Machine-learning models have demonstrated great success in learning complex patterns
that enable them to make predictions about unobserved data. In addition to using models for …

Machine-learning models have demonstrated great success in learning complex patterns
that enable them to make predictions about unobserved data. In addition to using models for …

Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes,
genome-wide binding sites for proteins, and many other molecular processes. However, …

As the COVID-19 outbreak evolves, accurate forecasting continues to play an extremely
important role in informing policy decisions. In this article, we present our continuous curation of …

Tree ensembles such as Random Forests have achieved impressive empirical success across
a wide variety of applications. To understand how these models make predictions, people …

Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical
concepts through human-machine collaboration during the generation of data, the …

Increasing occurrence of harmful algal blooms across the land–water interface poses significant
risks to coastal ecosystem structure and human health. Defining significant drivers and …

High-content microscopy offers a scalable approach to screen against multiple targets in a
single pass. Prior work has focused on methods to select “optimal” cellular readouts in …

A major challenge for understanding and treating Amyotrophic Lateral Sclerosis (ALS) is that
most patients have no known genetic cause. Even within defined genetic subtypes, patients …

Advances in supervised learning have enabled accurate prediction in biological systems
governed by complex interactions among biomolecules. However, state-of-the-art predictive …