Significant directed walk framework to increase the accuracy of cancer classification using gene expression data

Author:

Seah Choon Sen,Kasim Shahreen,Md. Fudzee Mohd Farhan,Hassan Rohayanti

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Reference64 articles.

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3. Bhattacharjee A, Vishwakarma GK (2019) Time-course data prediction for repeatedly measured gene expression. Int J Biomath 12(04):1950033

4. Buraczewski D, Dyszewski P (2018) Precise large deviations for random walk in random environment. Electron J Prob 23(114):1–26. https://doi.org/10.1214/18-EJP239

5. Campos G, Pataki A, Pérez P (2013) The BGLR (Bayesian Generalized Linear Regression) R-Package [Internet]. Bglr.r-forge.r-project.org. https://bglr.r-forge.r-project.org/BGLR-tutorial.pdf. Accessed 4 Sept 2018

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2. Coyote–Wolf optimization-based deep neural network for cancer classification using gene expression profiles;International Journal of Modeling, Simulation, and Scientific Computing;2022-04-12

3. Improved local fisher discriminant analysis based dimensionality reduction for cancer disease prediction;Journal of Ambient Intelligence and Humanized Computing;2020-09-17

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