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Phase diagrams for 140 variants of prion-like low-complexity domains are computed to establish scaling laws that quantify the impact of amino acid mutations on the thermodynamic stability of condensates.

An interpretable machine learning approach can recapitulate the molecular features associated to immune protein-protein binding and leverage them for the prediction of immune response specificity.

Quantitative proof that persistent cell migration in the timescale of hours relies on a feedback between polarized trafficking and protrusive activity stabilizing cell front.

Acute myeloid leukemia-specific predictive logic models reveal new therapeutic targets involved in cancer resistance mechanisms paving the way for personalized precision medicine.

Applying deep learning tools to pinpoint predictive gaps in behavioral cognitive modeling.

A computational framework enables the inference of hydrodynamic continuum models for collective cell migration from live-cell imaging data recorded in zebrafish embryos.

Minimal modifications of common regulatory circuits can allow bacteria to learn from the past to better predict the future, on a physiological, rather than evolutionary, timescale.

Full-text analysis of the human brain-imaging literature can predict the spatial distribution of observations in the brain given the description of a subject of study.

Epigenetic signature of maternal smoking contributes to smaller body size and lower birth weight in newborns of European and South Asian ancestry.

Predicting age jointly from multimodal brain images and electrophysiology with machine learning enhances detecting health issues and facets of cognitive decline.