首页|Stepwise Bayesian Machine Learning Uncovers a Novel Gene Regulatory Network Comp onent in Neural Tube Development
Stepwise Bayesian Machine Learning Uncovers a Novel Gene Regulatory Network Comp onent in Neural Tube Development
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from bi orxiv.org: “Recent advancements in machine learning-based data processing techniques have f acilitated the inference of gene regulatory interactions and the identification of key genes from multidimensional gene expression data. “In this study, we applied a stepwise Bayesian framework to uncover a novel regu latory component involved in differentiation of specific neural and neuronal cel ls. We treated naive neural precursor cells with Sonic Hedgehog (Shh) at various concentrations and time points, generating comprehensive whole-genome sequencin g data that captured dynamic gene expression profiles during differentiation. Th e genes were categorized into 224 subsets based on their expression profiles, an d the relationships between these subsets were extrapolated. To accurately predi ct gene regulation among subsets, known networks were used as a core model and s ubsets to be added were tested stepwise.
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