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SIGIR
2006
ACM
16 years 24 days ago
Large scale semi-supervised linear SVMs
Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In...
Vikas Sindhwani, S. Sathiya Keerthi
RECOMB
2004
Springer
16 years 7 months ago
Computational identification of evolutionarily conserved exons
Phylogenetic hidden Markov models (phylo-HMMs) have recently been proposed as a means for addressing a multispecies version of the ab initio gene prediction problem. These models ...
Adam C. Siepel, David Haussler
BMCBI
2010
124views more  BMCBI 2010»
15 years 7 months ago
A factor model to analyze heterogeneity in gene expression
Background: Microarray technology allows the simultaneous analysis of thousands of genes within a single experiment. Significance analyses of transcriptomic data ignore the gene d...
Yuna Blum, Guillaume Le Mignon, Sandrine Lagarrigu...
CIKM
2006
Springer
15 years 10 months ago
Finding highly correlated pairs efficiently with powerful pruning
We consider the problem of finding highly correlated pairs in a large data set. That is, given a threshold not too small, we wish to report all the pairs of items (or binary attri...
Jian Zhang, Joan Feigenbaum
BMCBI
2006
91views more  BMCBI 2006»
15 years 6 months ago
PeanutMap: an online genome database for comparative molecular maps of peanut
Background: Molecular maps have been developed for many species, and are of particular importance for varietal development and comparative genomics. However, despite the existence...
Arun M. Jesubatham, Mark D. Burow