Beilun Wang

DEEP MOTIF DASHBOARD: VISUALIZING AND UNDERSTANDING GENOMIC SEQUENCES USING DEEP NEURAL NETWORKS.

Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification. We demonstrate how to visualize and understand three important DNN models: convolutional, recurrent, and convolutional-recurrent networks. Our first visualization method is finding a test sequences saliency map which uses first-order derivatives to describe the importance of each nucleotide in making the final prediction. Second, considering recurrent models make predictions in a temporal manner (from one end of a TFBS sequence to the other), we introduce temporal output scores, indicating the prediction score of a model over time for a sequential input. Lastly, a class-specific visualization strategy finds the optimal input sequence for a given TFBS positive class via stochastic gradient optimization. Our experimental results indicate that a convolutional-recurrent architecture performs the best among the three architectures. The visualization techniques indicate that CNN-RNN makes predictions by modeling both motifs as well as dependencies among them.

GaKCo: A Fast Gapped k-mer String Kernel Using Counting.

String Kernel (SK) techniques, especially those using gapped

A constrained \(\ell \)1 minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models

k

A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples

-mers as features (gk), have obtained great success in classifying sequences like DNA, protein, and text. However, the state-of-the-art gk-SK runs extremely slow when we increase the dictionary size (

A Theoretical Framework for Robustness of (Deep) Classifiers Under Adversarial Noise.

\Sigma

DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples

) or allow more mismatches (

DeepMask: Masking DNN Models for robustness against adversarial samples.

M

A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models

). This is because current gk-SK uses a trie-based algorithm to calculate co-occurrence of mismatched substrings resulting in a time cost proportional to

Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure

O(\Sigma^{M})

A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models

. We propose a \textbf{fast} algorithm for calculating \underline{Ga}pped