Academic Journal
ITNR: Inversion Transformer-based Neural Ranking for cancer drug recommendations.
| Τίτλος: | ITNR: Inversion Transformer-based Neural Ranking for cancer drug recommendations. |
|---|---|
| Συγγραφείς: | Sotudian S; Department of Electrical and Computer Engineering, Division of Systems Engineering, Boston University, Boston, MA, USA. Electronic address: sotudian@bu.edu., Paschalidis IC; Department of Electrical and Computer Engineering, Division of Systems Engineering, Boston University, Boston, MA, USA; Department of Biomedical Engineering, and Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA. Electronic address: yannisp@bu.edu. |
| Πηγή: | Computers in biology and medicine [Comput Biol Med] 2024 Apr; Vol. 172, pp. 108312. Date of Electronic Publication: 2024 Mar 16. |
| Τύπος έκδοσης: | Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, Non-U.S. Gov't; Research Support, N.I.H., Extramural |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE |
| Imprint Name(s): | Publication: New York : Elsevier Original Publication: New York, Pergamon Press. |
| Ιατρικοί όροι (MeSH): | Antineoplastic Agents*/pharmacology , Antineoplastic Agents*/therapeutic use , Neoplasms*/drug therapy , Neoplasms*/genetics, Humans ; Cell Line ; Genomics ; Machine Learning |
| Περίληψη: | Personalized drug response prediction is an approach for tailoring effective therapeutic strategies for patients based on their tumors' genomic characterization. While machine learning methods are widely employed in the literature, they often struggle to capture drug-cell line relations across various cell lines. In addressing this challenge, our study introduces a novel listwise Learning-to-Rank (LTR) model named 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. (Copyright © 2024 Elsevier Ltd. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
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| Grant Information: | R01 GM093147 United States GM NIGMS NIH HHS; R01 GM135930 United States GM NIGMS NIH HHS; UL1 TR001430 United States TR NCATS NIH HHS |
| Contributed Indexing: | Keywords: Approximate permutation; Drug response prediction; Inversion; Learning-to-rank; Transformers |
| Substance Nomenclature: | 0 (Antineoplastic Agents) |
| Entry Date(s): | Date Created: 20240319 Date Completed: 20240326 Latest Revision: 20250402 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC10990436 |
| DOI: | 10.1016/j.compbiomed.2024.108312 |
| PMID: | 38503090 |
| Βάση Δεδομένων: | MEDLINE |
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