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Elucidata's Liver OmixAtlas: The World's Most Comprehensive & Curated Liver Tissue-Derived Data
Drug discovery has evolved into a data-driven process since the advent of high-throughput omics technologies. Vast amounts of biomedical multi-omics data are being generated by academic labs and organizations worldwide. These data hold tremendous potential for reuse and discovery but are scattered across multiple, disparate public sources and lack standardization. The availability of data does not equate to its easy usability. Further, to harness the power of large volumes of biomedical data, machine learning applications are increasingly being utilized. This makes the need for an efficient means of exploring and accessing molecular data in a machine learning-ready format, an immediate necessity.
Elucidata’s Liver OmixAtlas, the largest, multi-omics data repository of liver tissue-derived data, aims to address these issues. It contains 7000+ biomedical multi-omics datasets (Genomics, Transcriptomics, Proteomics, and Metabolomics) from several public sources (GEO, TCGA, DepMap, LINCS, MetaboLights), encompassing normal as well as perturbed (disease, drug treatment, gene knockouts/down, etc.) states of liver tissue. The data have been ML-curated with an accuracy matching that of human experts and is ready for downstream machine learning applications. In keeping with the rapid pace of biological data generation, OmixAtlas is updated with a frequency that is in sync with the source repositories.
Data integrability is another key feature of OmixAtlas. This enables any number of harmonized omics data to be integrated and analyzed, thereby enriching the process of data exploration and insight discovery. You can also analyze these data with a toolbox of scalable and customizable bioinformatics pipelines on our cloud-based platform, Polly, or on your preferred analytical platform. Liver OmixAtlas, thus, will accelerate the process of deriving unique insights for research questions and enable R&D teams to make better data-driven decisions to discover therapeutics!


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