Establishment of multiple diagnosis models for colorectal cancer with artificial neural networks

The current study aimed to develop multiple diagnosis models for colorectal cancer (CRC) based on data from The Cancer Genome Atlas database and analysis with artificial neural networks in order to enhance CRC diagnosis methods. A genetic algorithm and mean impact value were used to select genes to...

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Bibliographic Details
Published in:Oncology letters 2019-03, Vol.17 (3), p.3314-3322
Main Authors: Wang, Qiang, Wei, Jianchang, Chen, Zhuanpeng, Zhang, Tong, Zhong, Junbin, Zhong, Bingzheng, Yang, Ping, Li, Wanglin, Cao, Jie
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Antigens
Artificial intelligence
Artificial neural networks
Cancer genetics
Cancer research
Carcinoembryonic antigen
Care and treatment
Colorectal cancer
Criminal investigation
Datasets
Diagnosis
Gene expression
Genes
Genomes
Genomics
Kinases
Medical diagnosis
Medical prognosis
Metastasis
Mortality
Neural networks
Oncology
Proteins
Statistical analysis
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Summary:The current study aimed to develop multiple diagnosis models for colorectal cancer (CRC) based on data from The Cancer Genome Atlas database and analysis with artificial neural networks in order to enhance CRC diagnosis methods. A genetic algorithm and mean impact value were used to select genes to be used as numerical encoded parameters to reflect cancer metastasis or aggression. Back propagation and learning vector quantization neural networks were used to build four diagnosis models: Cancer/Normal, M0/M1, carcinoembryonic antigen (CEA)
ISSN:1792-1074
1792-1082
DOI:10.3892/ol.2019.10010