Eigengene

eigengene develops proprietary ai, and uses it to analyze cancer whole genome sequencing (wgs) data, in order to develop (i) personalized prognostics and diagnostics, and (ii) novel therapeutics. our algorithms do what no others can, namely, simultaneously find both the similarities and dissimilarities across multiple and diverse high-dimensional datasets, known as tensors. this enables them to find what other methods miss. for example, in cancer genomic data, our algorithms were able to identify multi-hundred-thousand marker dna signatures that predict a patient’s survival and response to treatment. our algorithms can scale to petabyte-sized datasets, yet are sensitive enough to detect meaningful patterns in cohorts of as few as 250 patients, each with a genomic profile of billions of nucleotides. this enables us to tap the full wealth of information that sequencing the whole genome provides. most other methods have difficulty finding patterns in such “skinny” datasets, in which the n