Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.03.16.533057v1?rss=1
Authors: Sotudian, S., Paschalidis, I. C.
Abstract:
Personalized 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.
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