A Matrix Factorization-based Drug-virus Link Prediction Method for SARS-CoV-2 Drug Prioritization

TY - CHAP

T1 - A Matrix Factorization-based Drug-virus Link Prediction Method for SARS-CoV-2 Drug Prioritization

AU - Li, Yutong

AU - Xu, Xiaorui

AU - Tsoka, Sophia

N1 - Conference code: 8

PY - 2023/3/9

Y1 - 2023/3/9

N2 - Matrix factorization (MF) has been widely used in drug discovery for link prediction, which aims to reveal new drug-target links by integrating drug-drug and target-target similarity information with a drug-target interaction matrix. The MF method is based on the assumption that similar drugs share similar targets and vice versa. However, one major disadvantage is that only one similarity metric is used in MF models, which is not enough to represent the similarity between drugs or targets. In this work, we develop a similarity fusion enhanced MF model to incorporate different types of similarity for novel drug-target link prediction. We apply the proposed model on a drug-virus association dataset for anti-COVID drug prioritization, and compare the performance with other existing MF models developed for COVID. The results show that the similarity fusion method can provide more useful information for drug-drug and virus-virus similarity and hence improve the performance of MF models. The top 10 drugs as prioritized by our model are provided, together with supporting evidence from literature.

AB - Matrix factorization (MF) has been widely used in drug discovery for link prediction, which aims to reveal new drug-target links by integrating drug-drug and target-target similarity information with a drug-target interaction matrix. The MF method is based on the assumption that similar drugs share similar targets and vice versa. However, one major disadvantage is that only one similarity metric is used in MF models, which is not enough to represent the similarity between drugs or targets. In this work, we develop a similarity fusion enhanced MF model to incorporate different types of similarity for novel drug-target link prediction. We apply the proposed model on a drug-virus association dataset for anti-COVID drug prioritization, and compare the performance with other existing MF models developed for COVID. The results show that the similarity fusion method can provide more useful information for drug-drug and virus-virus similarity and hence improve the performance of MF models. The top 10 drugs as prioritized by our model are provided, together with supporting evidence from literature.

UR - http://www.scopus.com/inward/record.url?scp=85151045474&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-25599-1_4

DO - 10.1007/978-3-031-25599-1_4

M3 - Conference paper

SN - 9783031255984

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 35

EP - 47

BT - Lecture Notes in Computer Science

A2 - Nicosia, Giuseppe

A2 - Giuffrida, Giovanni

A2 - Ojha, Varun

A2 - La Malfa, Emanuele

A2 - La Malfa, Gabriele

A2 - Pardalos, Panos

A2 - Di Fatta, Giuseppe

A2 - Umeton, Renato

T2 - The 8th International Conference on Machine Learning, Optimization, and Data Science

Y2 - 18 September 2022 through 22 September 2022

ER -