{"title":"基于并行离散人工蜂群算法从单细胞数据中推断因果蛋白质信号网络","authors":"Jinduo Liu, Jihao Zhai, Junzhong Ji","doi":"10.1049/cit2.12344","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 6","pages":"1587-1604"},"PeriodicalIF":8.4000,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12344","citationCount":"0","resultStr":"{\"title\":\"Inferring causal protein signalling networks from single-cell data based on parallel discrete artificial bee colony algorithm\",\"authors\":\"Jinduo Liu, Jihao Zhai, Junzhong Ji\",\"doi\":\"10.1049/cit2.12344\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":46211,\"journal\":{\"name\":\"CAAI Transactions on Intelligence Technology\",\"volume\":\"9 6\",\"pages\":\"1587-1604\"},\"PeriodicalIF\":8.4000,\"publicationDate\":\"2024-05-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12344\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CAAI Transactions on Intelligence Technology\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/cit2.12344\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CAAI Transactions on Intelligence Technology","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cit2.12344","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Inferring causal protein signalling networks from single-cell data based on parallel discrete artificial bee colony algorithm
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.
期刊介绍:
CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.