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SANGO efficiently removed batch effects between the query and reference single-cell ATAC signals through the underlying genome sequences, to enable cell type assignment according to the reference data. The method achieved superior performance on diverse datasets and could detect unknown tumor cells, providing valuable functional biological signals.
Approaches are needed to accelerate the discovery of transition metal complexes (TMCs), which is challenging owing to their vast chemical space. A large dataset of diverse ligands is now introduced and leveraged in a multiobjective genetic algorithm that enables the efficient optimization of TMCs in chemical spaces containing billions of them.
We present SCORPION, a computational tool to model gene regulatory networks based on single-cell transcriptomic data and prior knowledge of gene regulation. SCORPION networks can be modeled for specific cell types in individual samples, and are therefore suitable for conducting comparisons between experimental groups.
The laws of physics, formulated in a compact form, are elusive for complex dynamic phenomena. However, it is now shown that, using artificial intelligence constrained by the physical Onsager principle, a custom thermodynamic description of a complex system can be constructed from the observation of its dynamical behavior.
