Using Naïve Bayes Algorithm to Estimate the Response to Drug in Lung Cancer Patients

Author(s): Baoling Guo, Qiuxiang Zheng*.

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research

Volume 21 , Issue 10 , 2018

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Abstract:

Aim and Objective: Lung cancer is a highly heterogeneous cancer, due to the significant differences in molecular levels, resulting in different clinical manifestations of lung cancer patients there is a big difference. Including disease characterization, drug response, the risk of recurrence, survival, etc.

Method: Clinical patients with lung cancer do not have yet particularly effective treatment options, while patients with lung cancer resistance not only delayed the treatment cycle but also caused strong side effects. Therefore, if we can sum up the abnormalities of functional level from the molecular level, we can scientifically and effectively evaluate the patients' sensitivity to treatment and make the personalized treatment strategies to avoid the side effects caused by over-treatment and improve the prognosis.

Result & Conclusion: According to the different sensitivities of lung cancer patients to drug response, this study screened out genes that were significantly associated with drug resistance. The bayes model was used to assess patient resistance.

Keywords: Naïve bayes, lung cancer, drug sensitivity, functional deviation, drug resistance, treatment strategies.

[1]
McCarthy, J.J.; McLeod, H.L.; Ginsburg, G.S. Genomic medicine: a decade of successes, challenges, and opportunities. Sci. Transl. Med., 2013, 5(189), 189sr4.
[2]
Collisson, E. Comprehensive molecular profiling of lung adenocarcinoma. Nature, 2014, 511(7511), 543-550.
[3]
Hoadley, K.A.; Yau, C.; Wolf, D.M.; Cherniack, A.D.; Tamborero, D.; Ng, S.; Leiserson, M.D.M.; Niu, B.; McLellan, M.D.; Uzunangelov, V.; Zhang, J.; Kandoth, C.; Akbani, R.; Shen, H.; Omberg, L.; Chu, A.; Margolin, A.A.; Van’t Veer, L.J.; Lopez-Bigas, N.; Laird, P.W.; Raphael, B.J.; Ding, L.; Robertson, A.G.; Byers, L.A.; Mills, G.B.; Weinstein, J.N.; Van Waes, C.; Chen, Z.; Collisson, E.A.; Benz, C.C.; Perou, C.M.; Stuart, J.M. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell, 2014, 158(4), 929-944.
[4]
Vayshlya, N.a. Increased expression of BIRC5 in non-small cell lung cancer and esophageal squamous cell carcinoma does not correlate with the expression of its inhibitors SMAC/DIABLO and PML. Mol. Biol., 2008, 42, 579-587.
[5]
Liu, J.; Lee, W.; Jiang, Z.; Chen, Z.; Jhunjhunwala, S.; Haverty, P.M.; Gnad, F.; Guan, Y.; Gilbert, H.N.; Stinson, J.; Klijn, C.; Guillory, J.; Bhatt, D.; Vartanian, S.; Walter, K.; Chan, J.; Holcomb, T.; Dijkgraaf, P.; Johnson, S.; Koeman, J.; Minna, J.D.; Gazdar, A.F.; Stern, H.M.; Hoeflich, K.P.; Wu, T.D.; Settleman, J.; de Sauvage, F.J.; Gentleman, R.C.; Neve, R.M.; Stokoe, D.; Modrusan, Z.; Seshagiri, S.; Shames, D.S.; Zhang, Z. Genome and transcriptome sequencing of lung cancers reveal diverse mutational and splicing events. Genome Res., 2012, 22(12), 2315-2327.
[6]
San Segundo, E.; Tsanas, A.; Gómez-Vilda, P. Euclidean Distances as measures of speaker similarity including identical twin pairs: A forensic investigation using source and filter voice characteristics. Forensic Sci. Int., 2017, 270, 25-38.
[7]
Sõber, S.; Rull, K.; Reiman, M.; Ilisson, P.; Mattila, P.; Laan, M. RNA sequencing of chorionic villi from recurrent pregnancy loss patients reveals impaired function of basic nuclear and cellular machinery. Sci. Rep., 2016, 6, 38439.
[8]
Liao, Y.Y.; Lee, T.S.; Lin, Y.M. A Fisher exact test will be more proper. Radiology, 2006, 239(1), 300-301.
[9]
Nam, H.; Lee, J.; Lee, D. Computational identification of altered metabolism using gene expression and metabolic pathways. Biotechnol. Bioeng., 2009, 103(4), 835-843.
[10]
Ferstl, F.; Kanzler, M.; Rautenhaus, M.; Westermann, R. Time-Hierarchical Clustering and Visualization of Weather Forecast Ensembles. IEEE Trans. Vis. Comput. Graph., 2017, 23(1), 831-840.
[11]
Liu, A.N.; Wang, L.L.; Li, H.P.; Gong, J.; Liu, X.H. Correlation Between Posttraumatic Growth and Posttraumatic Stress Disorder Symptoms Based on Pearson Correlation Coefficient: A Meta-Analysis. J. Nerv. Ment. Dis., 2017, 205(5), 380-389.
[12]
Xie, X.; Yu, Z.L.; Lu, H.; Gu, Z.; Li, Y. Motor Imagery Classification Based on Bilinear Sub-Manifold Learning of Symmetric Positive-Definite Matrices. IEEE Trans. Neural Syst. Rehabil. Eng., 2017, 25(6), 504-516.
[13]
Zhang, R.; Tao, J.; Lu, R.; Jin, Q. Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems. IEEE Trans. Neural Netw. Learn. Syst., 2018, 29(2), 457-469.


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Article Details

VOLUME: 21
ISSUE: 10
Year: 2018
Page: [734 - 748]
Pages: 15
DOI: 10.2174/1386207322666190125151624
Price: $58

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