Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data
Diffuse large B-cell lymphoma is one of the most frequent mature B-cell hematological neoplasms and non-Hodgkin lymphomas. Despite advances in diagnosis and treatment, clinical evolution is unfavorable in a subset of patients. Using molecular techniques, several pathogenic models have been proposed,...
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MDPI AG
2024-01-01
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| author | Joaquim Carreras Yara Yukie Kikuti Masashi Miyaoka Saya Miyahara Giovanna Roncador Rifat Hamoudi Naoya Nakamura |
| author_facet | Joaquim Carreras Yara Yukie Kikuti Masashi Miyaoka Saya Miyahara Giovanna Roncador Rifat Hamoudi Naoya Nakamura |
| author_sort | Joaquim Carreras |
| collection | DOAJ |
| description | Diffuse large B-cell lymphoma is one of the most frequent mature B-cell hematological neoplasms and non-Hodgkin lymphomas. Despite advances in diagnosis and treatment, clinical evolution is unfavorable in a subset of patients. Using molecular techniques, several pathogenic models have been proposed, including cell-of-origin molecular classification; Hans’ classification and derivates; and the Schmitz, Chapuy, Lacy, Reddy, and Sha models. This study introduced different machine learning techniques and their classification. Later, several machine learning techniques and artificial neural networks were used to predict the DLBCL subtypes with high accuracy (100–95%), including Germinal center B-cell like (GCB), Activated B-cell like (ABC), Molecular high-grade (MHG), and Unclassified (UNC), in the context of the data released by the REMoDL-B trial. In order of accuracy (MHG vs. others), the techniques were XGBoost tree (100%); random trees (99.9%); random forest (99.5%); and C5, Bayesian network, SVM, logistic regression, KNN algorithm, neural networks, LSVM, discriminant analysis, CHAID, C&R tree, tree-AS, Quest, and XGBoost linear (99.4–91.1%). The inputs (predictors) were all the genes of the array and a set of 28 genes related to DLBCL-Burkitt differential expression. In summary, artificial intelligence (AI) is a useful tool for predictive analytics using gene expression data. |
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| spelling | doaj.art-2a87a98ec13f4a0786cab6f026a1162e2024-03-27T13:27:29ZengMDPI AGBioMedInformatics2673-74262024-01-014129532010.3390/biomedinformatics4010017Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression DataJoaquim Carreras0Yara Yukie Kikuti1Masashi Miyaoka2Saya Miyahara3Giovanna Roncador4Rifat Hamoudi5Naoya Nakamura6Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, JapanDepartment of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, JapanDepartment of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, JapanDepartment of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, JapanMonoclonal Antibodies Unit, Spanish National Cancer Research Center (Centro Nacional de Investigaciones Oncologicas, CNIO), Melchor Fernandez Almagro 3, 28029 Madrid, SpainDepartment of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah P.O. Box 27272, United Arab EmiratesDepartment of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, JapanDiffuse large B-cell lymphoma is one of the most frequent mature B-cell hematological neoplasms and non-Hodgkin lymphomas. Despite advances in diagnosis and treatment, clinical evolution is unfavorable in a subset of patients. Using molecular techniques, several pathogenic models have been proposed, including cell-of-origin molecular classification; Hans’ classification and derivates; and the Schmitz, Chapuy, Lacy, Reddy, and Sha models. This study introduced different machine learning techniques and their classification. Later, several machine learning techniques and artificial neural networks were used to predict the DLBCL subtypes with high accuracy (100–95%), including Germinal center B-cell like (GCB), Activated B-cell like (ABC), Molecular high-grade (MHG), and Unclassified (UNC), in the context of the data released by the REMoDL-B trial. In order of accuracy (MHG vs. others), the techniques were XGBoost tree (100%); random trees (99.9%); random forest (99.5%); and C5, Bayesian network, SVM, logistic regression, KNN algorithm, neural networks, LSVM, discriminant analysis, CHAID, C&R tree, tree-AS, Quest, and XGBoost linear (99.4–91.1%). The inputs (predictors) were all the genes of the array and a set of 28 genes related to DLBCL-Burkitt differential expression. In summary, artificial intelligence (AI) is a useful tool for predictive analytics using gene expression data.https://www.mdpi.com/2673-7426/4/1/17diffuse large B-cell lymphomaBurkitt lymphomaartificial intelligencemachine learningartificial neural networksmultilayer perceptron |
| spellingShingle | Joaquim Carreras Yara Yukie Kikuti Masashi Miyaoka Saya Miyahara Giovanna Roncador Rifat Hamoudi Naoya Nakamura Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data BioMedInformatics diffuse large B-cell lymphoma Burkitt lymphoma artificial intelligence machine learning artificial neural networks multilayer perceptron |
| title | Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data |
| title_full | Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data |
| title_fullStr | Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data |
| title_full_unstemmed | Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data |
| title_short | Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data |
| title_sort | artificial intelligence analysis and reverse engineering of molecular subtypes of diffuse large b cell lymphoma using gene expression data |
| topic | diffuse large B-cell lymphoma Burkitt lymphoma artificial intelligence machine learning artificial neural networks multilayer perceptron |
| url | https://www.mdpi.com/2673-7426/4/1/17 |
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