SL 2 MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization.

Authors:
Yong Liu
Yong Liu
Nanjing Drum Tower Hospital
Nanjing Shi | China
Min Wu
Min Wu
University of North Dakota
Grand Forks | United States
Chenghao Liu
Chenghao Liu
the First Affiliated Hospital of Nanjing Medical University
Xiaoli Li
Xiaoli Li
Yanshan University
China
Jie Zheng
Jie Zheng
The University of Akron
United States

IEEE/ACM Trans Comput Biol Bioinform 2019 Apr 9. Epub 2019 Apr 9.

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 SL 2 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 SL 2 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 SL 2 MF.

Download full-text PDF

Source
http://dx.doi.org/10.1109/TCBB.2019.2909908DOI Listing
April 2019

Publication Analysis

Top Keywords

logistic matrix
8
unknown pairs
8
synthetic lethality
8
matrix factorization
8
pairs
4
latent vectors
4
combination gene
4
modeled linear
4
linear combination
4
gene latent
4
pairs trustworthy
4
weighting schemes
4
schemes assign
4
assign higher
4
design weighting
4
pairs design
4
form modeled
4
trustworthy unknown
4
vectors pairs
4
data probability
4

Similar Publications