Paper Info
Title
Integration and interplay of machine learning and bioinformatics approach to identify genetic interaction related to ovarian cancer chemoresistance
Abstract
Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. This paper investigates the genetic interaction (GI) related to OCa-CR. To decrease the complexity of establishing gene networks, individual signature genes related to OCa-CR are identified using a gradient boosting decision tree algorithm. Additionally, the genetic interaction coefficient (GIC) is proposed to measure the correlation of two signature genes quantitatively and explain their joint influence on OCa-CR. Gene pair that possesses high GIC is identified as signature pair. A total of 24 signature gene pairs are selected that include 10 individual signature genes and the influence of signature gene pairs on OCa-CR is explored. Finally, a signature gene pair-based prediction of OCa-CR is identified. The area under curve (AUC) is a widely used performance measure for machine learning prediction. The AUC of signature gene pair reaches 0.9658, whereas the AUC of individual signature gene-based prediction is 0.6823 only. The identified signature gene pairs not only build an efficient GI network of OCa-CR but also provide an interesting way for OCa-CR prediction. This improvement shows that our proposed method is a useful tool to investigate GI related to OCa-CR.
Year
DOI
Venue
2021
10.1093/bib/bbab100
BRIEFINGS IN BIOINFORMATICS
Keywords
DocType
Volume
chemoresistance, ovarian cancer, gene pair, genetic interaction
Journal
22
Issue
ISSN
Citations 
6
1467-5463
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Kexin Chen100.34
Haoming Xu2112.65
Yiming Lei301.01
Pietro Lio46316.20
Yuan Li500.34
Hongyan Guo600.34
Mohammad Ali Moni74116.64