Inferring causal protein signalling networks from single‐cell data based on parallel discrete artificial bee colony algorithm
Abstract Inferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells, which has attracted considerable attention within the bioinformatics field. Recently, Bayesian netwo...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | CAAI Transactions on Intelligence Technology |
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| Online Access: | https://doi.org/10.1049/cit2.12344 |
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| author | Jinduo Liu Jihao Zhai Junzhong Ji |
| author_facet | Jinduo Liu Jihao Zhai Junzhong Ji |
| author_sort | Jinduo Liu |
| collection | DOAJ |
| description | Abstract Inferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells, which has attracted considerable attention within the bioinformatics field. Recently, Bayesian network (BN) techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single‐cell data. However, current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells. A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony (PDABC), named PDABC. Specifically, PDABC is a score‐based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 metric. The experimental results on several simulated datasets, as well as a previously published multi‐parameter fluorescence‐activated cell sorter dataset, indicate that PDABC surpasses the existing state‐of‐the‐art methods in terms of performance and computational efficiency. |
| format | Article |
| id | doaj-art-e505d18d7bd249ed99a97a0584061e21 |
| institution | OA Journals |
| issn | 2468-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj-art-e505d18d7bd249ed99a97a0584061e212025-08-20T02:34:53ZengWileyCAAI Transactions on Intelligence Technology2468-23222024-12-01961587160410.1049/cit2.12344Inferring causal protein signalling networks from single‐cell data based on parallel discrete artificial bee colony algorithmJinduo Liu0Jihao Zhai1Junzhong Ji2Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Faculty of Information Technology, Beijing University of Technology Beijing ChinaBeijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Faculty of Information Technology, Beijing University of Technology Beijing ChinaBeijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Faculty of Information Technology, Beijing University of Technology Beijing ChinaAbstract Inferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells, which has attracted considerable attention within the bioinformatics field. Recently, Bayesian network (BN) techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single‐cell data. However, current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells. A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony (PDABC), named PDABC. Specifically, PDABC is a score‐based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 metric. The experimental results on several simulated datasets, as well as a previously published multi‐parameter fluorescence‐activated cell sorter dataset, indicate that PDABC surpasses the existing state‐of‐the‐art methods in terms of performance and computational efficiency.https://doi.org/10.1049/cit2.12344Bayesian networkcausal protein signaling networksparallel discrete artificial bee colonysingle‐cell data |
| spellingShingle | Jinduo Liu Jihao Zhai Junzhong Ji Inferring causal protein signalling networks from single‐cell data based on parallel discrete artificial bee colony algorithm CAAI Transactions on Intelligence Technology Bayesian network causal protein signaling networks parallel discrete artificial bee colony single‐cell data |
| title | Inferring causal protein signalling networks from single‐cell data based on parallel discrete artificial bee colony algorithm |
| title_full | Inferring causal protein signalling networks from single‐cell data based on parallel discrete artificial bee colony algorithm |
| title_fullStr | Inferring causal protein signalling networks from single‐cell data based on parallel discrete artificial bee colony algorithm |
| title_full_unstemmed | Inferring causal protein signalling networks from single‐cell data based on parallel discrete artificial bee colony algorithm |
| title_short | Inferring causal protein signalling networks from single‐cell data based on parallel discrete artificial bee colony algorithm |
| title_sort | inferring causal protein signalling networks from single cell data based on parallel discrete artificial bee colony algorithm |
| topic | Bayesian network causal protein signaling networks parallel discrete artificial bee colony single‐cell data |
| url | https://doi.org/10.1049/cit2.12344 |
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