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A Parallel Swarm Library Based on Functional Programming Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Breast Cancer Microarray and RNASeq Data Integration Applied to Classification

Authors : Daniel Castillo, Juan Manuel Galvez, Luis Javier Herrera, Ignacio Rojas

Published in: Advances in Computational Intelligence

Publisher: Springer International Publishing

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Abstract

Although Next-Generation Sequencing (NGS) has more impact nowadays than microarray sequencing, there is a huge volume of microarray data that has not still been processed. The last represents the most important source of biological information nowadays due largely to its use over many years, and a very important potential source of genetic knowledge deserving appropriate analysis. Thanks to the two techniques, there is now a huge amount of data that allows us to obtain robust results from its integration. This paper deals with the integration of RNASeq data with microarrays data in order to find breast cancer biomarkers as expressed genes. These integrated data has been used to create a classifier for an early diagnosis of breast cancer.

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previous chapter A Novel Technique to Estimate Biological Parameters in an Epidemiology Problem
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Metadata
Title
Breast Cancer Microarray and RNASeq Data Integration Applied to Classification
Authors
Daniel Castillo
Juan Manuel Galvez
Luis Javier Herrera
Ignacio Rojas
Copyright Year
2017
Book
Advances in Computational Intelligence

Print ISBN: 978-3-319-59152-0

Electronic ISBN: 978-3-319-59153-7

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59153-7_11

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