发布求助
文献互助 智能选刊 最新文献

Journal of Bioinformatics and Computational Biology最新文献

筛选
英文 中文
Gtie-Rt: A comprehensive graph learning model for predicting drugs targeting metabolic pathways in human. Gtie-Rt：用于预测以人类代谢途径为靶点的药物的综合图学习模型。
IF 0.9 4区 生物学
Journal of Bioinformatics and Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-07-20 DOI: 10.1142/S0219720024500100
Hayat Ali Shah, Juan Liu, Zhihui Yang
{"title":"Gtie-Rt: A comprehensive graph learning model for predicting drugs targeting metabolic pathways in human.","authors":"Hayat Ali Shah, Juan Liu, Zhihui Yang","doi":"10.1142/S0219720024500100","DOIUrl":"10.1142/S0219720024500100","url":null,"abstract":"<p><p>Drugs often target specific metabolic pathways to produce a therapeutic effect. However, these pathways are complex and interconnected, making it challenging to predict a drug's potential effects on an organism's overall metabolism. The mapping of drugs with targeting metabolic pathways in the organisms can provide a more complete understanding of the metabolic effects of a drug and help to identify potential drug-drug interactions. In this study, we proposed a machine learning hybrid model Graph Transformer Integrated Encoder (GTIE-RT) for mapping drugs to target metabolic pathways in human. The proposed model is a composite of a Graph Convolution Network (GCN) and transformer encoder for graph embedding and attention mechanism. The output of the transformer encoder is then fed into the Extremely Randomized Trees Classifier to predict target metabolic pathways. The evaluation of the GTIE-RT on drugs dataset demonstrates excellent performance metrics, including accuracy (>95%), recall (>92%), precision (>93%) and F1-score (>92%). Compared to other variants and machine learning methods, GTIE-RT consistently shows more reliable results.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":" ","pages":"2450010"},"PeriodicalIF":0.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141727966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Construction of transcript regulation mechanism prediction models based on binding motif environment of transcription factor AoXlnR in Aspergillus oryzae. 基于黑曲霉转录因子 AoXlnR 的结合主题环境构建转录本调控机制预测模型
IF 0.9 4区 生物学
Journal of Bioinformatics and Computational Biology Pub Date : 2024-06-01 DOI: 10.1142/S0219720024500173
Hiroya Oka, Takaaki Kojima, Ryuji Kato, Kunio Ihara, Hideo Nakano
{"title":"Construction of transcript regulation mechanism prediction models based on binding motif environment of transcription factor AoXlnR in <i>Aspergillus oryzae</i>.","authors":"Hiroya Oka, Takaaki Kojima, Ryuji Kato, Kunio Ihara, Hideo Nakano","doi":"10.1142/S0219720024500173","DOIUrl":"10.1142/S0219720024500173","url":null,"abstract":"<p><p>DNA-binding transcription factors (TFs) play a central role in transcriptional regulation mechanisms, mainly through their specific binding to target sites on the genome and regulation of the expression of downstream genes. Therefore, a comprehensive analysis of the function of these TFs will lead to the understanding of various biological mechanisms. However, the functions of TFs <i>in vivo</i> are diverse and complicated, and the identified binding sites on the genome are not necessarily involved in the regulation of downstream gene expression. In this study, we investigated whether DNA structural information around the binding site of TFs can be used to predict the involvement of the binding site in the regulation of the expression of genes located downstream of the binding site. Specifically, we calculated the structural parameters based on the DNA shape around the DNA binding motif located upstream of the gene whose expression is directly regulated by one TF AoXlnR from <i>Aspergillus oryzae</i>, and showed that the presence or absence of expression regulation can be predicted from the sequence information with high accuracy ([Formula: see text]-1.0) by machine learning incorporating these parameters.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"22 3","pages":"2450017"},"PeriodicalIF":0.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141761969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
NDMNN: A novel deep residual network based MNN method to remove batch effects from scRNA-seq data. NDMNN：基于深度残差网络的新型 MNN 方法，用于消除 scRNA-seq 数据中的批次效应。
IF 0.9 4区 生物学
Journal of Bioinformatics and Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-07-20 DOI: 10.1142/S021972002450015X
Yupeng Ma, Yongzhen Pei
{"title":"NDMNN: A novel deep residual network based MNN method to remove batch effects from scRNA-seq data.","authors":"Yupeng Ma, Yongzhen Pei","doi":"10.1142/S021972002450015X","DOIUrl":"10.1142/S021972002450015X","url":null,"abstract":"<p><p>The rapid development of single-cell RNA sequencing (scRNA-seq) technology has generated vast amounts of data. However, these data often exhibit batch effects due to various factors such as different time points, experimental personnel, and instruments used, which can obscure the biological differences in the data itself. Based on the characteristics of scRNA-seq data, we designed a dense deep residual network model, referred to as NDnetwork. Subsequently, we combined the NDnetwork model with the MNN method to correct batch effects in scRNA-seq data, and named it the NDMNN method. Comprehensive experimental results demonstrate that the NDMNN method outperforms existing commonly used methods for correcting batch effects in scRNA-seq data. As the scale of single-cell sequencing continues to expand, we believe that NDMNN will be a valuable tool for researchers in the biological community for correcting batch effects in their studies. The source code and experimental results of the NDMNN method can be found at https://github.com/mustang-hub/NDMNN.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":" ","pages":"2450015"},"PeriodicalIF":0.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141735374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
How much can ChatGPT really help computational biologists in programming? ChatGPT 对计算生物学家的编程到底有多大帮助？
IF 1 4区 生物学
Journal of Bioinformatics and Computational Biology Pub Date : 2024-04-01 Epub Date: 2024-05-22 DOI: 10.1142/S021972002471001X
Chowdhury Rafeed Rahman, Limsoon Wong
{"title":"How much can ChatGPT really help computational biologists in programming?","authors":"Chowdhury Rafeed Rahman, Limsoon Wong","doi":"10.1142/S021972002471001X","DOIUrl":"10.1142/S021972002471001X","url":null,"abstract":"<p><p>ChatGPT, a recently developed product by openAI, is successfully leaving its mark as a multi-purpose natural language based chatbot. In this paper, we are more interested in analyzing its potential in the field of computational biology. A major share of work done by computational biologists these days involve coding up bioinformatics algorithms, analyzing data, creating pipelining scripts and even machine learning modeling and feature extraction. This paper focuses on the potential influence (both positive and negative) of ChatGPT in the mentioned aspects with illustrative examples from different perspectives. Compared to other fields of computer science, computational biology has (1) less coding resources, (2) more sensitivity and bias issues (deals with medical data), and (3) more necessity of coding assistance (people from diverse background come to this field). Keeping such issues in mind, we cover use cases such as code writing, reviewing, debugging, converting, refactoring, and pipelining using ChatGPT from the perspective of computational biologists in this paper.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":" ","pages":"2471001"},"PeriodicalIF":1.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141082392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Learning Long- and Short-term Dependencies for Improving Drug-Target Binding Affinity Prediction using Transformer and Edge Contraction Pooling 利用变换器和边缘收缩池学习长期和短期依赖性以改进药物-靶点结合亲和力预测
IF 1 4区 生物学
Journal of Bioinformatics and Computational Biology Pub Date : 2023-12-15 DOI: 10.1142/s0219720023500300
Min Gao, Shaohua Jiang, Weibin Ding, Ting Xu, Zhijian Lyu
{"title":"Learning Long- and Short-term Dependencies for Improving Drug-Target Binding Affinity Prediction using Transformer and Edge Contraction Pooling","authors":"Min Gao, Shaohua Jiang, Weibin Ding, Ting Xu, Zhijian Lyu","doi":"10.1142/s0219720023500300","DOIUrl":"https://doi.org/10.1142/s0219720023500300","url":null,"abstract":"","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"23 5","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138999807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Predictive Recognition of DNA-binding proteins based on Pre-trained Language Model BERT 基于预训练语言模型的 DNA 结合蛋白预测识别 BERT
IF 1 4区 生物学
Journal of Bioinformatics and Computational Biology Pub Date : 2023-12-08 DOI: 10.1142/s0219720023500282
Yue Ma, Yongzhen Pei, Changguo Li
{"title":"Predictive Recognition of DNA-binding proteins based on Pre-trained Language Model BERT","authors":"Yue Ma, Yongzhen Pei, Changguo Li","doi":"10.1142/s0219720023500282","DOIUrl":"https://doi.org/10.1142/s0219720023500282","url":null,"abstract":"","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"185 3","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139011307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Imputation for single-cell RNA-seq data with non-negative matrix factorization and transfer learning 利用非负矩阵因式分解和迁移学习对单细胞 RNA-seq 数据进行估算
IF 1 4区 生物学
Journal of Bioinformatics and Computational Biology Pub Date : 2023-12-08 DOI: 10.1142/s0219720023500294
Jiadi Zhu, Youlong Yang
{"title":"Imputation for single-cell RNA-seq data with non-negative matrix factorization and transfer learning","authors":"Jiadi Zhu, Youlong Yang","doi":"10.1142/s0219720023500294","DOIUrl":"https://doi.org/10.1142/s0219720023500294","url":null,"abstract":"","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"65 2","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139011371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Algorithms for the Uniqueness of the Longest Common Subsequence. 最长共同后序唯一性算法。
IF 0.9 4区 生物学
Journal of Bioinformatics and Computational Biology Pub Date : 2023-12-01 Epub Date: 2024-01-10 DOI: 10.1142/S0219720023500270
Yue Wang
{"title":"Algorithms for the Uniqueness of the Longest Common Subsequence.","authors":"Yue Wang","doi":"10.1142/S0219720023500270","DOIUrl":"10.1142/S0219720023500270","url":null,"abstract":"<p><p>Given several number sequences, determining the longest common subsequence is a classical problem in computer science. This problem has applications in bioinformatics, especially determining transposable genes. Nevertheless, related works only consider how to find one longest common subsequence. In this paper, we consider how to determine the uniqueness of the longest common subsequence. If there are multiple longest common subsequences, we also determine which number appears in all/some/none of the longest common subsequences. We focus on four scenarios: (1) linear sequences without duplicated numbers; (2) circular sequences without duplicated numbers; (3) linear sequences with duplicated numbers; (4) circular sequences with duplicated numbers. We develop corresponding algorithms and apply them to gene sequencing data.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":" ","pages":"2350027"},"PeriodicalIF":0.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139425753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Small groups in multidimensional feature space: two examples of supervised two-group classification from biomedicine 多维特征空间中的小群体:生物医学中监督双群体分类的两个例子
4区 生物学
Journal of Bioinformatics and Computational Biology Pub Date : 2023-11-07 DOI: 10.1142/s0219720023500257
Dmitriy Karpenko, Aleksei Bigildeev
{"title":"Small groups in multidimensional feature space: two examples of supervised two-group classification from biomedicine","authors":"Dmitriy Karpenko, Aleksei Bigildeev","doi":"10.1142/s0219720023500257","DOIUrl":"https://doi.org/10.1142/s0219720023500257","url":null,"abstract":"","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"115 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135541469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
CNV-FB: A Feature bagging strategy-based approach to detect copy number variants from NGS data CNV-FB:基于特征装袋策略的NGS数据拷贝数变异检测方法
4区 生物学
Journal of Bioinformatics and Computational Biology Pub Date : 2023-11-07 DOI: 10.1142/s0219720023500269
Chengyou Li, Shiqiang Fan, Haiyong Zhao, Xiaotong Liu
{"title":"CNV-FB: A Feature bagging strategy-based approach to detect copy number variants from NGS data","authors":"Chengyou Li, Shiqiang Fan, Haiyong Zhao, Xiaotong Liu","doi":"10.1142/s0219720023500269","DOIUrl":"https://doi.org/10.1142/s0219720023500269","url":null,"abstract":"","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"115 22","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135541468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信