Reverse Item Response Theory for Sparsity-Robust Ranking in Fragmented Cancer Drug-Response Matrices
Researchers apply reverse Item Response Theory to pharmacogenomics to rank cancer drug sensitivity.
By modeling cancer types as subjects with resistance traits and drugs as items with evasion difficulty, this approach addresses sparsity in fragmented drug-response datasets. The model provides a robust ranking system for drug activity across diverse cancer types.