Debajyoti Ray

TL;DR: A fast heuristic algorithm, derived from ridge regression, to integrate multiple functional association networks and predict gene function from a single process-specific network using label propagation, that is efficient enough to be deployed on a modern webserver and as accurate as the leading methods on the MouseFunc I benchmark and a new yeast function prediction benchmark.

TL;DR: This paper investigates the problem of learning optimal descriptors for a given classification task using the kernel learning framework and learns the optimal, domain-specific kernel as a combination of base kernels corresponding to base features which achieve different levels of trade-off.

TL;DR: The results show that currently available data for mammals allows predictions with both breadth and accuracy, and many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized.

TL;DR: This paper proposes an approach to utilizing category-based information in segmentation, through a formulation as an image labelling problem, that exploits bottom-up image cues to create an over-segmented representation of an image.

TL;DR: In this article, the authors propose an approach to utilize category-based information in segmentation, through a formulation as an image labelling problem, which exploits bottom-up image cues to create an over-segmented representation of an image.