Measuring Comparative Statistical Effectiveness of Cancer Subtype Categorization Using Gene Expression Data

• Published in: Computer assisted methods in engineering and science Vol. 31; no. 2
• Main Authors: Avila Clemenshia P., Deepa C.
• Format: Journal Article
• Language: English
• Published: Institute of Fundamental Technological Research Polish Academy of Sciences 01.06.2024
• Subjects: cancer subtypes; Deep Flexible Neural Forest (DFN Forest) strategy; gene expression data; machine learing
• ISSN: 2299-3649; 2956-5839
• DOI: 10.24423/cames.2024.555

This work focused on the analysis of various gene expression-based cancer subtype classification approaches. Correctly classifying cancer subtypes is critical for understanding cancer pathophysiology and effectively treating cancer patients by using gene expression data to categorize cancer subtypes. When dealing with limited samples and high-dimensional biological data, most classifiers may suffer from overfitting and lower precision. The goal of this research is to develop a machine learning (ML) system capable of classifying human cancer subtypes based on gene expression data in cancer cells. These issues can be solved using ML algorithms such as Transductive Support Vector Machines (TSVM), Boosting Cascade Deep Forest (BCD Forest), Enhanced Neural Network Classifier (ENNC), Deep Flexible Neural Forest (DFN Forest), Convolutional Neural Network (CNN), and Cascade Flexible Neural Forest (CFN Forest). In inferring the benefits and rawbacks of these strategies, such as DFN Forest and CFN Forest, the findings are 95%.