Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …

Abstract Machine learning (ML) promises to tackle the grand challenges in chemistry and
speed up the generation, improvement and/or ordering of research hypotheses. Despite the …

Background The reporting of machine learning (ML) prognostic and diagnostic modeling
studies is often inadequate, making it difficult to understand and replicate such studies. To …

Pathway enrichment analysis (PEA) is a computational biology method that identifies
biological functions that are overrepresented in a group of genes more than would be …

In population genetics, the emergence of large-scale genomic data for various species and
populations has provided new opportunities to understand the evolutionary forces that drive …

Proteins are “social molecules.” Recent experimental evidence supports the notion that
large protein aggregates, known as biomolecular condensates, affect structurally and …

Background Preeclampsia presents a highly prevalent burden on pregnant women with an
estimated incidence of 2% to 5%. Preeclampsia increases the maternal risk of death 20-fold …

Ensuring consistent high yields and product quality are key challenges in biomanufacturing.
Even minor deviations in critical process parameters (CPPs) such as media and feed …

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as
highly reliable and effective methods for predicting protein structures. This article explores …

SHAP (Shapley additive explanations) is a framework for explainable AI that makes
explanations locally and globally. In this work, we propose a general method to obtain …