Background: A number of methods that use both protein structural and evolutionary information are available to predict the functional consequences of missense mutations. However, ...
Chris J. Needham, James R. Bradford, Andrew J. Bul...
We study the potential benefits to classification prediction that arise from having access to unlabeled samples. We compare learning in the semi-supervised model to the standard, ...
This paper considers nonlinear modeling based on a limited amount of experimental data and a simulator built from prior knowledge. The problem of how to best incorporate the data ...
Abstract--We present a tool that facilitates the efficient extension of morphological lexica. The tool exploits information from a morphological lexicon, a morphological grammar an...
Given multiple possible models b1, b2, . . . bn for a protein structure, a common sub-task in in-silico Protein Structure Prediction is ranking these models according to their qua...