Jack Lanchantin

TL;DR: It is found that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art.

TL;DR: DeepWordBug as mentioned in this paper generates small text perturbations in a black-box setting that force a deep-learning classifier to misclassify a text input by scoring strategies to find the most important words to modify.

TL;DR: A unified discriminative framework using a deep convolutional neural network to classify gene expression using histone modification data as input and it is shown that DeepChrome outperforms state-of-the-art models like Support Vector Machines and Random Forests for gene expression classification task on 56 different cell-types from REMC database.

TL;DR: A toolkit called the Deep Motif Dashboard (DeMo Dashboard) is proposed which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification, and results indicate that a convolutional-recurrent architecture performs the best among the three architectures.

TL;DR: The Classification Transformer (C-Tran) is proposed, a general framework for multi-label image classification that leverages Transformers to exploit the complex dependencies among visual features and labels.