Current Bioinformatics最新文献

{"title":"Research on the Mechanism of Traditional Chinese Medicine Treatment for Diseases caused by Human Coronavirus COVID-19","authors":"Xian-Fang Wang, Chong-Yang Ma, Zhi-Yong Du, Yi-Feng Liu, Shao-Hui Ma, Sang Yu, Rui-xia Jin, Dong-qing Wei","doi":"10.2174/0115748936292599240308102616","DOIUrl":"https://doi.org/10.2174/0115748936292599240308102616","url":null,"abstract":"Background: Human coronaviruses are a large group of viruses that exist widely in nature and multiply through self-replication. Due to its suddenness and variability, it poses a great threat to global human health and is a major problem currently faced by the medical and health fields. background: Human coronaviruses are a large group of viruses that exist widely in nature and multiply through self-replication. Due to its suddenness and variability, it poses a great threat to global human health and is a major problem currently faced by the medical and health fields. Objective: COVID-19 is the seventh known coronavirus that can infect humans. The main purpose of this paper is to analyze the effective components and action targets of the Longyi Zhengqi formula and Lianhua Qingwen formula, study their mechanism of action in the treatment of new coronavirus pneumonia (new coronavirus pneumonia), compare the similarities and differences of their pharmacological effects, and obtain the pharmacodynamic mechanism of the two traditional Chinese medicine compounds. Method: Obtain the effective ingredients and targets of Longyi-Zhengqi Formula and Lianhua- Qingwen Formula from ETCM (Encyclopedia of Traditional Chinese Medicine) and other traditional Chinese medicine databases, use GeneCards database to obtain the relevant targets of COVID-19, and use Cytoscape software to build the component COVID-19 target network of Longyi-Zhengqi Formula and the component COVID-19 target network of Lianhua-Qingwen Formula. STRING was used to construct a protein interaction network and screen key targets. GO (Gene Ontology) was used for enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) was used for pathways to find out the targets and pathways related to the treatment of COVID-19. Results: In the GO enrichment analysis results, there are 106 biological processes, 31 cell localization and 28 molecular functions of the intersection PPI network targets of Longyi-Zhengqi Formula- COVID-19, 224 biological processes, 51 cell localization and 55 molecular functions of the intersection PPI network targets of Lianhua-Qingwen Formula-COVID-19. In the KEGG pathway analysis results, the number of targets of Longyi-Zhengqi Formula on the COVID-19 pathway is 7, and the number of targets of Lianhua-Qingwen Formula on the COVID-19 pathway is 19; In the regulation analysis results, Longyi-Zhengqi Formula achieves the effect of treating COVID-19 by regulating IL-6, and Lianhua-Qingwen Formula achieves the effect of treating pneumonia by regulating TLR4. Conclusion: This paper explores the mechanism of action of Longyi-Zhengqi Formula and Lianhua-Qingwen Formula in treating COVID-19 based on the method of network pharmacology, and provides a theoretical basis for traditional Chinese medicine to treat sudden diseases caused by human coronavirus in terms of drug targets and disease interactions. It has certain practical significance.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140582836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Machine-learning Model to Classify Schizophrenia Using Methylation Data Based on Gene Expression","authors":"Karthikeyan A. Vijayakumar, Gwang-Won Cho","doi":"10.2174/0115748936293407240222113019","DOIUrl":"https://doi.org/10.2174/0115748936293407240222113019","url":null,"abstract":"Introduction: The recent advancement in artificial intelligence has compelled medical research to adapt the technologies. The abundance of molecular data and AI technology has helped in explaining various diseases, even cancers. Schizophrenia is a complex neuropsychological disease whose etiology is unknown. Several gene-wide association studies attempted to narrow down the cause of the disease but did not successfully point out the mechanism behind the disease. There are studies regarding the epigenetic changes in the schizophrenia disease condition, and a classification machine-learning model has been trained using the blood methylation data. Method: In this study, we have demonstrated a novel approach to elucidating the molecular cause of the disease. We used a two-step machine-learning approach to determine the causal molecular markers. By doing so, we developed classification models using both gene expression microarray and methylation microarray data. Result: Our models, because of our novel approach, achieved good classification accuracy with the available data size. We analyzed the important features, and they add up as evidence for the glutamate hypothesis of schizophrenia. Conclusion: In this way, we have demonstrated explaining a disease through machine learning models.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Extended Feature Representation Technique for Predicting Sequenced-based Host-pathogen Protein-protein Interaction","authors":"Jerry Emmanuel, Itunuoluwa Isewon, Grace Olasehinde, Jelili Oyelade","doi":"10.2174/0115748936286848240108074303","DOIUrl":"https://doi.org/10.2174/0115748936286848240108074303","url":null,"abstract":"Background: The use of machine learning models in sequence-based Protein-Protein Interaction prediction typically requires the conversion of amino acid sequences into feature vectors. From the literature, two approaches have been used to achieve this transformation. These are referred to as the Independent Protein Feature (IPF) and Merged Protein Feature (MPF) extraction methods. As observed, studies have predominantly adopted the IPF approach, while others preferred the MPF method, in which host and pathogen sequences are concatenated before feature encoding. Objective: This presents the challenge of determining which approach should be adopted for improved HPPPI prediction. Therefore, this work introduces the Extended Protein Feature (EPF) method. Methods: The proposed method combines the predictive capabilities of IPF and MPF, extracting essential features, handling multicollinearity, and removing features with zero importance. EPF, IPF, and MPF were tested using bacteria, parasite, virus, and plant HPPPI datasets and were deployed to machine learning models, including Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Naïve Bayes (NB), Logistic Regression (LR), and Deep Forest (DF). Results: The results indicated that MPF exhibited the lowest performance overall, whereas IPF performed better with decision tree-based models, such as RF and DF. In contrast, EPF demonstrated improved performance with SVM, LR, NB, and MLP and also yielded competitive results with DF and RF. Conclusion: In conclusion, the EPF approach developed in this study exhibits substantial improvements in four out of the six models evaluated. This suggests that EPF offers competitiveness with IPF and is particularly well-suited for traditional machine learning models.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relational Graph Convolution Network with Multi Features for AntiCOVID-19 Drugs Discovery using 3CLpro Potential Target","authors":"Medard Edmund Mswahili, Goodwill Erasmo Ndomba, Young Jin Kim, Kyuri Jo, Young-Seob Jeong","doi":"10.2174/0115748936280392240219054047","DOIUrl":"https://doi.org/10.2174/0115748936280392240219054047","url":null,"abstract":"Background: The potential of graph neural networks (GNNs) to revolutionize the analysis of non-Euclidean data has gained attention recently, making them attractive models for deep machine learning. However, insufficient compound or moleculargraphs and feature representations might significantly impair and jeopardize their full potential. Despite the devastating impacts of ongoing COVID-19 across the globe, for which there is no drug with proven efficacy that has been shown tobe effective. As various stages of drug discovery and repositioning require the accurate prediction of drugtarget interactions(DTI), here, we propose a relational graph convolution network using multi-features based on the developed drug chemicalcompound-coronavirus target graph representation and combination of features. During the implementation of the model, we further introduced the use of not only the feature module to understand the topological structure of drugs but also the structure of the proven drug target (i.e., 3CLpro) for SARS-Cov-2 that shares a genome sequence similar to that of other members of the beta-coronavirus group such as SARS-Cov, MERS-CoV, bat coronavirus. Our feature comprises topologicalinformation in molecular SMILES and local chemical context in the SMILES sequence for the drug chemical compound and drug target. Our proposed method prevailed with high and compelling performance accuracy of 97.30% which could beprioritized as the potential and promising prediction route for the development of novel oral antiviral medicine for COVID-19 drugs. Objective: Forecasting DTI stands as a pivotal aspect of drug discovery. The focus on computational methods in DTI prediction has intensified due to the considerable expense and time investment associated with conducting extensive in vitro and in vivo experiments. Machine learning techniques, particularly deep learning, have found broad applications in DTI prediction. We are convinced that this study could be prioritized and utilized as the promising predictive route for the development of novel oral antiviral treatments for COVID-19 and other variants of coronaviruses. Methods: This study addressed the problem of COVID-19 drugs using proposed RGCN with multifeatures as an attractive and potential route. This study focused mainly on the prediction of novel antiviral drugs against coronaviruses using graph-based methodology, namely RGCN. This research further utilized the features of both drugs and common potential drug targets found in betacoronaviruses group to deepen understanding of their underlying relation. Results: Our suggested approach prevailed with a high and convincing performance accuracy of 97.30%, which may be utilizedas a top priority to support and advance this field in the prediction and development of novel antiviral treatments against coronaviruses and their variants. Conclusion: We recursively performed experiments using the proposed method on our constructed DCCCvT graph dataset from our c","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MCHAN: Prediction of Human Microbe-drug Associations Based on Multiview Contrastive Hypergraph Attention Network","authors":"Guanghui Li, Ziyan Cao, Cheng Liang, Qiu Xiao, Jiawei Luo","doi":"10.2174/0115748936288616240212073805","DOIUrl":"https://doi.org/10.2174/0115748936288616240212073805","url":null,"abstract":"Background: Complex and diverse microbial communities play a pivotal role in human health and have become a new drug target. Exploring the connections between drugs and microbes not only provides profound insights into their mechanisms but also drives progress in drug discovery and repurposing. The use of wet lab experiments to identify associations is time-consuming and laborious. Hence, the advancement of precise and efficient computational methods can effectively improve the efficiency of association identification between microorganisms and drugs. Objective: In this experiment, we propose a new deep learning model, a new multiview comparative hypergraph attention network (MCHAN) method for human microbe–drug association prediction. Methods: First, we fuse multiple similarity matrices to obtain a fused microbial and drug similarity network. By combining graph convolutional networks with attention mechanisms, we extract key information from multiple perspectives. Then, we construct two network topologies based on the above fused data. One topology incorporates the concept of hypernodes to capture implicit relationships between microbes and drugs using virtual nodes to construct a hyperheterogeneous graph. Next, we propose a cross-contrastive learning task that facilitates the simultaneous guidance of graph embeddings from both perspectives, without the need for any labels. This approach allows us to bring nodes with similar features and network topologies closer while pushing away other nodes. Finally, we employ attention mechanisms to merge the outputs of the GCN and predict the associations between drugs and microbes. Results: To confirm the effectiveness of this method, we conduct experiments on three distinct datasets. The results demonstrate that the MCHAN model surpasses other methods in terms of performance. Furthermore, case studies provide additional evidence confirming the consistent predictive accuracy of the MCHAN model. Conclusion: MCHAN is expected to become a valuable tool for predicting potential associations between microbiota and drugs in the future.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140019928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network Subgraph-based Method: Alignment-free Technique for Molecular Network Analysis","authors":"Efendi Zaenudin, Ezra B. Wijaya, Venugopala Reddy Mekala, Ka-Lok Ng","doi":"10.2174/0115748936285057240126062220","DOIUrl":"https://doi.org/10.2174/0115748936285057240126062220","url":null,"abstract":"Objective: We propose a novel method to compare directed networks by decomposing the network into small modules, the so-called network subgraph approach, which is distinct from the network motif approach because it does not depend on null model assumptions. Method: We developed an alignment-free algorithm called the Subgraph Identification Algorithm (SIA), which could generate all subgraphs that have five connected nodes (5-node subgraph). There were 9,364 such modules. Then, we applied the SIA method to examine 17 cancer networks and measured the similarity between the two networks by gauging the similarity level using Jensen- Shannon entropy (HJS). Method: We developed an alignment-free algorithm called the Subgraph Identification Algorithm (SIA), which could generate all subgraphs that have five connected nodes (5-node subgraph). There were 9,364 such modules. Then, we applied the SIA method to examine 17 cancer networks and measured the similarity between the two networks by gauging the similarity level using Jensen- Shannon entropy (HJS). Results:: We identified and examined the biological meaning of 5-node regulatory modules and pairs of cancer networks with the smallest HJS values. The two pairs of networks that show similar patterns are (i) endometrial cancer and hepatocellular carcinoma and (ii) breast cancer and pathways in cancer. Some studies have provided experimental data supporting the 5-node regulatory modules. result: We identify and examine the biological meaning of 5-node regulatory modules and pairs of cancer networks which have the smallest HJS values. These two pairs of networks that show similar patterns are (i) endometrial cancer and hepatocellular carcinoma, and (ii) breast cancer and pathways in cancer. Some literature studies provide experimental data to support the 5-node regulatory modules. Conclusion: Our method is an alignment-free approach that measures the topological similarity of 5-node regulatory modules and aligns two directed networks based on their topology. These modules capture complex interactions among multiple genes that cannot be detected using existing methods that only consider single-gene relations. We analyzed the biological relevance of the regulatory modules and used the subgraph method to identify the modules that shared the same topology across 2 cancer networks out of 17 cancer networks. We validated our findings using evidence from the literature.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139953791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A-RFP: An Adaptive Residue Flexibility Prediction Method Improving Protein-ligand Docking Based on Homologous Proteins","authors":"Chuqi Lei, Senbiao Fang, Yaohang Li, Fei Guo, Min Li","doi":"10.2174/0115748936258790240101062642","DOIUrl":"https://doi.org/10.2174/0115748936258790240101062642","url":null,"abstract":"background: computational molecular docking plays an important role in determining the precise receptor-ligand conformation, which becomes a powerful tool for drug discovery. In the past 30 years, most computational docking methods treat the receptor structure as a rigid body, although flexible docking often yields higher accuracy. The main disadvantage of flexible docking is its significantly higher computational cost. Due to the fact that different protein pock-et residues exhibit different degrees of flexibility, semi-flexible docking methods, balancing rigid docking and flexible docking, have demonstrated success in predicting highly accurate conformations with a relatively low computational cost. method: In our study, the number of flexible pocket residues was assessed by quantitative analysis, and a novel adaptive residue flexibility prediction method, named A-RFP, was proposed to improve the docking performance. Based on the homologous information, a joint strategy is used to predict the pocket residue flexibility by combining RMSD, the distance between the residue sidechain and the ligand, and the sidechain orientation. For each receptor-ligand pair, A-RFP provides a docking conformation with the optimal affinity. result: By analyzing the docking affinities of 3507 target-ligand pairs in 5 different values ranging from 0 to 10, we found there is a general trend that the larger number of flexible residues inevitably improves the docking results by using Autodock Vina. However, a certain number of counterexamples still exist. To validate the effectiveness of A-RFP, the experimental assessment was tested in a small-scale virtual screening on 5 proteins, which confirmed that A-RFP could enhance the docking performance. And the flexible-receptor virtual screening on a low-similarity dataset with 85 receptors validates the accuracy of residue flexibility comprehensive evaluation. Moreover, we studied three receptors with FDA-approved drugs, which further proved A-RFP can play a suitable role in ligand discovery. conclusion: Our analysis confirms that the screening performance of the various number of flexible residues varies wildly across receptors. It suggests that a fine-grained docking method would offset the aforementioned deficiency. Thus, we presented A-RFP, an adaptive pocket residue flexibility prediction method based on homologous information. Without considering computational resources and time costs, A-RFP provides the optimal docking result.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139926820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CFCN: An HLA-peptide Prediction Model based on Taylor Extension\u0000Theory and Multi-view Learning","authors":"B. Rao, Bing Han, Leyi Wei, Zeyu Zhang, Xinbo Jiang, Balachandran Manavalan","doi":"10.2174/0115748936299044240202100019","DOIUrl":"https://doi.org/10.2174/0115748936299044240202100019","url":null,"abstract":"\u0000\u0000With the increasing development of biotechnology, many cancer solutions\u0000have been proposed nowadays. In recent years, Neo-peptides-based methods have made significant\u0000contributions, with an essential prerequisite of bindings between peptides and HLA molecules.\u0000However, the binding is hard to predict, and the accuracy is expected to improve further.\u0000\u0000\u0000\u0000Therefore, we propose the Crossed Feature Correction Network (CFCN) with deep\u0000learning method, which can automatically extract and adaptively learn the discriminative features\u0000in HLA-peptide binding, in order to make more accurate predictions on HLA-peptide binding\u0000tasks. With the fancy structure of encoding and feature extracting process for peptides, as well as\u0000the feature fusion process between fine-grained and coarse-grained level, it shows many advantages\u0000on given tasks.\u0000\u0000\u0000\u0000The experiment illustrates that CFCN achieves better performances overall, compared\u0000with other fancy models in many aspects.\u0000\u0000\u0000\u0000In addition, we also consider to use multi-view learning methods for the feature fusion\u0000process, in order to find out further relations among binding features. Eventually, we encapsulate\u0000our model as a useful tool for further research on binding tasks.\u0000","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140454148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sia-m7G: Predicting m7G Sites through the Siamese Neural Network with an Attention Mechanism","authors":"Jia Zheng, Yetong Zhou","doi":"10.2174/0115748936285540240116065719","DOIUrl":"https://doi.org/10.2174/0115748936285540240116065719","url":null,"abstract":"Background: The chemical modification of RNA plays a crucial role in many biological processes. N7-methylguanosine (m7G), being one of the most important epigenetic modifications, plays an important role in gene expression, processing metabolism, and protein synthesis. Detecting the exact location of m7G sites in the transcriptome is key to understanding their relevant mechanism in gene expression. On the basis of experimentally validated data, several machine learning or deep learning tools have been designed to identify internal m7G sites and have shown advantages over traditional experimental methods in terms of speed, cost-effectiveness and robustness. Aims: In this study, we aim to develop a computational model to help predict the exact location of m7G sites in humans. Objective: Simple and advanced encoding methods and deep learning networks are designed to achieve excellent m7G prediction efficiently. Methods: Three types of feature extractions and six classification algorithms were tested to identify m7G sites. Our final model, named Sia-m7G, adopts one-hot encoding and a delicate Siamese neural network with an attention mechanism. In addition, multiple 10-fold cross-validation tests were conducted to evaluate our predictor. Results: Sia-m7G achieved the highest sensitivity, specificity and accuracy on 10-fold crossvalidation tests compared with the other six m7G predictors. Nucleotide preference and model visualization analyses were conducted to strengthen the interpretability of Sia-m7G and provide a further understanding of m7G site fragments in genomic sequences. Conclusion: Sia-m7G has significant advantages over other classifiers and predictors, which proves the superiority of the Siamese neural network algorithm in identifying m7G sites.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139759975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated Machine Learning Algorithms for Stratification of Patients with Bladder Cancer","authors":"Yuanyuan He, Haodong Wei, Siqing Liao, Ruiming Ou, Yuqiang Xiong, Yongchun Zuo, Lei Yang","doi":"10.2174/0115748936288453240124082031","DOIUrl":"https://doi.org/10.2174/0115748936288453240124082031","url":null,"abstract":"Background: Bladder cancer is a prevalent malignancy globally, characterized by rising incidence and mortality rates. Stratifying bladder cancer patients into different subtypes is crucial for the effective treatment of this form of cancer. Therefore, there is a need to develop a stratification model specific to bladder cancer. Purpose: This study aims to establish a prognostic prediction model for bladder cancer, with the primary goal of accurately predicting prognosis and treatment outcomes. objective: This study aims to establish a prognostic prediction model for bladder cancer, with the primary goal of accurately predicting prognosis and treatment outcomes. Methods: We collected datasets from 10 bladder cancer samples sourced from the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA) databases, and IMvigor210 dataset. The machine learning based algorithms were used to generate 96 models for establishing the risk score for each patient. Based on the risk score, all the patients was classified into two different risk score groups. Results: The two groups of bladder cancer patients exhibited significant differences in prognosis, biological functions, and drug sensitivity. Nomogram model demonstrated that the risk score had a robust predictive effect with good clinical utility. Conclusion: The risk score constructed in this study can be utilized to predict the prognosis, response to drug treatment, and immunotherapy of bladder cancer patients, providing assistance for personalized clinical treatment of bladder cancer. other: None","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139760199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}