Herophilus developed the first complex in vitro model of C4A induced schizophrenia. Read about it in our bioRxiv preprint.
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Every data/AI-driven drug discovery company must eventually do real-world biology experiments. A new class of human in vitro experimental models is ripe to serve this need.
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Herophilus leaders explain the importance of data science in their research, exploring a score that identifies hierarchical confounder effects in raw data and machine learning-derived data embeddings.
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Lone single-target approaches, are doomed to fail in the quest to cure polygenic neurological diseases such as Alzheimer’s, schizophrenia, Parkinson’s, and autisms — ironically, common neurological diseases of high prevalence. A new approach is needed.
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Explore how Herophilus approaches hierarchical confounder discovery in the experiment–machine learning cycle in recent paper in Cell Patterns.
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Herophilus is working on new methods in transcriptomic analysis. Read about our bioRxiv preprint and open source code release of Demuxalot.
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Learn how Herophilus optimizes the scaling of patient-derived brain organoids to uncover deep phenotypes of disease in our bioRxiv preprint.
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