A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT GENE IDENTIFICATION

Cancer detection from gene expression data continues to pose a challenge due to the high dimensionality and complexity of these data. After decades of research there is still uncertainty in the clinical diagnosis of cancer and the identification of tumor-specific markers. Here we present a deep lear...

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Vydané v:Biocomputing 2017 Ročník 22; s. 219 - 229
Hlavní autori: DANAEE, PADIDEH, GHAEINI, REZA, HENDRIX, DAVID A.
Médium: Kapitola Journal Article
Jazyk:English
Vydavateľské údaje: United States WORLD SCIENTIFIC 01.01.2017
Predmet:
Algorithms
Biomarkers, Tumor - genetics
Breast Neoplasms - classification
Breast Neoplasms - diagnosis
Breast Neoplasms - genetics
Computational Biology
Databases, Genetic - statistics & numerical data
Female
Gene Expression Profiling - statistics & numerical data
Gene Ontology
Humans
PATTERNS IN BIOMEDICAL DATA-HOW DO WE FIND THEM?
Principal Component Analysis
Supervised Machine Learning
Stacked Denoising Autoencoder
Dimensionality Reduction
Cancer Detection
RNA-seq Expression
Deep Learning
Classification
ISBN:9789813207820, 9789813207806, 9813207825, 9813207809, 9789813207813, 9813207817
ISSN:2335-6936
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Shrnutí:Cancer detection from gene expression data continues to pose a challenge due to the high dimensionality and complexity of these data. After decades of research there is still uncertainty in the clinical diagnosis of cancer and the identification of tumor-specific markers. Here we present a deep learning approach to cancer detection, and to the identification of genes critical for the diagnosis of breast cancer. First, we used Stacked Denoising Autoencoder (SDAE) to deeply extract functional features from high dimensional gene expression profiles. Next, we evaluated the performance of the extracted representation through supervised classification models to verify the usefulness of the new features in cancer detection. Lastly, we identified a set of highly interactive genes by analyzing the SDAE connectivity matrices. Our results and analysis illustrate that these highly interactive genes could be useful cancer biomarkers for the detection of breast cancer that deserve further studies.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISBN:9789813207820
9789813207806
9813207825
9813207809
9789813207813
9813207817
ISSN:2335-6936
DOI:10.1142/9789813207813_0022