DeepVir - Graphical Deep Matrix Factorization for In Silico Antiviral Repositioning: Application to COVID-19
| Autor: | Stuti Jain, Emilie Chouzenoux, Angshul Majumdar, Aanchal Mongia |
|---|---|
| Přispěvatelé: | Indraprastha Institute of Information Technology [New Delhi] (IIIT-Delhi), OPtimisation Imagerie et Santé (OPIS), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-CentraleSupélec-Université Paris-Saclay |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning 0303 health sciences Matrix completion Computer science In silico Machine Learning (stat.ML) 020206 networking & telecommunications 02 engineering and technology Regularization (mathematics) Machine Learning (cs.LG) [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 3. Good health Matrix decomposition 03 medical and health sciences Matrix (mathematics) Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) Laplacian matrix [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing Algorithm 030304 developmental biology |
| Zdroj: | Journal of Computational Biology Journal of Computational Biology, 2022, 29 (5), pp.441-452. ⟨10.36227/techrxiv.12987497.v1⟩ |
| ISSN: | 1066-5277 1557-8666 |
| DOI: | 10.36227/techrxiv.12987497.v1 |
| Popis: | This work formulates antiviral repositioning as a matrix completion problem where the antiviral drugs are along the rows and the viruses along the columns. The input matrix is partially filled, with ones in positions where the antiviral has been known to be effective against a virus. The curated metadata for antivirals (chemical structure and pathways) and viruses (genomic structure and symptoms) is encoded into our matrix completion framework as graph Laplacian regularization. We then frame the resulting multiple graph regularized matrix completion problem as deep matrix factorization. This is solved by using a novel optimization method called HyPALM (Hybrid Proximal Alternating Linearized Minimization). Results on our curated RNA drug virus association (DVA) dataset shows that the proposed approach excels over state-of-the-art graph regularized matrix completion techniques. When applied to in silico prediction of antivirals for COVID-19, our approach returns antivirals that are either used for treating patients or are under for trials for the same. |
| Databáze: | OpenAIRE |
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