  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.
