IET Systems Biology最新文献

{"title":"Deep-GB: A novel deep learning model for globular protein prediction using CNN-BiLSTM architecture and enhanced PSSM with trisection strategy.","authors":"Sonia Zouari, Farman Ali, Atef Masmoudi, Sarah Abu Ghazalah, Wajdi Alghamdi, Faris A Kateb, Nouf Ibrahim","doi":"10.1049/syb2.12108","DOIUrl":"https://doi.org/10.1049/syb2.12108","url":null,"abstract":"<p><p>Globular proteins (GPs) play vital roles in a wide range of biological processes, encompassing enzymatic catalysis and immune responses. Enzymes, among these globular proteins, facilitate biochemical reactions, while others, such as haemoglobin, contribute to essential physiological functions such as oxygen transport. Given the importance of these considerations, accurately identifying Globular proteins is essential. To address the need for precise GP identification, this research introduces an innovative approach that employs a hybrid-based deep learning model called Deep-GP. We generated two datasets based on primary sequences and developed a novel feature descriptor called, Consensus Sequence-based Trisection-Position Specific Scoring Matrix (CST-PSSM). The model training phase involved the application of deep learning techniques, including the bidirectional long short-term memory network (BiLSTM), gated recurrent unit (GRU), and convolutional neural network (CNN). The BiLSTM and CNN were hybridised for ensemble learning. The CST-PSSM-based ensemble model achieved the most accurate predictive outcomes, outperforming other competitive predictors across both training and testing datasets. This demonstrates the potential of harnessing deep learning for precise GB prediction as a robust tool to expedite research, streamline drug discovery, and unveil novel therapeutic targets.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142607355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a machine learning model with enhanced performance for predicting COVID-19 from patients presenting to the emergency room with acute respiratory symptoms.","authors":"Maha Mesfer Alghamdi, Naael H Alazwary, Waleed A Alsowayan, Mohmmed Algamdi, Ahmed F Alohali, Mustafa A Yasawy, Abeer M Alghamdi, Abdullah M Alassaf, Mohammed R Alshehri, Hussein A Aljaziri, Nujoud H Almoqati, Shatha S Alghamdi, Norah A Bin Magbel, Tareq A AlMazeedi, Nashaat K Neyazi, Mona M Alghamdi, Mohammed N Alazwary","doi":"10.1049/syb2.12101","DOIUrl":"https://doi.org/10.1049/syb2.12101","url":null,"abstract":"<p><p>Artificial Intelligence is playing a crucial role in healthcare by enhancing decision-making and data analysis, particularly during the COVID-19 pandemic. This virus affects individuals across all age groups, but its impact is more severe on the elderly and those with underlying health issues like chronic diseases. This study aimed to develop a machine learning model to improve the prediction of COVID-19 in patients with acute respiratory symptoms. Data from 915 patients in two hospitals in Saudi Arabia were used, categorized into four groups based on chronic lung conditions and COVID-19 status. Four supervised machine learning algorithms-Random Forest, Bagging classifier, Decision Tree, and Logistic Regression-were employed to predict COVID-19. Feature selection identified 12 key variables for prediction, including CXR abnormalities, smoking status, and WBC count. The Random Forest model showed the highest accuracy at 99.07%, followed by Decision Tree, Bagging classifier, and Logistic Regression. The study concluded that machine learning algorithms, particularly Random Forest, can effectively predict and classify COVID-19 cases, supporting the development of computer-assisted diagnostic tools in healthcare.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanism of action of \"cistanche deserticola-Polygala\" in treating Alzheimer's disease based on network pharmacology methods and molecular docking analysis.","authors":"Shaoqiang Wang, Yifan Wang","doi":"10.1049/syb2.12100","DOIUrl":"https://doi.org/10.1049/syb2.12100","url":null,"abstract":"<p><p>This article used network pharmacology, molecular docking, GEO analysis, and Gene Set Enrichment Analysis to obtain 38 main chemical components and 66 corresponding targets involved in Alzheimer's disease (AD) treatment in \"Cistanche deserticola-Polygala\". Through further Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes enrichment analysis, we obtained AD signalling pathways, calcium signalling pathways, and other signalling pathways related to the treatment of AD with \"Cistanche deserticola-Polygala\". Molecular docking showed that most of the core chemical components had good binding ability with the core targets. This article aims to reveal the mechanism of \"Cistanche deserticola-Polygala\" in treating AD and provide a basis for the treatment of AD with \"Cistanche deserticola-Polygala\".</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive transcriptome and scRNA-seq analyses uncover the expression and underlying mechanism of SYNJ2 in papillary thyroid carcinoma","authors":"Yuan-Ping Yang,&nbsp;Zhi-Guang Huang,&nbsp;Jia-Yuan Luo,&nbsp;Juan He,&nbsp;Lin Shi,&nbsp;Gang Chen,&nbsp;Si-Yuan Chen,&nbsp;Yu-Wen Deng,&nbsp;Yi-Jia Yang,&nbsp;Yi-Jun Tang,&nbsp;Yu-Yan Pang","doi":"10.1049/syb2.12099","DOIUrl":"10.1049/syb2.12099","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Synaptojanin 2 (SYNJ2) has crucial role in various tumors, but its role in papillary thyroid carcinoma (PTC) remains unexplored. This study first detected SYNJ2 protein expression in PTC using immunohistochemistry method and further assessed SYNJ2 mRNA expression through mRNA chip and RNA sequencing data and its association with clinical characteristics. Additionally, KEGG, GSVA, and GSEA analyses were conducted to investigate potential biological functions, while single-cell RNA sequencing data were used to explore SYNJ2's underlying mechanisms in PTC. Meanwhile, immune infiltration status in different SYNJ2 expression groups were analyzed. Besides, we investigated the immune checkpoint gene expression and implemented drug sensitivity analysis. Results indicated that SYNJ2 is highly expressed in PTC (SMD = 0.66 [95% CI: 0.17–1.15]) and could distinguish between PTC and non-PTC tissues (AUC = 0.74 [0.70–0.78]). Furthermore, the study identified 134 intersecting genes of DEGs and CEGs, mainly enriched in the angiogenesis and epithelial-mesenchymal transition (EMT) pathways. Subsequent analysis showed the above pathways were activated in PTC epithelial cells. PTC patients with high SYNJ2 expression showed higher sensitivity to the six common drugs. Summarily, SYNJ2 may promote PTC progression through angiogenesis and EMT pathways. High SYNJ2 expression is associated with better response to immunotherapy and chemotherapy.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.12099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"iGATTLDA: Integrative graph attention and transformer-based model for predicting lncRNA-Disease associations","authors":"Biffon Manyura Momanyi,&nbsp;Sebu Aboma Temesgen,&nbsp;Tian-Yu Wang,&nbsp;Hui Gao,&nbsp;Ru Gao,&nbsp;Hua Tang,&nbsp;Li-Xia Tang","doi":"10.1049/syb2.12098","DOIUrl":"10.1049/syb2.12098","url":null,"abstract":"<p>Long non-coding RNAs (lncRNAs) have emerged as significant contributors to the regulation of various biological processes, and their dysregulation has been linked to a variety of human disorders. Accurate prediction of potential correlations between lncRNAs and diseases is crucial for advancing disease diagnostics and treatment procedures. The authors introduced a novel computational method, iGATTLDA, for the prediction of lncRNA-disease associations. The model utilised lncRNA and disease similarity matrices, with known associations represented in an adjacency matrix. A heterogeneous network was constructed, dissecting lncRNAs and diseases as nodes and their associations as edges. The Graph Attention Network (GAT) is employed to process initial features and corresponding adjacency information. GAT identified significant neighbouring nodes in the network, capturing intricate relationships between lncRNAs and diseases, and generating new feature representations. Subsequently, the transformer captures global dependencies and interactions across the entire sequence of features produced by the GAT. Consequently, iGATTLDA successfully captures complex relationships and interactions that conventional approaches may overlook. In evaluating iGATTLDA, it attained an area under the receiver operating characteristic (ROC) curve (AUC) of 0.95 and an area under the precision recall curve (AUPRC) of 0.96 with a two-layer multilayer perceptron (MLP) classifier. These results were notably higher compared to the majority of previously proposed models, further substantiating the model’s efficiency in predicting potential lncRNA-disease associations by incorporating both local and global interactions. The implementation details can be obtained from https://github.com/momanyibiffon/iGATTLDA.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.12098","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A tumour-associated macrophage-based signature for deciphering prognosis and immunotherapy response in prostate cancer","authors":"Jian Wang,&nbsp;Tao Guo,&nbsp;Yuanyuan Mi,&nbsp;Xiangyu Meng,&nbsp;Shuang Xu,&nbsp;Feng Dai,&nbsp;Chengwen Sun,&nbsp;Yi Huang,&nbsp;Jun Wang,&nbsp;Lijie Zhu,&nbsp;Jianquan Hou,&nbsp;Sheng Wu","doi":"10.1049/syb2.12097","DOIUrl":"10.1049/syb2.12097","url":null,"abstract":"<p>For the multistage progression of prostate cancer (PCa) and resistance to immunotherapy, tumour-associated macrophage is an essential contributor. Although immunotherapy is an important and promising treatment modality for cancer, most patients with PCa are not responsive towards it. In addition to exploring new therapeutic targets, it is imperative to identify highly immunotherapy-sensitive individuals. This research aimed to establish a signature risk model, which derived from the macrophage, to assess immunotherapeutic responses and predict prognosis. Data from the UCSC-XENA, GEO and TISCH databases were extracted for analysis. Based on both single-cell datasets and bulk transcriptome profiles, a macrophage-related score (MRS) consisting of the 10-gene panel was constructed using the gene set variation analysis. MRS was highly correlated with hypoxia, angiogenesis, and epithelial-mesenchymal transition, suggesting its potential as a risk indicator. Moreover, poor immunotherapy responses and worse prognostic performance were observed in the high-MRS group of various immunotherapy cohorts. Additionally, APOE, one of the constituent genes of the MRS, affected the polarisation of macrophages. In particular, the reduced level of M2 macrophage and tumour progression suppression were observed in PCa xenografts which implanted in Apolipoprotein E-knockout mice. The constructed MRS has the potential as a robust prognostic prediction tool, and can aid in the treatment selection of PCa, especially immunotherapy options.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.12097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141977112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification and analysis of epithelial-mesenchymal transition-related key long non-coding RNAs in hypospadias","authors":"Hongjie Gao,&nbsp;Chen Ding,&nbsp;Mengmeng Chang,&nbsp;Zhiyi Lu,&nbsp;Ding Li,&nbsp;Dan Bi,&nbsp;Fengyin Sun","doi":"10.1049/syb2.12096","DOIUrl":"10.1049/syb2.12096","url":null,"abstract":"<p>EMT dysfunction is a dominant mechanisms of hypospadias. Thus, identification of EMT-related lncRNAs based on transcriptome sequencing data of hypospadias might provide novel molecular markers and therapeutic targets for hypospadias. First, the microarray data related to hypospadias were downloaded from Gene Expression Omnibus (GEO). Besides, the differentially expressed lncRNAs and messenger RNAs (mRNAs) related to EMT were screened to construct lncRNA-mRNA co-expression interaction pairs. In addition, the microRNA (miRNA) prediction analysis was performed through bioinformatics methods to construct a ceRNA network. Moreover, function prediction and function enrichment and pathway analyses were also performed. Finally, the core EMT-related lncRNAs were verified based on mRNA expression changes and cell functions. A total of 6 EMT-related lncRNAs were identified and 123 mRNA-lncRNA co-expression interaction pairs were screened in this study. Additionally, a ceRNA regulatory network comprising 17 mRNAs, 4 lncRNAs, and 28 miRNAs was constructed based on the prediction of hypospadias-related miRNAs. The validation results of the dataset GSE121712 revealed that only BEX1 was positively correlated with the expression of the lncRNA GNAS-AS1 (r = 0.874, <i>P</i> &lt; 0.01), both of which had high expression. The cell experiment results demonstrated that interfering with the expression of GNAS-AS1 significantly promoted the proliferation, migration, and EMT of cells. Importantly, it was confirmed that GNAS-AS1 can serve as a ceRNA and play an important role in the EMT of hypospadias. Hence, it may be considered as a potential target in the treatment of this disease.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.12096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revealing the potential role of hub metabolism-related genes and their correlation with immune cells in acute ischemic stroke","authors":"Xianjing Zhang,&nbsp;Tengxiao Xu,&nbsp;Chen Wang,&nbsp;Yueyue Lin,&nbsp;Weimi Hu,&nbsp;Maokui Yue,&nbsp;Hao Li","doi":"10.1049/syb2.12095","DOIUrl":"10.1049/syb2.12095","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>Acute ischemic stroke (AIS) is caused by cerebral ischemia due to thrombosis in the blood vessel. The purpose of this study is to identify key genes related to metabolism to aid in the mechanism research and management of AIS.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Materials and Methods</h3>\u0000 \u0000 <p>Gene expression data were downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis, Gene Ontology and kyoto encyclopedia of genes and genomes analysis were used to identify metabolism-related genes that may be involved in the regulation of AIS. A protein protein interaction network was mapped using Cytoscape based on the STRING database. Subsequently, hub metabolism-related genes were identified based on Cytoscape-CytoNCA and Cytoscape-MCODE plug-ins. Least absolute shrinkage and selection operator algorithm and differential expression analysis. In addition, drug prediction, molecular docking, ceRNA network construction, and correlation analysis with immune cell infiltration were performed to explore their potential molecular mechanisms of action in AIS. Finally, the expression of hub gene was verified by real-time PCR.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Metabolism-related genes FBL, HEATR1, HSPA8, MTMR4, NDUFC1, NDUFS8 and SNU13 were identified. The AUC values of FBL, HEATR1, HSPA8, MTMR4, NDUFS8 and SNU13 were all greater than 0.8, suggesting that they had good diagnostic accuracy. Correlation analysis found that their expression levels were also related to the infiltration levels of multiple immune cells, such as Activated.CD8.T.cell and Activated.dendritic.cell. It was found that only HSPA8 was successfully matched to drugs with literature support, and these drugs were acetaminophen, bupivacaine, dexamethasone, gentamicin, tretinoin and cisplatin. Moreover, it was also identified that the ENSG000000218510-hsa-miR-330-3p-HEATR1 axis may be involved in regulating AIS.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The identification of FBL, HEATR1, HSPA8, MTMR4, NDUFC1, NDUFS8 and SNU13 provides a new research direction for exploring the molecular mechanisms of AIS, which can help in clinical management and diagnosis.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.12095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141293868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gene signatures of endoplasmic reticulum stress and mitophagy for prognostic risk prediction in lung adenocarcinoma","authors":"Xiong Lin,&nbsp;Miaoling Yang,&nbsp;Yuanling Huang,&nbsp;Xiaoli Huang,&nbsp;Huibo Shi,&nbsp;Binbin Chen,&nbsp;Jianle Kang,&nbsp;Sunkui Ke","doi":"10.1049/syb2.12092","DOIUrl":"10.1049/syb2.12092","url":null,"abstract":"<p>Genes associated with endoplasmic reticulum stress (ERS) and mitophagy can be conducive to predicting solid tumour prognosis. The authors aimed to develop a prognosis prediction model for these genes in lung adenocarcinoma (LUAD). Relevant gene expression and clinical information were collected from public databases including Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). A total of 265 differentially expressed genes was finally selected (71 up-regulated and 194 downregulated) in the LUAD dataset. Among these, 15 candidate ERS and mitophagy genes (<i>ATG12, CSNK2A1, MAP1LC3A, MAP1LC3B, MFN2, PGAM5, PINK1, RPS2</i>7A<i>, SQSTM1, SRC, UBA52, UBB, UBC, ULK1</i>, and <i>VDAC1</i>) might be critical to LUAD based on the expression analysis after crossing with the ERS and mitochondrial autophagy genes. The prediction model demonstrated the ability to effectively predict the 5-, 3-, and 1-year prognoses of LUAD patients in both GEO and TCGA databases. Moreover, high VDAC1 expression was associated with poor overall survival in LUAD (<i>p</i> &lt; 0.001), suggesting it might be a critical gene for LUAD prognosis prediction. Overall, the prognosis model based on ERS and mitophagy genes in LUAD can be useful for evaluating the prognosis of patients with LUAD, and VDAC1 may serve as a promising biomarker for LUAD prognosis.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.12092","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141176637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adjusting laser power to control the heat generated by nanoparticles at the site of a patient's cells","authors":"Seyed Ehsan Razavi,&nbsp;Hamed Khodadadi,&nbsp;Masoud Goharimanesh","doi":"10.1049/syb2.12093","DOIUrl":"10.1049/syb2.12093","url":null,"abstract":"<p>Cancer treatment often involves heat therapy, commonly administered alongside chemotherapy and radiation therapy. The authors address the challenges posed by heat treatment methods and introduce effective control techniques. These approaches enable the precise adjustment of laser radiation over time, ensuring the tumour's core temperature attains an acceptable level with a well-defined transient response. In these control strategies, the input is the actual tumour temperature compared to the desired value, while the output governs laser radiation power. Efficient control methods are explored for regulating tumour temperature in the presence of nanoparticles and laser radiation, validated through simulations on a relevant physiological model. Initially, a Proportional-Integral-Derivative (PID) controller serves as the foundational compensator. The PID controller parameters are optimised using a combination of trial and error and the Imperialist Competitive Algorithm (ICA). ICA, known for its swift convergence and reduced computational complexity, proves instrumental in parameter determination. Furthermore, an intelligent controller based on an artificial neural network is integrated with the PID controller and compared against alternative methods. Simulation results underscore the efficacy of the combined neural network-PID controller in achieving precise temperature control. This comprehensive study illuminates promising avenues for enhancing heat therapy's effectiveness in cancer treatment.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.12093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141094226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}