Prediction of Cancer Microarray and DNA Methylation Data using Non-negative Matrix Factorization

TL;DR

This paper explores the use of Non-negative Matrix Factorization (NMF) to reduce the dimensionality of cancer microarray and DNA methylation data, achieving high classification accuracy and aiding biological data analysis.

Contribution

It introduces NMF-based algorithms for dimensionality reduction in cancer-related microarray datasets, demonstrating their effectiveness in improving classification accuracy.

Findings

Achieved 98% classification accuracy using NMF.

Compared different algorithms for dimensionality reduction.

Validated the effectiveness of NMF in biological data analysis.

Abstract

Over the past few years, there has been a considerable spread of microarray technology in many biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms and approaches for the reduction of dimensionality of such microarray datasets. This study exploits the matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification accuracies are then compared for these algorithms. This technique gives an accuracy of 98%.