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Cellformatica is an innovative startup developing a pioneering AI technology for meaningful interpretation of massive high-throughput genomic screening datasets. Genetic screens generate immense volumes of data and interpreting them in a consistent, timely and meaningful way is a significant challenge for scientists. To solve this problem, Cellformatica employs artificial intelligence, natural language processing, and advanced knowledge representation techniques to link screening results with scientific understanding. By using knowledge representation, we construct a semantic network of the scientific literature, enabling our AI to comprehend and interpret the intricacies of genetic information more efficiently and accurately. This revolutionizes the time frame for data analysis, cutting it down from months to a few days for teams in academic and pharmaceutical settings. Cellformatica is specifically designed for the complexities and nuances of expert-level gene interpretation, leveraging AI and knowledge representation to analyze millions of PubMed abstracts and highly-cited full-text articles, as well as structured biological databases. Our proprietary AI chain framework summarizes and reasons over these data, providing our users with precise, consistent quantitative insights to dramatically enhance results interpretation and lead selection. To provide an enriched biological context, we assess results in aggregate, reconstructing probable gene regulatory pathways and performing combinatorial perturbation analysis. Founded by a group of seasoned entrepreneur-scientists, Cellformatica is at the cutting edge of a revolution, transforming how genomic data is interpreted by empowering scientists to make swift, well-informed decisions.

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Harness the knowledge of AI custom trained on complete corpus of scientific data and literature to de-noise functional screen data and understand the connections between the candidate compounds or genes and the integrative effects they cause.

Not all features are currently implemented.

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Warning: App may appear to work well but has not been peer reviewed. Not intended for clinical use. Use with caution.


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