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» Predicting Nucleolar Proteins Using Support-Vector Machines
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BMCBI
2008
97views more  BMCBI 2008»
14 years 9 months ago
SiteSeek: Post-translational modification analysis using adaptive locality-effective kernel methods and new profiles
Background: Post-translational modifications have a substantial influence on the structure and functions of protein. Post-translational phosphorylation is one of the most common m...
Paul D. Yoo, Yung Shwen Ho, Bing Bing Zhou, Albert...
85
Voted
IFIP12
2010
14 years 8 months ago
Learning the Preferences of News Readers with SVM and Lasso Ranking
We attack the task of predicting which news-stories are more appealing to a given audience by comparing ‘most popular stories’, gathered from various online news outlets, over ...
Elena Hensinger, Ilias N. Flaounas, Nello Cristian...
105
Voted
BMCBI
2008
100views more  BMCBI 2008»
14 years 9 months ago
PatternLab for proteomics: a tool for differential shotgun proteomics
Background: A goal of proteomics is to distinguish between states of a biological system by identifying protein expression differences. Liu et al. demonstrated a method to perform...
Paulo C. Carvalho, Juliana S. G. Fischer, Emily I....
JMLR
2010
121views more  JMLR 2010»
14 years 4 months ago
Sparse Semi-supervised Learning Using Conjugate Functions
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Shiliang Sun, John Shawe-Taylor
DAGM
2004
Springer
15 years 2 months ago
Learning from Labeled and Unlabeled Data Using Random Walks
We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to...
Dengyong Zhou, Bernhard Schölkopf