Search Results for author:
Found 6 papers, 3 papers with code
Learning from learning machines: a new generation of AI technology to meet the needs of science
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery.
Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery
We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain.
Sparse Canonical Correlation Analysis via Concave Minimization
We also introduceDirected Sparse CCA, which is able to find associations which are aligned with a specified experiment design, andMulti-View sCCA which is used to discover associations between multiple sets of covariates.
BLOCCS: Block Sparse Canonical Correlation Analysis With Application To Interpretable Omics Integration
We introduce Block Sparse Canonical Correlation Analysis which estimates multiple pairs of canonical directions (together a "block") at once, resulting in significantly improved orthogonality of the sparse directions which, we demonstrate, translates to more interpretable solutions.
Refining interaction search through signed iterative Random Forests
We describe stable and predictive importance metrics to rank signed interactions.
Iterative Random Forests to detect predictive and stable high-order interactions
Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes.
