Classification of single cell types during leukemia therapy using artificial neural networks
We trained artificial neural network (ANN) models to classify peripheral blood mononuclear cells (PBMC) in chronic lymphoid leukemia (CLL) patients. The classification task was to determine differences in gene expression profiles in PBMC pre-treatment (with ibrutinib) and on days 30, 120, 150, and 2...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2021
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| Online Access: | https://eprints.nottingham.ac.uk/64683/ |
| _version_ | 1848800153809453056 |
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| author | LYU, Minjie Radenkovic, Milena KESKIN, DerinB. Brusic, Vladimir |
| author_facet | LYU, Minjie Radenkovic, Milena KESKIN, DerinB. Brusic, Vladimir |
| author_sort | LYU, Minjie |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | We trained artificial neural network (ANN) models to classify peripheral blood mononuclear cells (PBMC) in chronic lymphoid leukemia (CLL) patients. The classification task was to determine differences in gene expression profiles in PBMC pre-treatment (with ibrutinib) and on days 30, 120, 150, and 280 after the start of treatment. Twelve datasets represented clinical samples containing a total 48,016 single cell profiles were used to train and test ANN models to classify the progress of therapy by gene expression changes. The accuracy of ANN classification was >92% in internal cross-validation. External cross-validation, using independent data sets for training and testing, showed the accuracy of classification of post-treatment PBMCs to more than 80%. To the best of our knowledge, this is the first study that has demonstrated the potential of ANNs with 10x single cell gene expression data for detecting the changes during treatment of CLL. |
| first_indexed | 2025-11-14T20:47:02Z |
| format | Conference or Workshop Item |
| id | nottingham-64683 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:47:02Z |
| publishDate | 2021 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-646832021-03-10T06:23:56Z https://eprints.nottingham.ac.uk/64683/ Classification of single cell types during leukemia therapy using artificial neural networks LYU, Minjie Radenkovic, Milena KESKIN, DerinB. Brusic, Vladimir We trained artificial neural network (ANN) models to classify peripheral blood mononuclear cells (PBMC) in chronic lymphoid leukemia (CLL) patients. The classification task was to determine differences in gene expression profiles in PBMC pre-treatment (with ibrutinib) and on days 30, 120, 150, and 280 after the start of treatment. Twelve datasets represented clinical samples containing a total 48,016 single cell profiles were used to train and test ANN models to classify the progress of therapy by gene expression changes. The accuracy of ANN classification was >92% in internal cross-validation. External cross-validation, using independent data sets for training and testing, showed the accuracy of classification of post-treatment PBMCs to more than 80%. To the best of our knowledge, this is the first study that has demonstrated the potential of ANNs with 10x single cell gene expression data for detecting the changes during treatment of CLL. 2021-01-13 Conference or Workshop Item PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/64683/1/Minjie_Classification%20of%20Single%20Cell%20Types%20During%20Leukemia%20Therapy%20using%20Artificial%20Neural%20Networks.pdf LYU, Minjie, Radenkovic, Milena, KESKIN, DerinB. and Brusic, Vladimir (2021) Classification of single cell types during leukemia therapy using artificial neural networks. In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 16-19 Dec. 2020, Seoul, Korea (South). ANN PBMC CLL ibrutinib scRNAseq Machine Learning http://dx.doi.org/10.1109/BIBM49941.2020.9313319 10.1109/BIBM49941.2020.9313319 10.1109/BIBM49941.2020.9313319 10.1109/BIBM49941.2020.9313319 |
| spellingShingle | ANN PBMC CLL ibrutinib scRNAseq Machine Learning LYU, Minjie Radenkovic, Milena KESKIN, DerinB. Brusic, Vladimir Classification of single cell types during leukemia therapy using artificial neural networks |
| title | Classification of single cell types during leukemia therapy using artificial neural networks |
| title_full | Classification of single cell types during leukemia therapy using artificial neural networks |
| title_fullStr | Classification of single cell types during leukemia therapy using artificial neural networks |
| title_full_unstemmed | Classification of single cell types during leukemia therapy using artificial neural networks |
| title_short | Classification of single cell types during leukemia therapy using artificial neural networks |
| title_sort | classification of single cell types during leukemia therapy using artificial neural networks |
| topic | ANN PBMC CLL ibrutinib scRNAseq Machine Learning |
| url | https://eprints.nottingham.ac.uk/64683/ https://eprints.nottingham.ac.uk/64683/ https://eprints.nottingham.ac.uk/64683/ |