Abstract. Most of the work in machine learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
Background: Single Nucleotide Polymorphisms (SNPs) are the most abundant form of genomic variation and can cause phenotypic differences between individuals, including diseases. Ba...
Vinayak Kulkarni, Mounir Errami, Robert Barber, Ha...
We present BayesMD, a Bayesian Motif Discovery model with several new features. Three different types of biological a priori knowledge are built into the framework in a modular fa...
In recent years, with the rapid proliferation of digital images, the need to search and retrieve the images accurately, efficiently, and conveniently is becoming more acute. Automa...
BACKGROUND: Defect predictors learned from static code measures can isolate code modules with a higher than usual probability of defects. AIMS: To improve those learners by focusi...