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Volume 4
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Article

Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data

by
Joaquim Carreras
1,*,
Yara Yukie Kikuti
1,
Masashi Miyaoka
1,
Saya Miyahara
1,
Giovanna Roncador
2,
Rifat Hamoudi
3,4,5,6 and
Naoya Nakamura
1
1
Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, Japan
2
Monoclonal Antibodies Unit, Spanish National Cancer Research Center (Centro Nacional de Investigaciones Oncologicas, CNIO), Melchor Fernandez Almagro 3, 28029 Madrid, Spain
3
Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
4
Division of Surgery and Interventional Science, University College London, London WC1E 6BT, UK
5
ASPIRE Precision Medicine Research Institute Abu Dhabi, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
6
BIMAI-Lab, Biomedically Informed Artificial Intelligence Laboratory, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
*
Author to whom correspondence should be addressed.
BioMedInformatics 2024, 4(1), 295-320; https://doi.org/10.3390/biomedinformatics4010017
Submission received: 7 December 2023 / Revised: 9 January 2024 / Accepted: 17 January 2024 / Published: 26 January 2024
(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)
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Abstract

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.
Keywords: diffuse large B-cell lymphoma; Burkitt lymphoma; artificial intelligence; machine learning; artificial neural networks; multilayer perceptron; aggressive mature B-cell lymphomas; predictive analytics; Molecular high-grade DLBCL; bioinformatics diffuse large B-cell lymphoma; Burkitt lymphoma; artificial intelligence; machine learning; artificial neural networks; multilayer perceptron; aggressive mature B-cell lymphomas; predictive analytics; Molecular high-grade DLBCL; bioinformatics
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MDPI and ACS Style

Carreras, J.; Yukie Kikuti, Y.; Miyaoka, M.; Miyahara, S.; Roncador, G.; Hamoudi, R.; Nakamura, N. Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data. BioMedInformatics 2024, 4, 295-320. https://doi.org/10.3390/biomedinformatics4010017

AMA Style

Carreras J, Yukie Kikuti Y, Miyaoka M, Miyahara S, Roncador G, Hamoudi R, Nakamura N. Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data. BioMedInformatics. 2024; 4(1):295-320. https://doi.org/10.3390/biomedinformatics4010017

Chicago/Turabian Style

Carreras, Joaquim, Yara Yukie Kikuti, Masashi Miyaoka, Saya Miyahara, Giovanna Roncador, Rifat Hamoudi, and Naoya Nakamura. 2024. "Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data" BioMedInformatics 4, no. 1: 295-320. https://doi.org/10.3390/biomedinformatics4010017

APA Style

Carreras, J., Yukie Kikuti, Y., Miyaoka, M., Miyahara, S., Roncador, G., Hamoudi, R., & Nakamura, N. (2024). Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data. BioMedInformatics, 4(1), 295-320. https://doi.org/10.3390/biomedinformatics4010017

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