Meet Excelra at CBI Annual Meeting 2026

The growing complexity of cancer biology and the availability of clinical and multi-omics data have created new opportunities for precision oncology. We present an integrated in-silico framework that transforms heterogeneous datasets into standardized, analysis-ready resources through curation, harmonization, quality control, and integration. Machine learning and statistical methods identify predictive and prognostic biomarkers that enable biologically meaningful patient stratification, distinguish responders from non-responders, and reveal mechanisms of therapeutic response and resistance. Validated biomarkers are further assessed across molecularly similar diseases to identify new patient populations that may benefit from existing therapies, while target identification, in-silico perturbation, and drug repurposing analyses provide mechanistic insights and generate testable therapeutic hypotheses. Together, this framework supports data-driven patient stratification, indication expansion, and more informed precision oncology decision-making.