Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy

Predictive analytics using artificial intelligence is a useful tool in cancer research. A multilayer perceptron neural network used gene expression data to predict the lymphoma subtypes of 290 cases of non-Hodgkin lymphoma (GSE132929). The input layer included both the whole array of 20,863 genes an...
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Gespeichert in:

Autor*in:	Joaquim Carreras [verfasserIn] Rifat Hamoudi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	non-Hodgkin lymphoma follicular lymphoma mantle cell lymphoma diffuse large B-cell lymphoma Burkitt lymphoma marginal zone lymphoma

Übergeordnetes Werk:	In: Machine Learning and Knowledge Extraction - MDPI AG, 2020, 3(2021), 3, Seite 720-739
Übergeordnetes Werk:	volume:3 ; year:2021 ; number:3 ; pages:720-739

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/make3030036

Katalog-ID:	DOAJ056663919

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520			\|a Predictive analytics using artificial intelligence is a useful tool in cancer research. A multilayer perceptron neural network used gene expression data to predict the lymphoma subtypes of 290 cases of non-Hodgkin lymphoma (GSE132929). The input layer included both the whole array of 20,863 genes and a cancer transcriptome panel of 1769 genes. The output layer was lymphoma subtypes, including follicular lymphoma, mantle cell lymphoma, diffuse large B-cell lymphoma, Burkitt lymphoma, and marginal zone lymphoma. The neural networks successfully classified the cases consistent with the lymphoma subtypes, with an area under the curve (AUC) that ranged from 0.87 to 0.99. The most relevant predictive genes were <i<LCE2B</i<, <i<KNG1</i<, <i<IGHV7_81</i<, <i<TG</i<, <i<C6</i<, <i<FGB</i<, <i<ZNF750</i<, <i<CTSV</i<, <i<INGX</i<, and <i<COL4A6</i< for the whole set; and <i<ARG1</i<, <i<MAGEA3</i<, <i<AKT2</i<, <i<IL1B</i<, <i<S100A7A</i<, <i<CLEC5A</i<, <i<WIF1</i<, <i<TREM1</i<, <i<DEFB1</i<, and <i<GAGE1</i< for the cancer panel. The characteristic predictive genes for each lymphoma subtypes were also identified with high accuracy (AUC = 0.95, incorrect predictions = 6.2%). Finally, the topmost relevant 30 genes of the whole set, which belonged to apoptosis, cell proliferation, metabolism, and antigen presentation pathways, not only predicted the lymphoma subtypes but also the overall survival of diffuse large B-cell lymphoma (series GSE10846, <i<n</i< = 414 cases), and most relevant cancer subtypes of The Cancer Genome Atlas (TCGA) consortium including carcinomas of breast, colorectal, lung, prostate, and gastric, melanoma, etc. (7441 cases). In conclusion, neural networks predicted the non-Hodgkin lymphoma subtypes with high accuracy, and the highlighted genes also predicted the survival of a pan-cancer series.
650		4	\|a non-Hodgkin lymphoma
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allfields	10.3390/make3030036 doi (DE-627)DOAJ056663919 (DE-599)DOAJf1149c6d362d41f9af5c4ef43bb94ced DE-627 ger DE-627 rakwb eng TK7885-7895 Joaquim Carreras verfasserin aut Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Predictive analytics using artificial intelligence is a useful tool in cancer research. A multilayer perceptron neural network used gene expression data to predict the lymphoma subtypes of 290 cases of non-Hodgkin lymphoma (GSE132929). The input layer included both the whole array of 20,863 genes and a cancer transcriptome panel of 1769 genes. The output layer was lymphoma subtypes, including follicular lymphoma, mantle cell lymphoma, diffuse large B-cell lymphoma, Burkitt lymphoma, and marginal zone lymphoma. The neural networks successfully classified the cases consistent with the lymphoma subtypes, with an area under the curve (AUC) that ranged from 0.87 to 0.99. The most relevant predictive genes were <i<LCE2B</i<, <i<KNG1</i<, <i<IGHV7_81</i<, <i<TG</i<, <i<C6</i<, <i<FGB</i<, <i<ZNF750</i<, <i<CTSV</i<, <i<INGX</i<, and <i<COL4A6</i< for the whole set; and <i<ARG1</i<, <i<MAGEA3</i<, <i<AKT2</i<, <i<IL1B</i<, <i<S100A7A</i<, <i<CLEC5A</i<, <i<WIF1</i<, <i<TREM1</i<, <i<DEFB1</i<, and <i<GAGE1</i< for the cancer panel. The characteristic predictive genes for each lymphoma subtypes were also identified with high accuracy (AUC = 0.95, incorrect predictions = 6.2%). Finally, the topmost relevant 30 genes of the whole set, which belonged to apoptosis, cell proliferation, metabolism, and antigen presentation pathways, not only predicted the lymphoma subtypes but also the overall survival of diffuse large B-cell lymphoma (series GSE10846, <i<n</i< = 414 cases), and most relevant cancer subtypes of The Cancer Genome Atlas (TCGA) consortium including carcinomas of breast, colorectal, lung, prostate, and gastric, melanoma, etc. (7441 cases). In conclusion, neural networks predicted the non-Hodgkin lymphoma subtypes with high accuracy, and the highlighted genes also predicted the survival of a pan-cancer series. non-Hodgkin lymphoma follicular lymphoma mantle cell lymphoma diffuse large B-cell lymphoma Burkitt lymphoma marginal zone lymphoma Computer engineering. Computer hardware Rifat Hamoudi verfasserin aut In Machine Learning and Knowledge Extraction MDPI AG, 2020 3(2021), 3, Seite 720-739 (DE-627)1025562380 25044990 nnns volume:3 year:2021 number:3 pages:720-739 https://doi.org/10.3390/make3030036 kostenfrei https://doaj.org/article/f1149c6d362d41f9af5c4ef43bb94ced kostenfrei https://www.mdpi.com/2504-4990/3/3/36 kostenfrei https://doaj.org/toc/2504-4990 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2021 3 720-739
spelling	10.3390/make3030036 doi (DE-627)DOAJ056663919 (DE-599)DOAJf1149c6d362d41f9af5c4ef43bb94ced DE-627 ger DE-627 rakwb eng TK7885-7895 Joaquim Carreras verfasserin aut Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Predictive analytics using artificial intelligence is a useful tool in cancer research. A multilayer perceptron neural network used gene expression data to predict the lymphoma subtypes of 290 cases of non-Hodgkin lymphoma (GSE132929). The input layer included both the whole array of 20,863 genes and a cancer transcriptome panel of 1769 genes. The output layer was lymphoma subtypes, including follicular lymphoma, mantle cell lymphoma, diffuse large B-cell lymphoma, Burkitt lymphoma, and marginal zone lymphoma. The neural networks successfully classified the cases consistent with the lymphoma subtypes, with an area under the curve (AUC) that ranged from 0.87 to 0.99. The most relevant predictive genes were <i<LCE2B</i<, <i<KNG1</i<, <i<IGHV7_81</i<, <i<TG</i<, <i<C6</i<, <i<FGB</i<, <i<ZNF750</i<, <i<CTSV</i<, <i<INGX</i<, and <i<COL4A6</i< for the whole set; and <i<ARG1</i<, <i<MAGEA3</i<, <i<AKT2</i<, <i<IL1B</i<, <i<S100A7A</i<, <i<CLEC5A</i<, <i<WIF1</i<, <i<TREM1</i<, <i<DEFB1</i<, and <i<GAGE1</i< for the cancer panel. The characteristic predictive genes for each lymphoma subtypes were also identified with high accuracy (AUC = 0.95, incorrect predictions = 6.2%). Finally, the topmost relevant 30 genes of the whole set, which belonged to apoptosis, cell proliferation, metabolism, and antigen presentation pathways, not only predicted the lymphoma subtypes but also the overall survival of diffuse large B-cell lymphoma (series GSE10846, <i<n</i< = 414 cases), and most relevant cancer subtypes of The Cancer Genome Atlas (TCGA) consortium including carcinomas of breast, colorectal, lung, prostate, and gastric, melanoma, etc. (7441 cases). In conclusion, neural networks predicted the non-Hodgkin lymphoma subtypes with high accuracy, and the highlighted genes also predicted the survival of a pan-cancer series. non-Hodgkin lymphoma follicular lymphoma mantle cell lymphoma diffuse large B-cell lymphoma Burkitt lymphoma marginal zone lymphoma Computer engineering. Computer hardware Rifat Hamoudi verfasserin aut In Machine Learning and Knowledge Extraction MDPI AG, 2020 3(2021), 3, Seite 720-739 (DE-627)1025562380 25044990 nnns volume:3 year:2021 number:3 pages:720-739 https://doi.org/10.3390/make3030036 kostenfrei https://doaj.org/article/f1149c6d362d41f9af5c4ef43bb94ced kostenfrei https://www.mdpi.com/2504-4990/3/3/36 kostenfrei https://doaj.org/toc/2504-4990 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2021 3 720-739
allfields_unstemmed	10.3390/make3030036 doi (DE-627)DOAJ056663919 (DE-599)DOAJf1149c6d362d41f9af5c4ef43bb94ced DE-627 ger DE-627 rakwb eng TK7885-7895 Joaquim Carreras verfasserin aut Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Predictive analytics using artificial intelligence is a useful tool in cancer research. A multilayer perceptron neural network used gene expression data to predict the lymphoma subtypes of 290 cases of non-Hodgkin lymphoma (GSE132929). The input layer included both the whole array of 20,863 genes and a cancer transcriptome panel of 1769 genes. The output layer was lymphoma subtypes, including follicular lymphoma, mantle cell lymphoma, diffuse large B-cell lymphoma, Burkitt lymphoma, and marginal zone lymphoma. The neural networks successfully classified the cases consistent with the lymphoma subtypes, with an area under the curve (AUC) that ranged from 0.87 to 0.99. The most relevant predictive genes were <i<LCE2B</i<, <i<KNG1</i<, <i<IGHV7_81</i<, <i<TG</i<, <i<C6</i<, <i<FGB</i<, <i<ZNF750</i<, <i<CTSV</i<, <i<INGX</i<, and <i<COL4A6</i< for the whole set; and <i<ARG1</i<, <i<MAGEA3</i<, <i<AKT2</i<, <i<IL1B</i<, <i<S100A7A</i<, <i<CLEC5A</i<, <i<WIF1</i<, <i<TREM1</i<, <i<DEFB1</i<, and <i<GAGE1</i< for the cancer panel. The characteristic predictive genes for each lymphoma subtypes were also identified with high accuracy (AUC = 0.95, incorrect predictions = 6.2%). Finally, the topmost relevant 30 genes of the whole set, which belonged to apoptosis, cell proliferation, metabolism, and antigen presentation pathways, not only predicted the lymphoma subtypes but also the overall survival of diffuse large B-cell lymphoma (series GSE10846, <i<n</i< = 414 cases), and most relevant cancer subtypes of The Cancer Genome Atlas (TCGA) consortium including carcinomas of breast, colorectal, lung, prostate, and gastric, melanoma, etc. (7441 cases). In conclusion, neural networks predicted the non-Hodgkin lymphoma subtypes with high accuracy, and the highlighted genes also predicted the survival of a pan-cancer series. non-Hodgkin lymphoma follicular lymphoma mantle cell lymphoma diffuse large B-cell lymphoma Burkitt lymphoma marginal zone lymphoma Computer engineering. Computer hardware Rifat Hamoudi verfasserin aut In Machine Learning and Knowledge Extraction MDPI AG, 2020 3(2021), 3, Seite 720-739 (DE-627)1025562380 25044990 nnns volume:3 year:2021 number:3 pages:720-739 https://doi.org/10.3390/make3030036 kostenfrei https://doaj.org/article/f1149c6d362d41f9af5c4ef43bb94ced kostenfrei https://www.mdpi.com/2504-4990/3/3/36 kostenfrei https://doaj.org/toc/2504-4990 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2021 3 720-739
allfieldsGer	10.3390/make3030036 doi (DE-627)DOAJ056663919 (DE-599)DOAJf1149c6d362d41f9af5c4ef43bb94ced DE-627 ger DE-627 rakwb eng TK7885-7895 Joaquim Carreras verfasserin aut Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Predictive analytics using artificial intelligence is a useful tool in cancer research. A multilayer perceptron neural network used gene expression data to predict the lymphoma subtypes of 290 cases of non-Hodgkin lymphoma (GSE132929). The input layer included both the whole array of 20,863 genes and a cancer transcriptome panel of 1769 genes. The output layer was lymphoma subtypes, including follicular lymphoma, mantle cell lymphoma, diffuse large B-cell lymphoma, Burkitt lymphoma, and marginal zone lymphoma. The neural networks successfully classified the cases consistent with the lymphoma subtypes, with an area under the curve (AUC) that ranged from 0.87 to 0.99. The most relevant predictive genes were <i<LCE2B</i<, <i<KNG1</i<, <i<IGHV7_81</i<, <i<TG</i<, <i<C6</i<, <i<FGB</i<, <i<ZNF750</i<, <i<CTSV</i<, <i<INGX</i<, and <i<COL4A6</i< for the whole set; and <i<ARG1</i<, <i<MAGEA3</i<, <i<AKT2</i<, <i<IL1B</i<, <i<S100A7A</i<, <i<CLEC5A</i<, <i<WIF1</i<, <i<TREM1</i<, <i<DEFB1</i<, and <i<GAGE1</i< for the cancer panel. The characteristic predictive genes for each lymphoma subtypes were also identified with high accuracy (AUC = 0.95, incorrect predictions = 6.2%). Finally, the topmost relevant 30 genes of the whole set, which belonged to apoptosis, cell proliferation, metabolism, and antigen presentation pathways, not only predicted the lymphoma subtypes but also the overall survival of diffuse large B-cell lymphoma (series GSE10846, <i<n</i< = 414 cases), and most relevant cancer subtypes of The Cancer Genome Atlas (TCGA) consortium including carcinomas of breast, colorectal, lung, prostate, and gastric, melanoma, etc. (7441 cases). In conclusion, neural networks predicted the non-Hodgkin lymphoma subtypes with high accuracy, and the highlighted genes also predicted the survival of a pan-cancer series. non-Hodgkin lymphoma follicular lymphoma mantle cell lymphoma diffuse large B-cell lymphoma Burkitt lymphoma marginal zone lymphoma Computer engineering. Computer hardware Rifat Hamoudi verfasserin aut In Machine Learning and Knowledge Extraction MDPI AG, 2020 3(2021), 3, Seite 720-739 (DE-627)1025562380 25044990 nnns volume:3 year:2021 number:3 pages:720-739 https://doi.org/10.3390/make3030036 kostenfrei https://doaj.org/article/f1149c6d362d41f9af5c4ef43bb94ced kostenfrei https://www.mdpi.com/2504-4990/3/3/36 kostenfrei https://doaj.org/toc/2504-4990 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2021 3 720-739
allfieldsSound	10.3390/make3030036 doi (DE-627)DOAJ056663919 (DE-599)DOAJf1149c6d362d41f9af5c4ef43bb94ced DE-627 ger DE-627 rakwb eng TK7885-7895 Joaquim Carreras verfasserin aut Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Predictive analytics using artificial intelligence is a useful tool in cancer research. A multilayer perceptron neural network used gene expression data to predict the lymphoma subtypes of 290 cases of non-Hodgkin lymphoma (GSE132929). The input layer included both the whole array of 20,863 genes and a cancer transcriptome panel of 1769 genes. The output layer was lymphoma subtypes, including follicular lymphoma, mantle cell lymphoma, diffuse large B-cell lymphoma, Burkitt lymphoma, and marginal zone lymphoma. The neural networks successfully classified the cases consistent with the lymphoma subtypes, with an area under the curve (AUC) that ranged from 0.87 to 0.99. The most relevant predictive genes were <i<LCE2B</i<, <i<KNG1</i<, <i<IGHV7_81</i<, <i<TG</i<, <i<C6</i<, <i<FGB</i<, <i<ZNF750</i<, <i<CTSV</i<, <i<INGX</i<, and <i<COL4A6</i< for the whole set; and <i<ARG1</i<, <i<MAGEA3</i<, <i<AKT2</i<, <i<IL1B</i<, <i<S100A7A</i<, <i<CLEC5A</i<, <i<WIF1</i<, <i<TREM1</i<, <i<DEFB1</i<, and <i<GAGE1</i< for the cancer panel. The characteristic predictive genes for each lymphoma subtypes were also identified with high accuracy (AUC = 0.95, incorrect predictions = 6.2%). Finally, the topmost relevant 30 genes of the whole set, which belonged to apoptosis, cell proliferation, metabolism, and antigen presentation pathways, not only predicted the lymphoma subtypes but also the overall survival of diffuse large B-cell lymphoma (series GSE10846, <i<n</i< = 414 cases), and most relevant cancer subtypes of The Cancer Genome Atlas (TCGA) consortium including carcinomas of breast, colorectal, lung, prostate, and gastric, melanoma, etc. (7441 cases). In conclusion, neural networks predicted the non-Hodgkin lymphoma subtypes with high accuracy, and the highlighted genes also predicted the survival of a pan-cancer series. non-Hodgkin lymphoma follicular lymphoma mantle cell lymphoma diffuse large B-cell lymphoma Burkitt lymphoma marginal zone lymphoma Computer engineering. Computer hardware Rifat Hamoudi verfasserin aut In Machine Learning and Knowledge Extraction MDPI AG, 2020 3(2021), 3, Seite 720-739 (DE-627)1025562380 25044990 nnns volume:3 year:2021 number:3 pages:720-739 https://doi.org/10.3390/make3030036 kostenfrei https://doaj.org/article/f1149c6d362d41f9af5c4ef43bb94ced kostenfrei https://www.mdpi.com/2504-4990/3/3/36 kostenfrei https://doaj.org/toc/2504-4990 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2021 3 720-739
language	English
source	In Machine Learning and Knowledge Extraction 3(2021), 3, Seite 720-739 volume:3 year:2021 number:3 pages:720-739
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institution	findex.gbv.de
topic_facet	non-Hodgkin lymphoma follicular lymphoma mantle cell lymphoma diffuse large B-cell lymphoma Burkitt lymphoma marginal zone lymphoma Computer engineering. Computer hardware
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authorswithroles_txt_mv	Joaquim Carreras @@aut@@ Rifat Hamoudi @@aut@@
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callnumber-first	T - Technology
author	Joaquim Carreras
spellingShingle	Joaquim Carreras misc TK7885-7895 misc non-Hodgkin lymphoma misc follicular lymphoma misc mantle cell lymphoma misc diffuse large B-cell lymphoma misc Burkitt lymphoma misc marginal zone lymphoma misc Computer engineering. Computer hardware Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy
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topic_title	TK7885-7895 Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy non-Hodgkin lymphoma follicular lymphoma mantle cell lymphoma diffuse large B-cell lymphoma Burkitt lymphoma marginal zone lymphoma
topic	misc TK7885-7895 misc non-Hodgkin lymphoma misc follicular lymphoma misc mantle cell lymphoma misc diffuse large B-cell lymphoma misc Burkitt lymphoma misc marginal zone lymphoma misc Computer engineering. Computer hardware
topic_unstemmed	misc TK7885-7895 misc non-Hodgkin lymphoma misc follicular lymphoma misc mantle cell lymphoma misc diffuse large B-cell lymphoma misc Burkitt lymphoma misc marginal zone lymphoma misc Computer engineering. Computer hardware
topic_browse	misc TK7885-7895 misc non-Hodgkin lymphoma misc follicular lymphoma misc mantle cell lymphoma misc diffuse large B-cell lymphoma misc Burkitt lymphoma misc marginal zone lymphoma misc Computer engineering. Computer hardware
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title	Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy
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title_full	Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy
author_sort	Joaquim Carreras
journal	Machine Learning and Knowledge Extraction
journalStr	Machine Learning and Knowledge Extraction
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lang_code	eng
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author_browse	Joaquim Carreras Rifat Hamoudi
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class	TK7885-7895
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author-letter	Joaquim Carreras
doi_str_mv	10.3390/make3030036
author2-role	verfasserin
title_sort	artificial neural network analysis of gene expression data predicted non-hodgkin lymphoma subtypes with high accuracy
callnumber	TK7885-7895
title_auth	Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy
abstract	Predictive analytics using artificial intelligence is a useful tool in cancer research. A multilayer perceptron neural network used gene expression data to predict the lymphoma subtypes of 290 cases of non-Hodgkin lymphoma (GSE132929). The input layer included both the whole array of 20,863 genes and a cancer transcriptome panel of 1769 genes. The output layer was lymphoma subtypes, including follicular lymphoma, mantle cell lymphoma, diffuse large B-cell lymphoma, Burkitt lymphoma, and marginal zone lymphoma. The neural networks successfully classified the cases consistent with the lymphoma subtypes, with an area under the curve (AUC) that ranged from 0.87 to 0.99. The most relevant predictive genes were <i<LCE2B</i<, <i<KNG1</i<, <i<IGHV7_81</i<, <i<TG</i<, <i<C6</i<, <i<FGB</i<, <i<ZNF750</i<, <i<CTSV</i<, <i<INGX</i<, and <i<COL4A6</i< for the whole set; and <i<ARG1</i<, <i<MAGEA3</i<, <i<AKT2</i<, <i<IL1B</i<, <i<S100A7A</i<, <i<CLEC5A</i<, <i<WIF1</i<, <i<TREM1</i<, <i<DEFB1</i<, and <i<GAGE1</i< for the cancer panel. The characteristic predictive genes for each lymphoma subtypes were also identified with high accuracy (AUC = 0.95, incorrect predictions = 6.2%). Finally, the topmost relevant 30 genes of the whole set, which belonged to apoptosis, cell proliferation, metabolism, and antigen presentation pathways, not only predicted the lymphoma subtypes but also the overall survival of diffuse large B-cell lymphoma (series GSE10846, <i<n</i< = 414 cases), and most relevant cancer subtypes of The Cancer Genome Atlas (TCGA) consortium including carcinomas of breast, colorectal, lung, prostate, and gastric, melanoma, etc. (7441 cases). In conclusion, neural networks predicted the non-Hodgkin lymphoma subtypes with high accuracy, and the highlighted genes also predicted the survival of a pan-cancer series.
abstractGer	Predictive analytics using artificial intelligence is a useful tool in cancer research. A multilayer perceptron neural network used gene expression data to predict the lymphoma subtypes of 290 cases of non-Hodgkin lymphoma (GSE132929). The input layer included both the whole array of 20,863 genes and a cancer transcriptome panel of 1769 genes. The output layer was lymphoma subtypes, including follicular lymphoma, mantle cell lymphoma, diffuse large B-cell lymphoma, Burkitt lymphoma, and marginal zone lymphoma. The neural networks successfully classified the cases consistent with the lymphoma subtypes, with an area under the curve (AUC) that ranged from 0.87 to 0.99. The most relevant predictive genes were <i<LCE2B</i<, <i<KNG1</i<, <i<IGHV7_81</i<, <i<TG</i<, <i<C6</i<, <i<FGB</i<, <i<ZNF750</i<, <i<CTSV</i<, <i<INGX</i<, and <i<COL4A6</i< for the whole set; and <i<ARG1</i<, <i<MAGEA3</i<, <i<AKT2</i<, <i<IL1B</i<, <i<S100A7A</i<, <i<CLEC5A</i<, <i<WIF1</i<, <i<TREM1</i<, <i<DEFB1</i<, and <i<GAGE1</i< for the cancer panel. The characteristic predictive genes for each lymphoma subtypes were also identified with high accuracy (AUC = 0.95, incorrect predictions = 6.2%). Finally, the topmost relevant 30 genes of the whole set, which belonged to apoptosis, cell proliferation, metabolism, and antigen presentation pathways, not only predicted the lymphoma subtypes but also the overall survival of diffuse large B-cell lymphoma (series GSE10846, <i<n</i< = 414 cases), and most relevant cancer subtypes of The Cancer Genome Atlas (TCGA) consortium including carcinomas of breast, colorectal, lung, prostate, and gastric, melanoma, etc. (7441 cases). In conclusion, neural networks predicted the non-Hodgkin lymphoma subtypes with high accuracy, and the highlighted genes also predicted the survival of a pan-cancer series.
abstract_unstemmed	Predictive analytics using artificial intelligence is a useful tool in cancer research. A multilayer perceptron neural network used gene expression data to predict the lymphoma subtypes of 290 cases of non-Hodgkin lymphoma (GSE132929). The input layer included both the whole array of 20,863 genes and a cancer transcriptome panel of 1769 genes. The output layer was lymphoma subtypes, including follicular lymphoma, mantle cell lymphoma, diffuse large B-cell lymphoma, Burkitt lymphoma, and marginal zone lymphoma. The neural networks successfully classified the cases consistent with the lymphoma subtypes, with an area under the curve (AUC) that ranged from 0.87 to 0.99. The most relevant predictive genes were <i<LCE2B</i<, <i<KNG1</i<, <i<IGHV7_81</i<, <i<TG</i<, <i<C6</i<, <i<FGB</i<, <i<ZNF750</i<, <i<CTSV</i<, <i<INGX</i<, and <i<COL4A6</i< for the whole set; and <i<ARG1</i<, <i<MAGEA3</i<, <i<AKT2</i<, <i<IL1B</i<, <i<S100A7A</i<, <i<CLEC5A</i<, <i<WIF1</i<, <i<TREM1</i<, <i<DEFB1</i<, and <i<GAGE1</i< for the cancer panel. The characteristic predictive genes for each lymphoma subtypes were also identified with high accuracy (AUC = 0.95, incorrect predictions = 6.2%). Finally, the topmost relevant 30 genes of the whole set, which belonged to apoptosis, cell proliferation, metabolism, and antigen presentation pathways, not only predicted the lymphoma subtypes but also the overall survival of diffuse large B-cell lymphoma (series GSE10846, <i<n</i< = 414 cases), and most relevant cancer subtypes of The Cancer Genome Atlas (TCGA) consortium including carcinomas of breast, colorectal, lung, prostate, and gastric, melanoma, etc. (7441 cases). In conclusion, neural networks predicted the non-Hodgkin lymphoma subtypes with high accuracy, and the highlighted genes also predicted the survival of a pan-cancer series.
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title_short	Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy
url	https://doi.org/10.3390/make3030036 https://doaj.org/article/f1149c6d362d41f9af5c4ef43bb94ced https://www.mdpi.com/2504-4990/3/3/36 https://doaj.org/toc/2504-4990
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