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Deep learning-based classifier of diffuse large B-cell lymphoma cell-of-origin with clinical outcome by Aswathi Viswanathan; Kavita Kundal; Avik Sengupta; Ambuj Kumar; Keerthana Vinod Kumar; Antony B Holmes; Rahul Kumar is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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## Abstract Diffuse large B-cell lymphoma (DLBCL) is an aggressive form of non-Hodgkin lymphoma with poor response to R-CHOP therapy due to remarkable heterogeneity. Based on gene expression, DLBCL cases were divided into two subtypes, i.e. ABC and GCB, where ABC subtype is associated with poor outcomes. Due to its association with clinical outcome, this classification, also known as cell-of-origin (COO), is an efficient way to predict the response to R-CHOP therapy. Previous COO classification methods have some shortcomings, e.g. limited number of samples in the training dataset. These shortcomings challenge the robustness of methods and make it difficult to implicate these methods at clinical level. To overcome the shortcomings of previous methods, we developed a deep learning-based classifier model on a cohort of 381 DLBCL patients using expression data of 20 genes. We implemented multilayer perceptron (MLP) to train deep learning-based classifier, named MLP-COO. MLP-COO achieved accuracy of 99.70% and 94.70% on training and testing datasets, respectively, with 10-fold cross-validation. We also assessed its performance on an independent dataset of 294 DLBCL patients. On independ

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Author

Aswathi Viswanathan; Kavita Kundal; Avik Sengupta; Ambuj Kumar; Keerthana Vinod Kumar; Antony B Holmes; Rahul Kumar

Publisher

Oxford University Press (OUP)

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)