Paper Info
Title
Computational methods, databases and tools for synthetic lethality prediction
Abstract
Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.
Year
DOI
Venue
2022
10.1093/bib/bbac106
BRIEFINGS IN BIOINFORMATICS
Keywords
DocType
Volume
synthetic lethality, computational methods, deep learning, machine learning
Journal
23
Issue
ISSN
Citations 
3
1467-5463
0
PageRank 
References 
Authors
0.34
0
15
Name
Order
Citations
PageRank
Jing Wang12823.94
Qinglong Zhang200.34
Junshan Han300.34
Yanpeng Zhao400.34
Caiyun Zhao500.34
Bo-Wei Yan601.69
Chong Dai700.34
Lian-Lian Wu802.03
Yuqi Wen903.72
Yixin Zhang1000.34
Dongjin Leng1100.34
Zhongming Wang1200.68
Xiao-Xi Yang1301.69
Song He1400.34
Xiaochen Bo1528523.72