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arXiv Machine Learning By Jung Min Kang

Reverse Item Response Theory for Sparsity-Robust Ranking in Fragmented Cancer Drug-Response Matrices

Read the original on arXiv Machine Learning →

The paper introduces reverse Item Response Theory (IRT) for analyzing cancer drug‑response data, treating cancer types as latent subjects with resistance ability and drugs as items with evasion difficulty. Using 242,036 measurements from the GDSC2 database, the model estimates cancer‑type‑level in‑vitro resistance and drug‑level broad activity on a shared latent scale. Across four missingness regimes, reverse IRT outperforms simple averaging, achieving higher correlation (Δρ = +0.089 to +0.095 at 60% missingness) and the best Brier score among five methods, with stable classifications for 19 of 28 cancer types and 82% directional agreement in a cross‑platform PRISM replication.

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