Yifei Cai - Augmenting LASSO regression with decision tree for

Conference

Yi-fei Cai, Zhao-hui Liang, Tong He, Gang Zhang, Jimmy Xiangji Huang, Xing Zeng
2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE, 2013, pp. 355-360

https://ieeexplore.ieee.org/abstract/document...

Cite

APA Click to copy
Cai, Y.-fei, Liang, Z.-hui, He, T., Zhang, G., Huang, J. X., & Zeng, X. (2013). Augmenting LASSO regression with decision tree for identifying the correlation of genetic polymorphism and adverse events (pp. 355–360). IEEE.

Chicago/Turabian Click to copy
Cai, Yi-fei, Zhao-hui Liang, Tong He, Gang Zhang, Jimmy Xiangji Huang, and Xing Zeng. “Augmenting LASSO Regression with Decision Tree for Identifying the Correlation of Genetic Polymorphism and Adverse Events.” In , 355–360. 2013 IEEE International Conference on Bioinformatics and Biomedicine. IEEE, 2013.

MLA Click to copy
Cai, Yi-fei, et al. Augmenting LASSO Regression with Decision Tree for Identifying the Correlation of Genetic Polymorphism and Adverse Events. IEEE, 2013, pp. 355–60.

BibTeX Click to copy

@conference{yi-fei2013a,
  title = {Augmenting LASSO regression with decision tree for identifying the correlation of genetic polymorphism and adverse events},
  year = {2013},
  pages = { 355-360},
  publisher = {IEEE},
  series = {2013 IEEE International Conference on Bioinformatics and Biomedicine},
  author = {Cai, Yi-fei and Liang, Zhao-hui and He, Tong and Zhang, Gang and Huang, Jimmy Xiangji and Zeng, Xing}
}

A novel algorithm that combines LASSO regression and decision tree is proposed to explore the correlation of adverse events (AE) and genetic polymorphism of CYP2D6*2, *10, *14, CYP1A2*1C, *1F in human subjects in a clinical trial. The genotypes of 30 healthy human subjects in a clinical trial for a natural herbal drug and 53 subjects in the blank group were detected by polymerase chain reaction (PCR) and DNA sequencing. The AEs occurring during the trial were recorded. The correlations of AE and genetic polymorphism are analyzed by the new combined algorithm. 53 AEs are reported in the end of the study. Five gene subtypes are selected as correlative factors to the specific AEs by the new algorithm: wild type of CYP1A2*1F and abnormal platelet counting, homozygous CYP1A2*1C and abnormal fibrinogen, heterozygous CYP1A2*1C and abnormal blood chlorine, heterozygous CYP1A2*1C and abnormal urobilinogen, wild type of CYP2D6*2 and abnormal APTT (activated partial thromboplastin time). The result indicates the novel algorithm is effective and is able to detect the correlation of AEs and genetic polymorphism in clinical trials.