Researchers developed binary and multiclass machine learning models to distinguish cancer from non-cancerous tissue samples.
By Pooja Toshniwal PahariaReviewed by Danielle Ellis, B.Sc.Jun 24 2024 In a recent study published in Biology Methods and Protocols, researchers developed binary and multiclass machine learning models to distinguish cancer from non-cancerous tissue samples.
About the study In the present study, researchers used machine learning and microarray-based methylation analysis to categorize 13 cancer types and their associated normal tissues. While preprocessing the datasets, the researchers analyzed unmethylated and methylated counts with TCGA data features to derive beta values. They used binary and multiple-class machine-learning models to distinguish between cancerous and normal tissues. Every binary model evaluated a single tissue type, identifying cancer from non-cancers, whereas multiclass models used all 13 types of tissues and non-cancer data.
They created pan-cancer methylome models combining Molecular Mechanisms of Cancer pathways with Pathways in Cancer from the Ingenuity Route Analysis and the Kyoto Encyclopedia of Genes and Genomes databases. They denoted multiclass methylation characteristics linked with genes as blue-colored nodes or in purple in case they were listed as cancer genes in OncoKB or the Cosmic Cancer Gene Census.
The multiclass classification approach performed better than the binary categorization of DNA methylation in individual tumors and normal tissues. The multiclass logistic regression model achieved an average Mathews correlation coefficient score of 0.96; however, its efficacy varies by cancer type.
Carcinogenesis Cell DNA DNA Methylation Gene Genes Immune System Machine Learning Microarray Research Technology Toxins
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