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SL2MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization


Authors: Y. Liu, M. Wu, C. Liu, X.-L. Li, and J. Zheng
Title: SL2MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization
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 SL2MF, 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 SL2MF. 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 SL2MF.
Keywords: Synthetic lethality; Machine learning; Logistic matrix factorization; Importance weighting; Human cancers
Journal Name: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Publisher: IEEE
Year: 2019
Accepted PDF File: SL2MF_Predicting_Synthetic_Lethality_in_Human_Cancers_via_Logistic_Matrix_Factorization_accepted.pdf
Permanent Link: https://doi.org/10.1109/TCBB.2019.2909908
Reference: Y. Liu, M. Wu, C. Liu, X.-L. Li, and J. Zheng, "SL2MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization," IEEE/ACM Transactions on Computational Biology and Bioinformatics, pp. 1-10, April 2019.
bibtex: 
@article {LILY-j64,
   author 	= {Liu, Yong and Wu, Min and Liu, Chenghao and Li, Xiao-Li and Zheng, Jie},
   title 	= {SL^{2}MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization},
   journal 	= {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
   year 	= {2019},
   month 	= {April},
   volume 	= {},
   number 	= {},
   pages 	= {1-10},
   publisher 	= {IEEE},
}