Analysis of Breast Cancer Subtypes Prediction Based on Alternative Splicing Disorders

Peng XU; Bing WANG; Gang FANG; Xiaolong SHI; Wenbin LIU

doi:10.11999/JEIT190871

Volume 42 Issue 6

Jun. 2020

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Peng XU, Bing WANG, Gang FANG, Xiaolong SHI, Wenbin LIU. Analysis of Breast Cancer Subtypes Prediction Based on Alternative Splicing Disorders[J]. Journal of Electronics & Information Technology, 2020, 42(6): 1348-1354. doi: 10.11999/JEIT190871

Citation:

Peng XU, Bing WANG, Gang FANG, Xiaolong SHI, Wenbin LIU. Analysis of Breast Cancer Subtypes Prediction Based on Alternative Splicing Disorders[J]. Journal of Electronics & Information Technology, 2020, 42(6): 1348-1354. doi: 10.11999/JEIT190871

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Analysis of Breast Cancer Subtypes Prediction Based on Alternative Splicing Disorders

doi: 10.11999/JEIT190871

Peng XU^{1, 3},
Bing WANG²,
Gang FANG¹,
Xiaolong SHI¹,
Wenbin LIU^{1, 2
,
,}

1.
Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China
2.
College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
3.
School of Computer Science and Information Technology, Qiannan Normal University for Nationalities, Duyun 558000, China

Funds: The National Key R&D Program of China (2019YFA0706402), The National Natural Science Foundation of China (61572367, 61573017, 61972107, 61972109)

Received Date: 2019-11-01
Rev Recd Date: 2020-05-10

Available Online: 2020-05-23

Publish Date: 2020-06-22

Abstract

Abstract

Alternative splicing is closely related to the occurrence and development of a variety of complex diseases, the emergence of various diseases including tumors is often accompanied by the occurrence of alternative splicing disorders. The existing analysis of breast cancer subtypes is mainly based on single splicing isoform, and the difference in the overall distribution of splicing isoforms caused by alternative splicing disorders among subtypes is not considered. Therefore, a prediction method of breast cancer subtypes based on alternative splicing disorders is proposed, which mainly uses Jensen-Shannon(JS) divergence to find genes with large differences in alternative splicing disorders between subtypes, then constructes Back Propagation(BP) neural network model to classify breast cancer subtypes. The results show that this method could not only effectively detect tumor heterogeneous molecules, but also had good identification results in the classification of breast cancer subtypes, with an average F1-score of 0.89, and could provide personalized drug recommendations for patients with breast cancer subtypes. This study will effectively promote the development of breast cancer subtypes based on alternative splicing disorders.
- Breast cancer subtypes predicion,
- Alternative splicing,
- Jensen-Shannon(JS) divergence,
- Back Propagation(BP) neural network,
- Drug recommendation

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References(21)

References

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