Breast Cancer Microarray and RNASeq Data Integration Applied to Classification

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 v...

Full description

Saved in:
Bibliographic Details
Published inAdvances in Computational Intelligence pp. 123 - 131
Main Authors Castillo, Daniel, Galvez, Juan Manuel, Herrera, Luis Javier, Rojas, Ignacio
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Bioinformatics
Classification
Computational biology
Feature selection
Integration
Microarray
RNASeq
SVM
Online AccessGet full text

Cover

More Information
Summary: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.
ISBN:3319591525
9783319591520
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-59153-7_11