Pawar, Shrikant and Mittal, Karuna and Chandrajit, Lahiri * (2022) Evaluating Performance of Regression and Classification Models Using Known Lung Carcinomas Prognostic Markers. In: Bioinformatics and Biomedical Engineering. Lecture Notes in Computer Science, Part 2 (13347). Springer Cham, Berlin, pp. 413-418. ISBN 9783031078026
Full text not available from this repository.Abstract
Differential expression study between tumor and non-tumor cells aids lung cancer diagnostic classifications and prognostic prediction at various stages. Support vector machine (SVM) learning is used to categorize the morphology of lung cancer. Logistic regression, random forest, and group lasso-based models are used to model dichotomous outcome variables. The purpose is to take groups of observations and design boundaries to forecast which group future observations belong to base measurements. The performance of these selected regression and classification models using lung cancer prognostic indicators is evaluated in this article. The presented results might guide for further regularizations in classification techniques using known lung carcinoma marker genes.
| Item Type: | Book Section |
|---|---|
| Additional Information: | 9th International Work-Conference, IWBBIO 2022, Maspalomas, Gran Canaria, Spain, June 27–30, 2022, Proceedings, Part II |
| Uncontrolled Keywords: | tumor; differential expression; cancer; support vector machine; |
| Subjects: | R Medicine > RC Internal medicine |
| Divisions: | Others > Non Sunway Academics Sunway University > School of Medical and Life Sciences [formerly School of Healthcare and Medical Sciences until 2020] > Dept. Biological Sciences |
| Depositing User: | Ms Yong Yee Chan |
| Date Deposited: | 06 Aug 2024 00:31 |
| Last Modified: | 06 Aug 2024 00:31 |
| URI: | http://eprints.sunway.edu.my/id/eprint/2995 |
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