Bayesian networks can capture causal relations, but learning such a network from data is
NP-hard. Recent work has made it possible to approximate this problem as a continuous …

Abstract Machine learning has been increasingly used for protein engineering. However,
because the general sequence contexts they capture are not specific to the protein being …

The emergence of large-scale genomic, chemical and pharmacological data provides new
opportunities for drug discovery and repositioning. In this work, we develop a computational …

Enzyme function annotation is a fundamental challenge, and numerous computational tools
have been developed. However, most of these tools cannot accurately predict functional …

The global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
requires an urgent need to find effective therapeutics for the treatment of coronavirus disease …

Cell-line screens create expansive datasets for learning predictive markers of drug response,
but these models do not readily translate to the clinic with its diverse contexts and limited …

Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial
role in protein engineering and drug design. In this study, we develop GeoPPI, a novel …

Surface reflectance data with high resolutions in both space and time have been desired
and demanded by scientific research and societal applications. Standard satellite missions …

Leaf area index (LAI) is a key variable for characterizing crop growth conditions and estimating
crop productivity. Despite continuing efforts to develop LAI estimation algorithms, LAI …

Traditional drug development heavily relies on human-derived rational and effort to detect
the functional mechanisms of diseases, identify druggable targets, and design lead …