Classification of Cancer Primary Sites Using Machine Learning and Somatic Mutations
Joint Authors
Chen, Yukun
Huang, Liang-Chin
Sun, Jingchun
Zhao, Zhongming
Xu, Hua
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-11
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
An accurate classification of human cancer, including its primary site, is important for better understanding of cancer and effective therapeutic strategies development.
The available big data of somatic mutations provides us a great opportunity to investigate cancer classification using machine learning.
Here, we explored the patterns of 1,760,846 somatic mutations identified from 230,255 cancer patients along with gene function information using support vector machine.
Specifically, we performed a multiclass classification experiment over the 17 tumor sites using the gene symbol, somatic mutation, chromosome, and gene functional pathway as predictors for 6,751 subjects.
The performance of the baseline using only gene features is 0.57 in accuracy.
It was improved to 0.62 when adding the information of mutation and chromosome.
Among the predictable primary tumor sites, the prediction of five primary sites (large intestine, liver, skin, pancreas, and lung) could achieve the performance with more than 0.70 in F-measure.
The model of the large intestine ranked the first with 0.87 in F-measure.
The results demonstrate that the somatic mutation information is useful for prediction of primary tumor sites with machine learning modeling.
To our knowledge, this study is the first investigation of the primary sites classification using machine learning and somatic mutation data.
American Psychological Association (APA)
Chen, Yukun& Sun, Jingchun& Huang, Liang-Chin& Xu, Hua& Zhao, Zhongming. 2015. Classification of Cancer Primary Sites Using Machine Learning and Somatic Mutations. BioMed Research International،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1055683
Modern Language Association (MLA)
Chen, Yukun…[et al.]. Classification of Cancer Primary Sites Using Machine Learning and Somatic Mutations. BioMed Research International No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1055683
American Medical Association (AMA)
Chen, Yukun& Sun, Jingchun& Huang, Liang-Chin& Xu, Hua& Zhao, Zhongming. Classification of Cancer Primary Sites Using Machine Learning and Somatic Mutations. BioMed Research International. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1055683
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1055683