IET Systems Biology最新文献_第9页

Comprehensive analysis of alternative splicing signatures in pancreatic head cancer 胰头癌选择性剪接特征的综合分析

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2022-12-07 DOI: 10.1049/syb2.12056

Lingshan Zhou, Yuan Yang, Jian Ma, Min Liu, Rong Liu, Xiaopeng Ma, Chengdong Qiao

{"title":"Comprehensive analysis of alternative splicing signatures in pancreatic head cancer","authors":"Lingshan Zhou, Yuan Yang, Jian Ma, Min Liu, Rong Liu, Xiaopeng Ma, Chengdong Qiao","doi":"10.1049/syb2.12056","DOIUrl":"10.1049/syb2.12056","url":null,"abstract":"The correlation between dysregulation of splicing and cancers has been increasingly recognised and confirmed. The identification of valuable alternative splicing (AS) in pancreatic head cancer (PHC) has a great significance. AS profiles in PHC were generated using the data from The Cancer Genome Atlas and TCGASpliceSeq. Then, the NMF clustering method was performed to identify overall survival-associated AS (OS-AS) subtypes in PHC patients. Subsequently, we used least absolute shrinkage and selection operator Cox regression analysis to construct an AS-related risk model. The splicing regulatory network was uncovered by Cytoscape 3.7. A total of 1694 OS-AS events were obtained. The PHC patients were divided into clusters 1 and 2. Cluster 1 had poorer prognosis and lower infiltration of immune cells. Subsequently, a prognostic signature was established that showed good performance in predicting OS and progression-free survival. The risk score of this signature was associated with the unique tumour immunity. Moreover, a nomogram incorporating the risk score and clinicopathological parameters was established. Finally, a splicing factor-AS regulatory network was developed. A comprehensive analysis of the AS events in PHC associated with prognosis and tumour immunity may help provide reliable information to guide individual treatment strategies.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"17 1","pages":"14-26"},"PeriodicalIF":2.3,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/bb/64/SYB2-17-14.PMC9931058.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10825607","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}

引用次数: 0

The effect of normalisation and error model choice on the distribution of the maximum likelihood estimator for a biochemical reaction 归一化和误差模型选择对生化反应最大似然估计量分布的影响

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2022-11-28 DOI: 10.1049/syb2.12055

Caterina Thomaseth, Nicole E. Radde

{"title":"The effect of normalisation and error model choice on the distribution of the maximum likelihood estimator for a biochemical reaction","authors":"Caterina Thomaseth, Nicole E. Radde","doi":"10.1049/syb2.12055","DOIUrl":"10.1049/syb2.12055","url":null,"abstract":"Sparse and noisy measurements make parameter estimation for biochemical reaction networks difficult and might lead to ill-posed optimisation problems. This is potentiated if the data has to be normalised, and only fold changes rather than absolute amounts are available. Here, the authors consider the propagation of measurement noise to the distribution of the maximum likelihood (ML) estimator in an in silico study. Therefore, a model of a reversible reaction is considered, for which reaction rate constants using fold changes is estimated. Noise propagation is analysed for different normalisation strategies and different error models. In particular, accuracy, precision, and asymptotic properties of the ML estimator is investigated. Results show that normalisation by the mean of a time series outperforms normalisation by a single time point in the example provided by the authors. Moreover, the error model with a heavy-tail distribution is slightly more robust to large measurement noise, but, beyond this, the choice of the error model did not have a significant impact on the estimation results provided by the authors.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"17 1","pages":"1-13"},"PeriodicalIF":2.3,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a4/92/SYB2-17-1.PMC9931059.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9076339","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}

引用次数: 0

Genes associated with diagnosis and prognosis of Burkitt lymphoma 与伯基特淋巴瘤诊断和预后相关的基因。

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2022-11-10 DOI: 10.1049/syb2.12054

Albert Doughan, Samson Pandam Salifu

{"title":"Genes associated with diagnosis and prognosis of Burkitt lymphoma","authors":"Albert Doughan, Samson Pandam Salifu","doi":"10.1049/syb2.12054","DOIUrl":"10.1049/syb2.12054","url":null,"abstract":"Burkitt lymphoma (BL) is one of the most aggressive forms of non-Hodgkin's lymphomas that affect children and young adults. The expression of genes and other molecular markers during carcinogenesis can be the basis for diagnosis, prognosis and the design of new and effective drugs for the management of cancers. The aim of this study was to identify genes that can serve as prognostic and therapeutic targets for BL. We analysed RNA-seq data of BL transcriptome sequencing projects in Africa using standard RNA-seq analyses pipeline. We performed pathway enrichment analyses, protein–protein interaction networks, gene co-expression and survival analyses. Gene and pathway enrichment analyses showed that the differentially expressed genes are involved in tube development, signalling receptor binding, viral protein interaction, cell migration, external stimuli response, serine hydrolase activity and PI3K-Akt signalling pathway. Protein–protein interaction network analyses revealed the genes to be highly interconnected, whereas module analyses revealed 25 genes to possess the highest interaction score. Overall survival analyses delineated six genes (ADAMTSL4, SEMA5B, ADAMTS15, THBS2, SPON1 and THBS1) that can serve as biomarkers for prognosis for BL management.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"16 6","pages":"220-229"},"PeriodicalIF":2.3,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9675412/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10500144","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}

引用次数: 0

Erratum: Identifying genuine protein–protein interactions within communities of gene co-expression networks using a deconvolution method 勘误:鉴定真正的蛋白质蛋白质相互作用的社区内的基因共表达网络使用反卷积方法

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2022-10-29 DOI: 10.1049/syb2.12053

引用次数: 0

Design and implementation of an adaptive fuzzy sliding mode controller for drug delivery in treatment of vascular cancer tumours and its optimisation using genetic algorithm tool 基于遗传算法的血管肿瘤药物传递自适应模糊滑模控制器的设计与实现

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2022-09-30 DOI: 10.1049/syb2.12051

Ehsan Sadeghi Ghasemabad, Iman Zamani, Hami Tourajizadeh, Mahdi Mirhadi, Zahra Goorkani Zarandi

{"title":"Design and implementation of an adaptive fuzzy sliding mode controller for drug delivery in treatment of vascular cancer tumours and its optimisation using genetic algorithm tool","authors":"Ehsan Sadeghi Ghasemabad, Iman Zamani, Hami Tourajizadeh, Mahdi Mirhadi, Zahra Goorkani Zarandi","doi":"10.1049/syb2.12051","DOIUrl":"10.1049/syb2.12051","url":null,"abstract":"In this paper, the side effects of drug therapy in the process of cancer treatment are reduced by designing two optimal non-linear controllers. The related gains of the designed controllers are optimised using genetic algorithm and simultaneously are adapted by employing the Fuzzy scheduling method. The cancer dynamic model is extracted with five differential equations, including normal cells, endothelial cells, cancer cells, and the amount of two chemotherapy and anti-angiogenic drugs left in the body as the engaged state variables, while double drug injection is considered as the corresponding controlling signals of the mentioned state space. This treatment aims to reduce the tumour cells by providing a timely schedule for drug dosage. In chemotherapy, not only the cancer cells are killed but also other healthy cells will be destroyed, so the rate of drug injection is highly significant. It is shown that the simultaneous application of chemotherapy and anti-angiogenic therapy is more efficient than single chemotherapy. Two different non-linear controllers are employed and their performances are compared. Simulation results and comparison studies show that not only adding the anti-angiogenic reduce the side effects of chemotherapy but also the proposed robust controller of sliding mode provides a faster and stronger treatment in the presence of patient parametric uncertainties in an optimal way. As a result of the proposed closed-loop drug treatment, the tumour cells rapidly decrease to zero, while the normal cells remain healthy simultaneously. Also, the injection rate of the chemotherapy drug is very low after a short time and converges to zero.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"16 6","pages":"201-219"},"PeriodicalIF":2.3,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9675414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10499624","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}

引用次数: 3

Identifying driver modules based on multi-omics biological networks in prostate cancer 基于多组学生物学网络的前列腺癌驱动模块识别

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2022-08-30 DOI: 10.1049/syb2.12050

Zhongli Chen, Biting Liang, Yingfu Wu, Haoru Zhou, Yuchen Wang, Hao Wu

{"title":"Identifying driver modules based on multi-omics biological networks in prostate cancer","authors":"Zhongli Chen, Biting Liang, Yingfu Wu, Haoru Zhou, Yuchen Wang, Hao Wu","doi":"10.1049/syb2.12050","DOIUrl":"10.1049/syb2.12050","url":null,"abstract":"The development of sequencing technology has promoted the expansion of cancer genome data. It is necessary to identify the pathogenesis of cancer at the molecular level and explore reliable treatment methods and precise drug targets in cancer by identifying carcinogenic functional modules in massive multi-omics data. However, there are still limitations to identifying carcinogenic driver modules by utilising genetic characteristics simply. Therefore, this study proposes a computational method, NetAP, to identify driver modules in prostate cancer. Firstly, high mutual exclusivity, high coverage, and high topological similarity between genes are integrated to construct a weight function, which calculates the weight of gene pairs in a biological network. Secondly, the random walk method is utilised to reevaluate the strength of interaction among genes. Finally, the optimal driver modules are identified by utilising the affinity propagation algorithm. According to the results, the authors’ method identifies more validated driver genes and driver modules compared with the other previous methods. Thus, the proposed NetAP method can identify carcinogenic driver modules effectively and reliably, and the experimental results provide a powerful basis for cancer diagnosis, treatment and drug targets.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"16 6","pages":"187-200"},"PeriodicalIF":2.3,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e8/7f/SYB2-16-187.PMC9675413.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10848807","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}

引用次数: 1

CHAC1 as a novel biomarker for distinguishing alopecia from other dermatological diseases and determining its severity CHAC1作为区分脱发与其他皮肤病及判断其严重程度的新生物标志物

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2022-08-18 DOI: 10.1049/syb2.12048

Hassan Karami, Samira Nomiri, Mohammad Ghasemigol, Niloufar Mehrvarzian, Afshin Derakhshani, Mohammad Fereidouni, Masoud Mirimoghaddam, Hossein Safarpour

{"title":"CHAC1 as a novel biomarker for distinguishing alopecia from other dermatological diseases and determining its severity","authors":"Hassan Karami, Samira Nomiri, Mohammad Ghasemigol, Niloufar Mehrvarzian, Afshin Derakhshani, Mohammad Fereidouni, Masoud Mirimoghaddam, Hossein Safarpour","doi":"10.1049/syb2.12048","DOIUrl":"10.1049/syb2.12048","url":null,"abstract":"Alopecia Areata (AA) is characterised by an autoimmune response to hair follicles (HFs) and its exact pathobiology remains unclear. The current study aims to look into the molecular changes in the skin of AA patients as well as the potential underlying molecular mechanisms of AA in order to identify potential candidates for early detection and treatment of AA. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to identify key modules, hub genes, and mRNA–miRNA regulatory networks associated with AA. Furthermore, Chi2 as a machine-learning algorithm was used to compute the gene importance in AA. Finally, drug-target construction revealed the potential of repositioning drugs for the treatment of AA. Our analysis using four AA data sets established a network strongly correlated to AA pathogenicity based on GZMA, OXCT2, HOXC13, KRT40, COMP, CHAC1, and KRT83 hub genes. Interestingly, machine learning introduced these genes as important in AA pathogenicity. Besides that, using another ten data sets, we showed that CHAC1 could clearly distinguish AA from similar clinical phenotypes, such as scarring alopecia due to psoriasis. Also, two FDA-approved drug candidates and 30 experimentally validated miRNAs were identified that affected the co-expression network. Using transcriptome analysis, suggested CHAC1 as a potential diagnostic predictor to diagnose AA.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"16 5","pages":"173-185"},"PeriodicalIF":2.3,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469792/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10831174","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}

引用次数: 1

Positive input observer-based controller design for blood glucose regulation for type 1 diabetic patients: A backstepping approach 基于正输入观测器的1型糖尿病血糖调节控制器设计:一种回溯方法

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2022-08-17 DOI: 10.1049/syb2.12049

Mohamadreza Homayounzade

{"title":"Positive input observer-based controller design for blood glucose regulation for type 1 diabetic patients: A backstepping approach","authors":"Mohamadreza Homayounzade","doi":"10.1049/syb2.12049","DOIUrl":"10.1049/syb2.12049","url":null,"abstract":"In practice, there are many physical systems that can have only positive inputs, such as physiological systems. Most conventional control methods cannot ensure that the main system input is positive. A positive input observer-based controller is designed for an intravenous glucose tolerance test model of type 1 diabetes mellitus (T1DM). The backstepping (BS) approach is employed to design the feedback controller for artificial pancreas (AP) systems, based on the Extended Bergman's Minimal Model (EBMM). The EBMM represents the T1DM in terms of the blood glucose concentration (BGC), insulin concentration, and plasma level and the disturbance of insulin during medication due to either meal intake or burning sugar by doing some physical exercise. The insulin concentration and plasma level are estimated using observers, and these estimations are applied as feedback to the controller. The asymptotic stability of the observer-based controller is proved using the Lyapunov theorem. Moreover, it is proved that the system is bounded input-bounded output (BIBO) stable in the presence of uncertainties generated by uncertain parameters and external disturbance. For realistic situations, we consider only the BGC to be available for measurement and additionally inter-and intra-patient variability of system parameters is considered.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"16 5","pages":"157-172"},"PeriodicalIF":2.3,"publicationDate":"2022-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/36/21/SYB2-16-157.PMC9469794.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40704075","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}

引用次数: 2

Deep learning-based microarray cancer classification and ensemble gene selection approach 基于深度学习的微阵列癌症分类和集合基因选择方法

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2022-07-04 DOI: 10.1049/syb2.12044

Khosro Rezaee, Gwanggil Jeon, Mohammad R. Khosravi, Hani H. Attar, Alireza Sabzevari

{"title":"Deep learning-based microarray cancer classification and ensemble gene selection approach","authors":"Khosro Rezaee, Gwanggil Jeon, Mohammad R. Khosravi, Hani H. Attar, Alireza Sabzevari","doi":"10.1049/syb2.12044","DOIUrl":"10.1049/syb2.12044","url":null,"abstract":"Malignancies and diseases of various genetic origins can be diagnosed and classified with microarray data. There are many obstacles to overcome due to the large size of the gene and the small number of samples in the microarray. A combination strategy for gene expression in a variety of diseases is described in this paper, consisting of two steps: identifying the most effective genes via soft ensembling and classifying them with a novel deep neural network. The feature selection approach combines three strategies to select wrapper genes and rank them according to the k-nearest neighbour algorithm, resulting in a very generalisable model with low error levels. Using soft ensembling, the most effective subsets of genes were identified from three microarray datasets of diffuse large cell lymphoma, leukaemia, and prostate cancer. A stacked deep neural network was used to classify all three datasets, achieving an average accuracy of 97.51%, 99.6%, and 96.34%, respectively. In addition, two previously unreported datasets from small, round blue cell tumors (SRBCTs)and multiple sclerosis-related brain tissue lesions were examined to show the generalisability of the model method.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"16 3-4","pages":"120-131"},"PeriodicalIF":2.3,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c5/71/SYB2-16-120.PMC9290776.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40585256","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}

引用次数: 19

Identification of TNFAIP6 as a hub gene associated with the progression of glioblastoma by weighted gene co-expression network analysis 通过加权基因共表达网络分析确定TNFAIP6是胶质母细胞瘤进展相关的枢纽基因

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2022-06-29 DOI: 10.1049/syb2.12046

Dongdong Lin, Wei Li, Nu Zhang, Ming Cai

{"title":"Identification of TNFAIP6 as a hub gene associated with the progression of glioblastoma by weighted gene co-expression network analysis","authors":"Dongdong Lin, Wei Li, Nu Zhang, Ming Cai","doi":"10.1049/syb2.12046","DOIUrl":"10.1049/syb2.12046","url":null,"abstract":"This study aims to discover the genetic modules that distinguish glioblastoma multiforme (GBM) from low-grade glioma (LGG) and identify hub genes. A co-expression network is constructed using the expression profiles of 28 GBM and LGG patients from the Gene Expression Omnibus database. The authors performed gene ontology (GO) and Kyoto encyclopaedia of genes and genomes (KEGG) analysis on these genes. The maximal clique centrality method was used to identify hub genes. Online tools were employed to confirm the link between hub gene expression and overall patient survival rate. The top 5000 genes with major variance were classified into 18 co-expression gene modules. GO analysis indicated that abnormal changes in ‘cell migration’ and ‘collagen metabolic process’ were involved in the development of GBM. KEGG analysis suggested that ‘focal adhesion’ and ‘p53 signalling pathway’ regulate the tumour progression. TNFAIP6 was identified as a hub gene, and the expression of TNFAIP6 was increased with the elevation of pathological grade. Survival analysis indicated that the higher the expression of TNFAIP6, the shorter the survival time of patients. The authors identified TNFAIP6 as the hub gene in the progression of GBM, and its high expression indicates the poor prognosis of the patients.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"16 5","pages":"145-156"},"PeriodicalIF":2.3,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40404350","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}

引用次数: 1