Payel Das

TL;DR: The proposed method to obtain a few collective coordinates by using nonlinear dimensionality reduction can efficiently find a low-dimensional representation of a complex process such as protein folding.

TL;DR: In this paper, contrastive explanations are used to justify the classification of an input by a black box classifier such as a deep neural network, where the authors find what should be minimally and sufficiently present (viz. important object pixels in an image) to justify its classification and analogously what should not be necessary and sufficient (i.e. certain background pixels).

TL;DR: A novel method that provides contrastive explanations justifying the classification of an input by a black box classifier such as a deep neural network is proposed and it is argued that such explanations are natural for humans and are used commonly in domains such as health care and criminology.

TL;DR: The present results suggest that γD-crystallin may polymerize through successive domain swapping of those three C-terminal β-strands leading to age-onset cataract, as an evolutionary cost of its very high stability.

TL;DR: In this article, a computational method leveraging deep learning and molecular dynamics simulations enables the rapid discovery of antimicrobial peptides with low toxicity and with high potency against diverse Gram-positive and Gram-negative pathogens.