Version 1
: Received: 6 January 2019 / Approved: 8 January 2019 / Online: 8 January 2019 (11:46:34 CET)
How to cite:
Iqbal, N.; Kumar, P. A Framework for the RNA-Seq Based Classification and Prediction of Disease. Preprints2019, 2019010068. https://doi.org/10.20944/preprints201901.0068.v1
Iqbal, N.; Kumar, P. A Framework for the RNA-Seq Based Classification and Prediction of Disease. Preprints 2019, 2019010068. https://doi.org/10.20944/preprints201901.0068.v1
Iqbal, N.; Kumar, P. A Framework for the RNA-Seq Based Classification and Prediction of Disease. Preprints2019, 2019010068. https://doi.org/10.20944/preprints201901.0068.v1
APA Style
Iqbal, N., & Kumar, P. (2019). A Framework for the RNA-Seq Based Classification and Prediction of Disease. Preprints. https://doi.org/10.20944/preprints201901.0068.v1
Chicago/Turabian Style
Iqbal, N. and Pradeep Kumar. 2019 "A Framework for the RNA-Seq Based Classification and Prediction of Disease" Preprints. https://doi.org/10.20944/preprints201901.0068.v1
Abstract
Disease classification based on biological data is an important area in bioinformatics and biomedical research. It helps the doctors and medical practitioners for the early detection of disease and support them as a computer-aided diagnostic tool for accurate diagnosis, prognosis, and treatment of disease. Earlier Microarray gene expression data have wide application for the classification of disease, but now Next-generation sequencing (NGS) has replaced the Microarray technology. From the last few years, RNA sequence (RNA-Seq) data are widely used for the transcriptomic analysis. Hence, RNA-Seq based classification of disease is in its infancy. In this article, we present a general framework for the classification of disease constructed on RNA-Seq data. This framework will guide the researchers to process RNA-Seq, extract relevant features and apply the appropriate classifier to classify any kind of disease.
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.