
2008
138views more  BMCBI 2008»
15 years 3 months ago
Using neural networks and evolutionary information in decoy discrimination for protein tertiary structure prediction
Background: We present a novel method of protein fold decoy discrimination using machine learning, more specifically using neural networks. Here, decoy discrimination is represent...
Ching-Wai Tan, David T. Jones
137
Voted
UAI
2000
15 years 5 months ago
Variational Relevance Vector Machines
The Support Vector Machine (SVM) of Vapnik [9] has become widely established as one of the leading approaches to pattern recognition and machine learning. It expresses predictions...
Christopher M. Bishop, Michael E. Tipping
136
Voted
BIOCOMP
2006
15 years 5 months ago
Insight of the Signal Motif of GPI-(like)-anchored Proteins by using SVM
- Many proteins contain a signal sequence at their COOH-terminus recognized by glycosylphosphatidylinositol (GPI) anchor and attached on the membrane. Experimental result suggests ...
Wei Cao, Kazuya Sumikoshi, Tohru Terada, Shugo Nak...
139
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JMLR
2011
148views more  JMLR 2011»
14 years 10 months ago
Multitask Sparsity via Maximum Entropy Discrimination
A multitask learning framework is developed for discriminative classification and regression where multiple large-margin linear classifiers are estimated for different predictio...
Tony Jebara
96
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ICML
2008
IEEE
16 years 4 months ago
Sequence kernels for predicting protein essentiality
The problem of identifying the minimal gene set required to sustain life is of crucial importance in understanding cellular mechanisms and designing therapeutic drugs. This work d...
Cyril Allauzen, Mehryar Mohri, Ameet Talwalkar