Artificial neural network prediction for cancer survival time by gene expression data

Yen Chen Chen; Wen Wen Yang; Hung Wen Chiu

doi:10.1109/ICBBE.2009.5162409

Artificial neural network prediction for cancer survival time by gene expression data

Yen Chen Chen, Wen Wen Yang, Hung Wen Chiu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

This study aimed at training artificial neural networks (ANN) to predict survival time in cancer patients by using Microarray and clinical data. We analyzed public Microarrays and clinical data sets in different kinds of cancer. We selected 15-30 genes (correlation coefficient>0.4) as ANN variables to train networks. The results shows ANN can predict survival time from Microarray data gene expression and the prediction made by the proposed neural models show a good agreement with the measurements.

Original language	English
Title of host publication	3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
DOIs	https://doi.org/10.1109/ICBBE.2009.5162409
Publication status	Published - 2009
Event	3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009 - Beijing, China Duration: Jun 11 2009 → Jun 13 2009

Other

Other	3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
Country	China
City	Beijing
Period	6/11/09 → 6/13/09

Keywords

Artifical Nerual Network
Microarray
Survival time prediction

ASJC Scopus subject areas

Biotechnology
Biomedical Engineering

Access to Document

10.1109/ICBBE.2009.5162409

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Cite this

Chen, YC, Yang, WW & Chiu, HW 2009, Artificial neural network prediction for cancer survival time by gene expression data. in 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009., 5162409, 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009, Beijing, China, 6/11/09. https://doi.org/10.1109/ICBBE.2009.5162409

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abstract = "This study aimed at training artificial neural networks (ANN) to predict survival time in cancer patients by using Microarray and clinical data. We analyzed public Microarrays and clinical data sets in different kinds of cancer. We selected 15-30 genes (correlation coefficient>0.4) as ANN variables to train networks. The results shows ANN can predict survival time from Microarray data gene expression and the prediction made by the proposed neural models show a good agreement with the measurements.",

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AB - This study aimed at training artificial neural networks (ANN) to predict survival time in cancer patients by using Microarray and clinical data. We analyzed public Microarrays and clinical data sets in different kinds of cancer. We selected 15-30 genes (correlation coefficient>0.4) as ANN variables to train networks. The results shows ANN can predict survival time from Microarray data gene expression and the prediction made by the proposed neural models show a good agreement with the measurements.

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