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Computational biology and bioinformatics is an interdisciplinary field that develops and applies computational methods to analyse large collections of biological data, such as genetic sequences, cell populations or protein samples, to make new predictions or discover new biology. The computational methods used include analytical methods, mathematical modelling and simulation.
Multicellular modeling is increasingly being used to understand biological systems. SimuCell3D is a tool that allows mechanically realistic simulations, using the deformable cell model, to be developed and run.
Vision–language models can be trained to read cardiac ultrasound images with implications for improving clinical workflows, but additional development and validation will be required before such models can replace humans.
Skeletal muscle is a highly heterogenous tissue that comprises multiple cell types. Leveraging single-cell and single-nucleus experiments, we systematically mapped the cellular and molecular changes across different skeletal muscle compartments with age. We identify neuromuscular-junction accessory nuclei that may be pivotal in mitigating denervation and uncovered differences between myofiber and myonucleus aging.
Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful method for profiling biological samples. Here, the authors have developed a suit of Batch Effect Removal Neural Networks (BERNN) to remove batch effects in large LC-MS experiments to maximize sample classification between conditions.
Polypharmacology drugs are compounds designed to inhibit multiple protein targets. Here, authors use recent advances in AI to rapidly generate polypharmacology compounds against any pair of protein targets, experimentally validating numerous compounds targeting MEK1 and mTOR.
Multicellular modeling is increasingly being used to understand biological systems. SimuCell3D is a tool that allows mechanically realistic simulations, using the deformable cell model, to be developed and run.
Vision–language models can be trained to read cardiac ultrasound images with implications for improving clinical workflows, but additional development and validation will be required before such models can replace humans.