IET Systems Biology最新文献

{"title":"Analysis of type 2 diabetes mellitus-related genes by constructing the pathway-based weighted network","authors":"Xue-Yan Zhang,&nbsp;Chuan-Yun Xu,&nbsp;Ke-Fei Cao,&nbsp;Hong Luo,&nbsp;Xu-Sheng Zhang","doi":"10.1049/syb2.12110","DOIUrl":"10.1049/syb2.12110","url":null,"abstract":"<p>Complex network is an effective approach to studying complex diseases, and provides another perspective for understanding their pathological mechanisms by illustrating the interactions between various factors of diseases. Type 2 diabetes mellitus (T2DM) is a complex polygenic metabolic disease involving genetic and environmental factors. By combining the complex network approach with biological data, this study constructs a pathway-based weighted network model of T2DM-related genes to explore the interrelationships between genes, here a weight is assigned to each edge in terms of the number of the same pathways in which the two nodes (genes) connected to the edge are involved. The edge weights can reflect differences in the strength of connections (interactions) between nodes (genes), which intuitively reflect the extent of biological correlations between genes and contribute to the importance of the nodes. Analysis of statistical and topological characteristics shows that the edge weights are correlated to the network topology, and the edge weight distribution decays as a power-law. The disparity of the weights indicates that the edge weight distribution for the nodes with the same degree is of approximately equal weights; and most edges with the higher weights tend to connect with the higher degree nodes. To determine the key hub genes of the weighted network, an integrated ranking index is used to comprehensively reflect the contribution of the three indices (strength, degree and number of pathways) of nodes; by taking the threshold of integrated ranking index greater than 0.56, 12 key hub genes are identified: <i>MAPK1</i>, <i>PIK3CD</i>, <i>PIK3CA</i>, <i>PIK3R1</i>, <i>AKT2</i>, <i>AKT1</i>, <i>KRAS</i>, <i>TNF</i>, <i>MAPK8</i>, <i>PRKCA</i>, <i>IL6</i> and <i>MTOR</i>. These genes should play an important role in the occurrence and development of T2DM, and can be regarded as potential therapeutic targets for further biological and medical research on their functions in T2DM. It can be expected that combining complex network approach with other data analysis techniques can provide more clues for exploring the pathogenesis and treatment of T2DM and other complex diseases in the future.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.12110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814845","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 of CCR7 and CBX6 as key biomarkers in abdominal aortic aneurysm: Insights from multi-omics data and machine learning analysis","authors":"Xi Yong,&nbsp;Xuerui Hu,&nbsp;Tengyao Kang,&nbsp;Yanpiao Deng,&nbsp;Sixuan Li,&nbsp;Shuihan Yu,&nbsp;Yani Hou,&nbsp;Jin You,&nbsp;Xiaohe Dai,&nbsp;Jialin Zhang,&nbsp;Junjia Zhang,&nbsp;Junlin Zhou,&nbsp;Siyu Zhang,&nbsp;Jianghua Zheng,&nbsp;Qin Yang,&nbsp;Jingdong Li","doi":"10.1049/syb2.12106","DOIUrl":"10.1049/syb2.12106","url":null,"abstract":"<p>Abdominal aortic aneurysm (AAA) is a severe vascular condition, marked by the progressive dilation of the abdominal aorta, leading to rupture if untreated. The objective of this study was to identify key biomarkers and decipher the immune mechanisms underlying AAA utilising multi-omics data analysis and machine learning techniques. Single-cell RNA sequencing disclosed a heightened presence of macrophages and CD8-positive alpha-beta T cells in AAA, highlighting their critical role in disease pathogenesis. Analysis of cell–cell communication highlighted augmented interactions between macrophages and dendritic cells derived from monocytes. Enrichment analysis of differential expression gene indicated substantial involvement of immune and metabolic pathways in AAA pathogenesis. Machine learning techniques identified CCR7 and CBX6 as key candidate biomarkers. In AAA, CCR7 expression is upregulated, whereas CBX6 expression is downregulated, both showing significant correlations with immune cell infiltration. These findings provide valuable insights into the molecular mechanisms underlying AAA and suggest potential biomarkers for diagnosis and therapeutic intervention.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"18 6","pages":"250-260"},"PeriodicalIF":1.9,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665846/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741142","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 of co-localised transcription factors based on paired motifs analysis","authors":"Li Liu,&nbsp;Lu Han,&nbsp;Kaiyuan Han,&nbsp;Zheng Zhang,&nbsp;Haojiang Zhang,&nbsp;Lirong Zhang","doi":"10.1049/syb2.12104","DOIUrl":"10.1049/syb2.12104","url":null,"abstract":"<p>The interaction of transcription factors (TFs) with DNA precisely regulates gene transcription. In mammalian cells, thousands of TFs often interact with DNA <i>cis</i>-regulatory elements in a combinatorial manner rather than act alone. The identification of cooperativity between TFs can help to explore the mechanism of transcriptional regulation. However, little is known about the cooperative patterns of TFs in the genome. To identify which TFs prefer co-localisation, the authors conducted a paired motif analysis in the accessible regions of the human genome based on the Poisson background model. Especially, the authors distinguish the cooperative binding TFs and the competitive binding TFs according to the distance between TF motifs. In the K562 cell line, the authors find that TFs from a same family are always competing the same binding sites, such as FOS_JUN family, whereas KLF family TFs show significant cooperative binding in the adjacency region. Furthermore, the comparative analysis across 16 human cell lines indicates that most TF combination patterns are conserved, but there are still some cell-line-specific patterns. Finally, in human prostate cancer cells (PC-3) and human prostate normal cells (RWPE-2), the authors investigate the specific TF combination patterns in the disease cell and normal cell. The results show that the cooperative binding TF pairs shared by PC-3 and RWPE-2 account for over 90%. Simultaneously, the authors also identify 26 specific TF combination pairs in PC-3 cancer cells.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"18 6","pages":"238-249"},"PeriodicalIF":1.9,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142717601","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":"DDANet: A deep dilated attention network for intracerebral haemorrhage segmentation","authors":"Haiyan Liu,&nbsp;Yu Zeng,&nbsp;Hao Li,&nbsp;Fuxin Wang,&nbsp;Jianjun Chang,&nbsp;Huaping Guo,&nbsp;Jian Zhang","doi":"10.1049/syb2.12103","DOIUrl":"10.1049/syb2.12103","url":null,"abstract":"<p>Intracranial haemorrhage (ICH) is an urgent and potentially fatal medical condition caused by brain blood vessel rupture, leading to blood accumulation in the brain tissue. Due to the pressure and damage it causes to brain tissue, ICH results in severe neurological impairment or even death. Recently, deep neural networks have been widely applied to enhance the speed and precision of ICH detection yet they are still challenged by small or subtle hemorrhages. The authors introduce DDANet, a novel haematoma segmentation model for brain CT images. Specifically, a dilated convolution pooling block is introduced in the intermediate layers of the encoder to enhance feature extraction capabilities of middle layers. Additionally, the authors incorporate a self-attention mechanism to capture global semantic information of high-level features to guide the extraction and processing of low-level features, thereby enhancing the model's understanding of the overall structure while maintaining details. DDANet also integrates residual networks, channel attention, and spatial attention mechanisms for joint optimisation, effectively mitigating the severe class imbalance problem and aiding the training process. Experiments show that DDANet outperforms current methods, achieving the Dice coefficient, Jaccard index, sensitivity, accuracy, and a specificity of 0.712, 0.601, 0.73, 0.997, and 0.998, respectively. The code is available at https://github.com/hpguo1982/DDANet.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"18 6","pages":"285-297"},"PeriodicalIF":1.9,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142711849","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":"Human essential gene identification based on feature fusion and feature screening","authors":"Zhao-Yue Zhang,&nbsp;Yue-Er Fan,&nbsp;Cheng-Bing Huang,&nbsp;Meng-Ze Du","doi":"10.1049/syb2.12105","DOIUrl":"10.1049/syb2.12105","url":null,"abstract":"<p>Essential genes are necessary to sustain the life of a species under adequate nutritional conditions. These genes have attracted significant attention for their potential as drug targets, especially in developing broad-spectrum antibacterial drugs. However, studying essential genes remains challenging due to their variability in specific environmental conditions. In this study, the authors aim to develop a powerful prediction model for identifying essential genes in humans. The authors first obtained the essential gene data from human cancer cell lines and characterised gene sequences using 7 feature encoding methods such as Kmer, the Composition of K-spaced Nucleic Acid Pairs, and Z-curve. Subsequently, feature fusion and feature optimisation strategies were employed to select the impactful features. Finally, machine learning algorithms were applied to construct the prediction models and evaluate their performance. The single-feature-based model achieved the highest area under the Receiver Operating Characteristic curve (AUC) of 0.830. After fusing and filtering these features, the classical machine learning models achieved the highest AUC at 0.823 while the deep learning model reached 0.860. Results obtained by the authors show that compared to using individual features, feature fusion and feature optimisation strategies significantly improved model performance. Moreover, the study provided an advantageous method for essential gene identification compared to other methods.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"18 6","pages":"227-237"},"PeriodicalIF":1.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142693464","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":"Inference and analysis of cell-cell communication of non-myeloid circulating cells in late sepsis based on single-cell RNA-seq","authors":"Yanyan Tao,&nbsp;Miaomiao Li,&nbsp;Cheng Liu","doi":"10.1049/syb2.12109","DOIUrl":"10.1049/syb2.12109","url":null,"abstract":"<p>Sepsis is a severe systemic inflammatory syndrome triggered by infection and is a leading cause of morbidity and mortality in intensive care units (ICUs). Immune dysfunction is a hallmark of sepsis. In this study, the authors investigated cell-cell communication among lymphoid-derived leucocytes using single-cell RNA sequencing (scRNA-seq) to gain a deeper understanding of the underlying mechanisms in late-stage sepsis. The authors’ findings revealed that both the number and strength of cellular interactions were elevated in septic patients compared to healthy individuals, with several pathways showing significant alterations, particularly in conventional dendritic cells (cDCs) and plasmacytoid dendritic cells (pDCs). Notably, pathways such as CD6-ALCAM were more activated in sepsis, potentially due to T cell suppression. This study offers new insights into the mechanisms of immunosuppression and provides potential avenues for clinical intervention in sepsis.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"18 6","pages":"218-226"},"PeriodicalIF":1.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665843/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142693570","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":"siRNAEfficacyDB: An experimentally supported small interfering RNA efficacy database","authors":"Yang Zhang,&nbsp;Ting Yang,&nbsp;Yu Yang,&nbsp;Dongsheng Xu,&nbsp;Yucheng Hu,&nbsp;Shuo Zhang,&nbsp;Nanchao Luo,&nbsp;Lin Ning,&nbsp;Liping Ren","doi":"10.1049/syb2.12102","DOIUrl":"10.1049/syb2.12102","url":null,"abstract":"<p>Small interfering RNA (siRNA) has revolutionised biomedical research and drug development through precise post-transcriptional gene silencing technology. Despite its immense potential, siRNA therapy still faces technical challenges, such as delivery efficiency, targeting specificity, and molecular stability. To address these challenges and facilitate siRNA drug development, we have developed siRNAEfficacyDB, a comprehensive database that integrates experimentally validated siRNA efficacy data. This database contains 3544 siRNA records, covering 42 target genes and 5 cell lines. It provides detailed information on siRNA sequences, target genes, inhibition efficiencies, experimental techniques, cell lines, siRNA concentrations, and incubation times. siRNAEfficacyDB offers a user-friendly web interface that makes it easy to query, browse and analyse data, enabling efficient access to siRNA-related information. In summary, siRNAEfficacyDB provides a useful data foundation for siRNA drug design and optimisation, serving as a valuable resource for advancing computer-aided siRNA design research and nucleic acid drug development. siRNAEfficacyDB is freely available at https://cellknowledge.com.cn/siRNAEfficacy for non-commercial use.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"18 6","pages":"199-207"},"PeriodicalIF":1.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631563","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":"Deep-GB: A novel deep learning model for globular protein prediction using CNN-BiLSTM architecture and enhanced PSSM with trisection strategy","authors":"Sonia Zouari,&nbsp;Farman Ali,&nbsp;Atef Masmoudi,&nbsp;Sarah Abu Ghazalah,&nbsp;Wajdi Alghamdi,&nbsp;Faris A. Kateb,&nbsp;Nouf Ibrahim","doi":"10.1049/syb2.12108","DOIUrl":"10.1049/syb2.12108","url":null,"abstract":"<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":"18 6","pages":"208-217"},"PeriodicalIF":1.9,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665838/pdf/","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":"OA","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,&nbsp;Naael H. Alazwary,&nbsp;Waleed A. Alsowayan,&nbsp;Mohmmed Algamdi,&nbsp;Ahmed F. Alohali,&nbsp;Mustafa A. Yasawy,&nbsp;Abeer M. Alghamdi,&nbsp;Abdullah M. Alassaf,&nbsp;Mohammed R. Alshehri,&nbsp;Hussein A. Aljaziri,&nbsp;Nujoud H. Almoqati,&nbsp;Shatha S. Alghamdi,&nbsp;Norah A. Bin Magbel,&nbsp;Tareq A. AlMazeedi,&nbsp;Nashaat K. Neyazi,&nbsp;Mona M. Alghamdi,&nbsp;Mohammed N. Alazwary","doi":"10.1049/syb2.12101","DOIUrl":"10.1049/syb2.12101","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <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>\u0000 </section>\u0000 </div>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"18 6","pages":"298-317"},"PeriodicalIF":1.9,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665840/pdf/","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":"OA","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,&nbsp;Yifan Wang","doi":"10.1049/syb2.12100","DOIUrl":"10.1049/syb2.12100","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <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>\u0000 </section>\u0000 </div>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"18 6","pages":"271-284"},"PeriodicalIF":1.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665837/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}