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eRNAs detected in patient-derived transcriptomic data can delineate potential active enhancer regions in oesophageal adenocarcinoma and uncover genes associated with the cancer phenotype.

Using targeted functional manipulations of neural activity to map neuromodulatory connections can deliver considerable insight into the functional architecture of a behaviorally important network even in the absence of information about its synaptic connectivity.

A novel pan-neuronal antibody has revealed the organization of the nerve net controlling all behavior in the simple cnidarian Hydra.

Although courtship vocalizations are unique to each species, the basic architecture of the neural circuitries underlying this behavior is conserved among closely related species of frogs, suggesting behavior can diverge while utilizing the homologous neural network inherited through evolutionary lineage.

Proper gut health depends on the balance between two naturally occurring variants of a transcription factor in the colon.

A robust machine learning diagnostic platform utilizing exosome proteins to distinguish various human cancers in diverse biological fluids.

Deep neural networks can be trained to automatically find mechanistic models which quantitatively agree with experimental data, providing new opportunities for building and visualizing interpretable models of neural dynamics.

Cancer is a consequence of the release of basal cellular functions inherited from our unicellular ancestors from the control of regulatory networks that evolved during the emergence of multicellularity.

Genetically engineered murine models reveal novel mechanisms of cell identity regulation in lung cancer and provide insights into the complex interplay between lineage specifiers and oncogenic signaling pathways in this disease.