IET Systems Biology最新文献_第2页

Functionalist Oncology to Model the Contextuality of Dynamics and Treatment in Acute Myeloid Leukaemia 功能肿瘤学模拟急性髓性白血病动力学和治疗的背景。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2026-06-01 DOI: 10.1049/syb2.70073

Alexander Ehmann, Rakan Naboulsi, Martin Jädersten, Sylvain Tollis, Nikolas Herold

{"title":"Functionalist Oncology to Model the Contextuality of Dynamics and Treatment in Acute Myeloid Leukaemia","authors":"Alexander Ehmann, Rakan Naboulsi, Martin Jädersten, Sylvain Tollis, Nikolas Herold","doi":"10.1049/syb2.70073","DOIUrl":"10.1049/syb2.70073","url":null,"abstract":"Acute myeloid leukaemia (AML) is a disease with high intra- and interpatient heterogeneity. Current treatments are still based on cytarabine combined with an anthracycline, with 5-year overall survival rates below 30%. Functionalist oncology utilises edge-weighted digraphs as a representational tool to model the mathematical functional interdependencies of disease factors. This methodology enables a conceptual and mechanistic analysis of key factors influencing therapeutic success, providing a framework that can be parameterised to explore patient-specific treatment responses. It incorporates the oligoclonal nature of AML to perform, in principle, comprehensive cost-benefit analyses regarding the addition of individual drugs to existing treatment regimens and the number and type of chemotherapy courses to be given. Extrapolating from experimental data, we investigate the therapeutic potential and risks of SAMHD1 inhibitors in AML treatment as a proof-of-concept. We also provide a web-based interactive application to visualise hypothetical AML treatment scenarios, which can be combined with ex vivo single-cell phenotypic (gene expression), genotypic (somatic mutations) and functional (drug responses) analyses. Functionalist oncology can thus be used to generate testable hypotheses that might contribute to improving oncological decision-making, for example, by identifying the optimal number, nature and sequence of chemotherapy courses, including both existing and novel drugs, such as SAMHD1 inhibitors.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148145352","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

Fractional Order Total Variation Low-Rank Representation on Single-Cell RNA Sequencing Clustering 单细胞RNA序列聚类的分数阶全变异低秩表示。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2026-05-29 DOI: 10.1049/syb2.70070

Pengcheng Yang, Fei Lu, Qianwen Xue, Weimin Ma, Qianwen Liu, Qiang Li, Yulin Zhang, Xiaochun Cheng

{"title":"Fractional Order Total Variation Low-Rank Representation on Single-Cell RNA Sequencing Clustering","authors":"Pengcheng Yang, Fei Lu, Qianwen Xue, Weimin Ma, Qianwen Liu, Qiang Li, Yulin Zhang, Xiaochun Cheng","doi":"10.1049/syb2.70070","DOIUrl":"10.1049/syb2.70070","url":null,"abstract":"Traditional bulk RNA sequencing often masks cell-to-cell variability, leading to a loss of individual heterogeneity information. Single-cell RNA sequencing (scRNA-seq) preserves cellular heterogeneity by reverse-transcribing, amplifying, as well as sequencing mRNA molecules from individual cells, enabling in-depth studies of cell development, differentiation, and disease mechanisms. However, scRNA-seq data are inherently high-dimensional and noisy with prevalent dropout events, posing challenges for accurate clustering and subtype identification. To address these issues, this study proposes an Adaptive Fractional-Order Total Variation Regularised Low-Rank Representation (AFTV-LRR) model that integrates adaptive fractional-order total variation into the low-rank representation framework. The proposed method reconstructs low-rank subspace structures to learn cell similarities while preserving fine-grained cellular features through fractional-order gradient learning. The optimisation problem is efficiently solved using the Alternating Direction Method of Multipliers (ADMM), and spectral clustering is applied to the learnt similarity matrix for accurate cell type identification. Extensive experiments on 11 publicly available scRNA-seq datasets demonstrate that AFTV-LRR achieves competitive and often superior performance compared with eight representative single-cell clustering algorithms in terms of Adjusted Rand Index (ARI) and Normalised Mutual Information (NMI). Visualisation with t-SNE further confirms that the proposed model yields clearer inter-cluster separations and higher intra-cluster compactness. Moreover, marker gene analysis using the mouse embryo dataset supports the biological interpretability and robustness of the clustering results. Overall, this work provides an adaptive computational framework for improving the accuracy and reliability of single-cell clustering analysis.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148058206","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

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2026-05-14

引用次数: 0

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2026-05-13

引用次数: 0

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2026-04-22

引用次数: 0

Exploring the Power of Machine Learning in Analysing Protein–Protein Sequences 探索机器学习在分析蛋白质-蛋白质序列中的力量。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2026-04-22 DOI: 10.1049/syb2.70066

Anindya Nag, Riya Sil, Md. Mehedi Hassan, Tamanna Haque Ritu, Biva Das, Nilanjana Roy, Anurag Sinha, Dron Guin, Nishchal Maurya

{"title":"Exploring the Power of Machine Learning in Analysing Protein–Protein Sequences","authors":"Anindya Nag, Riya Sil, Md. Mehedi Hassan, Tamanna Haque Ritu, Biva Das, Nilanjana Roy, Anurag Sinha, Dron Guin, Nishchal Maurya","doi":"10.1049/syb2.70066","DOIUrl":"10.1049/syb2.70066","url":null,"abstract":"Proteins are fundamental biological macromolecules responsible for regulating nearly all cellular processes, and their functions are largely determined by the underlying amino acid sequences. Understanding the relationship between protein sequences and their structural and functional properties remains a major challenge in molecular biology. Although experimental techniques provide accurate insights, they are often time-consuming and expensive, which has led to increasing reliance on computational approaches for protein sequence analysis and structure prediction. Numerous techniques—including homology modelling, machine learning, deep learning, natural language processing and other artificial intelligence methods—have been developed for analysing protein sequences and predicting protein–protein interactions. However, the rapidly growing number of computational methods makes it difficult for researchers to systematically evaluate and select suitable approaches for specific biological applications. This study presents a comprehensive review of recent computational techniques for protein sequence analysis, focusing on machine learning- and deep learning-based frameworks used for predicting protein structure and interactions. This review systematically categorises existing approaches based on their methodological foundations, datasets and performance characteristics and provides a comparative discussion of their advantages and limitations. Furthermore, this study highlights current research gaps, challenges in large-scale protein analysis and emerging trends in AI-driven protein modelling. The findings of this review provide a structured reference for researchers and practitioners, facilitating the selection of appropriate computational techniques and supporting future advancements in protein sequence analysis, disease research and drug discovery.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13102167/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147787937","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

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2026-04-15

引用次数: 0

Research of the Abnormal Mechanism of Brain Functional Network in Early Mild Cognitive Impairment Patients 早期轻度认知障碍患者脑功能网络异常机制的研究。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2026-04-15 DOI: 10.1049/syb2.70063

Dong Qi, Weiping Wang, Yapeng Diao, Wenxiu Zhao, Wenqin Rong, Haiyan Zhao

{"title":"Research of the Abnormal Mechanism of Brain Functional Network in Early Mild Cognitive Impairment Patients","authors":"Dong Qi, Weiping Wang, Yapeng Diao, Wenxiu Zhao, Wenqin Rong, Haiyan Zhao","doi":"10.1049/syb2.70063","DOIUrl":"10.1049/syb2.70063","url":null,"abstract":"At present, the brain neural mechanism of the onset of early mild cognitive impairment (EMCI) patients has not yet been clarified, and it is difficult to detect the pre-disease stage of EMCI patients and to carry out the subsequent intervention treatment. In this paper, by constructing the brain functional networks of the two subject groups of EMCI and Normal Control (NC), the mechanism of abnormalities in the brain functional networks and brain regions of the patients was investigated. Using the analysis method of complex network combined with graph theory, the global topological metrics of the brain functional networks of the subjects were investigated. Considering the compensatory mechanism of brain regions, this study starts with more fine-grained brain regions to analyse the differential characteristics of brain functional networks between patients and normal subjects. Finally, we draw the preliminary conclusion that the global structure of the brain functional network of EMCI patients has been fundamentally reorganised, and the working mechanism has been abnormally changed, and we have unearthed the abnormal brain regions that cause the global changes of the brain functional network.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13084192/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147693247","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

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2026-04-10

引用次数: 0

Utilising Machine Learning and Single-Cell Analysis to Uncover SKCM Metastasis–Related Genes 利用机器学习和单细胞分析揭示SKCM转移相关基因。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2026-04-10 DOI: 10.1049/syb2.70061

Zhiwei Liao, Weiming Chen, Yingdi He, Yichen Zheng, Xiaonan Chen, Han Shen

{"title":"Utilising Machine Learning and Single-Cell Analysis to Uncover SKCM Metastasis–Related Genes","authors":"Zhiwei Liao, Weiming Chen, Yingdi He, Yichen Zheng, Xiaonan Chen, Han Shen","doi":"10.1049/syb2.70061","DOIUrl":"10.1049/syb2.70061","url":null,"abstract":"The high mortality rate of metastatic cutaneous melanoma (SKCM) remains a major challenge in clinical treatment. This study used single-cell RNA sequencing (scRNA-Seq) technology to compare the differences between metastatic and primary tumour cells. By manually annotating cell types, significant disparities in cell communication patterns and functional pathways between the two groups were identified. Combined with transcriptomic data, differential gene analysis was performed to screen out a core gene set associated with tumour metastasis. To achieve accurate prediction of tumour metastasis, this study innovatively constructed a binary classification algorithm (PSO–SVM) integrating particle swarm optimisation (PSO) and support vector machines (SVMs). This model optimises SVM parameters via the PSO algorithm, addressing the limitations of traditional machine learning models such as insufficient accuracy and poor generalization ability in tumour metastasis prediction. Verified by comparison with mainstream machine learning methods, the PSO–SVM model exhibited superior classification performance and successfully identified five key metastasis-related genes: SFN, S100A8, KLF5, ARL4D and TINCR. Furthermore, the expression differences of these genes in the metastatic group were verified at the single-cell level, clarifying their regulatory roles in different cell types and states. Through an innovative analytical strategy integrating single-cell and transcriptomic data, this study elucidated the core molecular mechanisms of SKCM metastasis and key regulatory pathways in the tumour microenvironment, providing potential biomarkers and therapeutic targets for the early diagnosis and targeted treatment of SKCM metastasis. This PSO–SVM–integrated analysis method also offers new insights for research on metastasis mechanisms of other cancers.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13068296/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147655113","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