Comparison of three high dimensional datasets for cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L1/2 regularization”

Volume 20 No 7 (2022)

Poonam Ahir, Kalpdrum Passi, Chakresh Kumar Jain

DOI: 10.14704/nq.2022.20.7.NQ33242

Abstract

The survival analysis is of utmost importance in analyzing the patient’s high dimensional clinical data, where the number of observations in the study is generally much less than the number of parameters because there are few experiments and each one includes many gene expressions. Treating high-dimensional data is necessary because the redundant and non-prognostic genes can lead the researchers to incorrect results. Hence, impose the challenge of identifying better analysis techniques

Keywords

L1/2 regularization, survival analysis, ” Cancer, AML, Cox proportional

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