A Bayesian ensemble learning method is introduced for unsupervised extraction of dynamic processes from noisy data. The data are assumed to be generated by an unknown nonlinear ma...
The NIPS 2003 workshops included a feature selection competition organized by the authors. We provided participants with five datasets from different application domains and calle...
Isabelle Guyon, Steve R. Gunn, Asa Ben-Hur, Gideon...
Identifying functionally important sites from biological sequences, formulated as a biological sequence labeling problem, has broad applications ranging from rational drug design ...
We present a method for explaining predictions for individual instances. The presented approach is general and can be used with all classification models that output probabilities...
Background: Prediction of transmembrane (TM) helices by statistical methods suffers from lack of sufficient training data. Current best methods use hundreds or even thousands of f...