Publication Type
Journal Article
Book Title/Conference/Journal
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Year
5-2020
Abstract
Synthetic lethality (SL) is a promising concept for novel discovery of anti-cancer drug targets. However, wet-lab experiments for detecting SLs are faced with various challenges, such as high cost, low consistency across platforms, or cell lines. Therefore, computational prediction methods are needed to address these issues. This paper proposes a novel SL prediction method, named $\mathsf{SL}^2 \mathsf{MF}$, which employs logistic matrix factorization to learn latent representations of genes from the observed SL data. The probability that two genes are likely to form SL is modeled by the linear combination of gene latent vectors. As known SL pairs are more trustworthy than unknown pairs, we design importance weighting schemes to assign higher importance weights for known SL pairs and lower importance weights for unknown pairs in $\mathsf{SL}^2 \mathsf{MF}$. Moreover, we also incorporate biological knowledge about genes from protein-protein interaction (PPI) data and Gene Ontology (GO). In particular, we calculate the similarity between genes based on their GO annotations and topological properties in the PPI network. Extensive experiments on the SL interaction data from SynLethDB database have been conducted to demonstrate the effectiveness of $\mathsf{SL}^2 \mathsf{MF}$.
Keywords
Synthetic lethality, machine learning, logistic matrix factorization, importance weighting, human cancers
Disciplines
Computer Sciences
Subject(s)
Applied or Integration/Application Scholarship
ISSN/ISBN
1545-5963
DOI
10.1109/TCBB.2019.2909908
Version
publishedVersion
Language
eng
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Format
application/PDF
Citation
LIU, Yong; WU, Min; LIU, Chenghao; LI, Xiao-Li; and ZHENG, Jie.
SL2MF: Predicting synthetic lethality in human cancers via logistic matrix factorization. (2020). IEEE/ACM Transactions on Computational Biology and Bioinformatics. 17, (3), 748-757.
Available at: https://ink.library.smu.edu.sg/studentpub/14
Additional URL
https://doi.org/10.1109/TCBB.2019.2909908
