144 citations · 269 across the 8 of their papers we have counts for
8 papers
Disentangled Wasserstein Autoencoder for T-Cell Receptor Engineering
Tianxiao Li, Hongyu Guo, Filippo Grazioli +2
In protein biophysics, the separation between the functionally important residues (forming the active site or binding surface) and those that create the overall structure (the fold…
Scalable privacy-preserving cancer type prediction with homomorphic encryption
Esha Sarkar, Eduardo Chielle, Gamze Gursoy +3
Machine Learning (ML) alleviates the challenges of high-dimensional data analysis and improves decision making in critical applications like healthcare. Effective cancer type from…
Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives
Jonathan Warrell, Mark Gerstein
Deep Probabilistic Programming (DPP) allows powerful models based on recursive computation to be learned using efficient deep-learning optimization techniques. Additionally, DPP of…
Forest Fire Clustering for Single-cell Sequencing with Iterative Label Propagation and Parallelized Monte Carlo Simulation
Zhanlin Chen, Jeremy Goldwasser, Philip Tuckman +3
In the era of single-cell sequencing, there is a growing need to extract insights from data with clustering methods. Here, we introduce Forest Fire Clustering, an efficient and int…
Quantum Computing at the Frontiers of Biological Sciences
Prashant S. Emani, Jonathan Warrell, Alan Anticevic +15
The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we pus…
Rank Projection Trees for Multilevel Neural Network Interpretation
Jonathan Warrell, Hussein Mohsen, Mark Gerstein
A variety of methods have been proposed for interpreting nodes in deep neural networks, which typically involve scoring nodes at lower layers with respect to their effects on the o…