We present a novel framework for disease classification from high-dimensional gene expression data or from several characteristic of patients. We take advantage of Principle Component Analysis to perform dimensionality reduction and heatmaps for embedding the complex information in a 2-D image, and we make use of a convolutional neural network to make classification of different tumor types. Experimental analyses show that the proposed method achieves good performance, and encourages its application to other genomic data or pathological context.

Using Heatmaps for Deep Learning based Disease Classification

Bruno P.
;
Calimeri F.
2019

Abstract

We present a novel framework for disease classification from high-dimensional gene expression data or from several characteristic of patients. We take advantage of Principle Component Analysis to perform dimensionality reduction and heatmaps for embedding the complex information in a 2-D image, and we make use of a convolutional neural network to make classification of different tumor types. Experimental analyses show that the proposed method achieves good performance, and encourages its application to other genomic data or pathological context.
978-1-7281-1462-0
Classification; Convolutional Neural Networks; Gene expression; Heatmap
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/299161
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