Large language models (LLMs) have the potential to revolutionize behavioral science by
accelerating and improving the research cycle, from conceptualization to data analysis …

As the global burden of antibiotic resistance continues to grow, creative approaches to
antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly …

Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …

We are now entering a new era in protein sequence and structure annotation, with hundreds
of millions of predicted protein structures made available through the AlphaFold database …

In any given machine learning problem, there may be many models that could explain the
data almost equally well. However, most learning algorithms return only one of these …

Dimension reduction (DR) algorithms project data from high dimensions to lower
dimensions to enable visualization of interesting high-dimensional structure. DR algorithms …

Here we represent human lives in a way that shares structural similarity to language, and we
exploit this similarity to adapt natural language processing techniques to examine the …

Rare genetic diseases are typically caused by a single gene defect. Despite this clear
causal relationship between genotype and phenotype, identifying the pathobiological …

We previously reported the results of a randomized phase II trial (NCT02904954) in patients
with early-stage non-small cell lung cancer (NSCLC) who were treated with either two …

This paper investigates the theoretical foundations of the t-distributed stochastic neighbor
embedding (t-SNE) algorithm, a popular nonlinear dimension reduction and data …