Gene Signatures Research Involved in Cancer Using Machine Learning

Clicks: 259
ID: 114446
2019
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Abstract
With the cheapening of mass sequencing techniques and the rise of computer technologies, capable of analyzing a huge amount of data, it is necessary nowadays that both branches mutually benefit. Transcriptomics, in this case, is a branch of biology focused on the study of mRNA molecules, among others. The quantification of these molecules gives us information about the expression that a gene is having at a given moment. Having information on the expression of the approximately 20,000 genes harbored by human beings is a really useful source of information for the study of certain conditions and/or pathologies. In this work, patient expression -omic data data have been used to offer a new analysis methodology through Machine Learning. The results of this methodology were compared with a conventional methodology to observe how they differed and how they resembled each other. These techniques, therefore, offer a new mechanism for the search of genetic signatures involved, in this case, with cancer.
Reference Key
liñares-blanco2019proceedingsgene Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jose Liñares-Blanco;Carlos Fernandez-Lozano;Liñares-Blanco, Jose;Fernandez-Lozano, Carlos;
Journal proceedings
Year 2019
DOI
10.3390/proceedings2019021019
URL
https://doi.org/10.3390/proceedings2019021019
Keywords
Cancer Machine learning transcriptomics rna-seq tcga

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