Paper Info
Title
Computational Prediction Of Driver Missense Mutations In Melanoma
Abstract
Discovering driver mutations used as the diagnostic and prognostic biomarkers is important for the treatment of cancer, including melanoma. Although during the last decade several computational methods have been developed to predict the effect of missense mutations in cancer, only a few have been specifically designed for identifying driver mutations in a specific disease context. To take into consideration of disease-specific factor, here we made efforts to prioritize missense mutations presented in melanoma. We collected 385 pathogenic mutations from the database of curated mutations (DoCM), and 392 benign mutations filtered from a benchmark neutral database (VariSnp), respectively. To evaluation of the model effect, we also selected 45 mutations from other databases. Then a random forest classifier was constructed to prioritize melanoma pathogenic mutations based on conservation, functional region annotation, protein secondary structure, protein domain, physicochemical features, and splicing information. The proposed method achieved an AUC of 0.94 on both training and test sets. When compared with previous developed algorithms, our method obtained a higher accuracy in identifying driver missense mutations in melanoma, along with a more balanced sensitivity and specificity than the other prediction methods.
Year
DOI
Venue
2018
10.1007/978-3-319-95933-7_53
INTELLIGENT COMPUTING THEORIES AND APPLICATION, PT II
Keywords
Field
DocType
Melanoma, Missense mutation, Pathogenicity prediction
Missense mutation,Protein domain,Computer science,Biomarker (medicine),Artificial intelligence,RNA splicing,Melanoma,Computational biology,Random forest,Cancer,Machine learning
Conference
Volume
ISSN
Citations 
10955
0302-9743
0
PageRank 
References 
Authors
0.34
2
6
Name
Order
Citations
PageRank
Haiyang Sun1188.18
Zhenyu Yue202.70
Le Zhao313.05
Junfeng Xia414420.14
Yannan Bin512.72
Di Zhang622.75