ITNR: Inversion Transformer-based Neural Ranking for Cancer Drug Recommendations

Abstract

ABSTRACTPersonalized drug response prediction is an approach for tailoring effective therapeutic strategies for patients based on their tumors’ genomic characterization. The current study introduces a new listwise Learning-to-rank (LTR) model called Inversion Transformer-based Neural Ranking (ITNR). ITNR utilizes genomic features and a transformer architecture to decipher functional relationships and construct models that can predict patient-specific drug responses. Our experiments were conducted on three major drug response data sets, showing that ITNR reliably and consistently outperforms state-of-the-art LTR models.HighlightsThe proposed framework is a transformer-based model to predict drug responses using RNAseq gene expression profile, drug descriptors and drug fingerprints.ITNR utilizes a Context-Aware-Transformer architecture as its scoring function that ensures the modeling of inter-item dependencies.We introduced a novel loss function using the concept of Inversion and Approximate Permutation matrices.Our computational results indicated that our method leads to substantially improved performance when compared to the baseline methods across all performance metrics, which can lead to selecting highly effective personalized treatment.