Geometric classifiers for high-dimensional noisy data

Ishii, Aki; Yata, Kazuyoshi; Aoshima, Makoto

doi:10.1016/j.jmva.2021.104850

Cited by 2 publications

(1 citation statement)

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“…The most crucial aspect of RNA-Seq analyses is differential analysis [12]. Traditional differential analysis techniques often match tumor samples to standard samples of the same tumor kind [13], [14]. However, due to its ignorance of additional tumor forms, such a technology could not distinguish between distinct tumor types [15].…”

Section: Introductionmentioning

confidence: 99%

Analyzing RNA-Seq Gene Expression Data for Cancer Classification Through ML Approach

Wahid,

Banday

2023

IJACSA

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Ribonucleic Acid Sequencing (RNA-Seq) is a technique that allows an efficient genome-wide analysis of gene expressions. Such analysis is a strategy for identifying hidden patterns in data, and those related to cancer-specific biomarkers. Prior analyses without samples of different cancer kinds used RNA-Seq data from the same type of cancer as the positive and negative samples. Therefore, different cancer types must be evaluated to uncover differentially expressed genes and perform multiple cancer classifications. Problem: Since gene expression reflects both the genetic make-up of an organism and the biochemical activities occurring in tissue and cells, it can be crucial in the early identification of cancer. The aim of this study is to classify the RNA-Sequence data into five different cancer forms, such as LUAD, BRCA, KIRC, LUSC, and UCEC, through an ensemble approach of machine learning algorithms. RNA-Seq data for five different cancer types from the UCI Machine Learning Repository are examined in this research. Methods: As a first step, the relevant features of RNA-Seq are extricated using Principal Component Analysis (PCA). Then, the extricated features are given to the ensemble of machine learning classifiers to classify the type of cancer. The ensemble of classifiers is built using Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbor (KNN). Results: The results demonstrated that the proposed ensemble classifier outperformed the existing machine-learning approaches with an accuracy of 99.59%.

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