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/Territory	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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICBBE.2009.5162409

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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title = "Artificial neural network prediction for cancer survival time by gene expression data",

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.",

keywords = "Artifical Nerual Network, Microarray, Survival time prediction",

author = "Chen, {Yen Chen} and Yang, {Wen Wen} and Chiu, {Hung Wen}",

year = "2009",

doi = "10.1109/ICBBE.2009.5162409",

language = "English",

isbn = "9781424429028",

booktitle = "3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009",

note = "3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009 ; Conference date: 11-06-2009 Through 13-06-2009",

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TY - GEN

T1 - Artificial neural network prediction for cancer survival time by gene expression data

AU - Chen, Yen Chen

AU - Yang, Wen Wen

AU - Chiu, Hung Wen

PY - 2009

Y1 - 2009

N2 - 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.

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.

KW - Artifical Nerual Network

KW - Microarray

KW - Survival time prediction

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U2 - 10.1109/ICBBE.2009.5162409

DO - 10.1109/ICBBE.2009.5162409

M3 - Conference contribution

AN - SCOPUS:72749096058

SN - 9781424429028

BT - 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009

T2 - 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009

Y2 - 11 June 2009 through 13 June 2009

ER -

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

Abstract

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Keywords

ASJC Scopus subject areas

UN SDGs

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