Prediction of Cancer Microarray and DNA Methylation Data using Non-negative Matrix Factorization
This work addresses the challenge of analyzing complex biological datasets for cancer research, but it is incremental as it applies an existing method to new data.
The study tackled the problem of high dimensionality in cancer microarray and DNA methylation data by applying Non-negative Matrix Factorization for dimensionality reduction, achieving a classification accuracy of 98%.
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%.