检索结果-内蒙古大学图书馆

ieee symposium on computational intelligence in bioinformatics and computational biology

ieee Transactions on Fuzzy Systems 2011年第4期19卷 795-795页

"Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers."

关键词：

来源：评论

学校读者我要写书评

暂无评论

EURYALE: A versatile Nextflow pipeline for taxonomic classification and functional annotation of metagenomics data 21

EURYALE: A versatile Nextflow pipeline for taxonomic classif...

引用

21st ieee Conference on computational intelligence in bioinformatics and computational biology, CIBCB 2024

作者： Cavalcante, João Vitor F. De Souza, Iara Dantas Morais, Diego A. A. Dalmolin, Rodrigo J. S. Federal University of Rio Grande do Norte Bioinformatics Multidisciplinary Environment Natal Brazil Federal University of Rio Grande do Norte Department of Biochemistry Natal Brazil

ISBN: (纸本)9798350356632

EURYALE is a Nextflow pipeline designed for the sensitive taxonomic classification and flexible functional annotation of metagenomic shotgun sequences. It provides a comprehensive solution for preprocessing, assembly, alignment, taxonomic classification, and functional annotation of metagenomic data. EURYALE builds upon the Snakemake-based pipeline MEDUSA. EURYALE inherits the tools present in MEDUSA, selected based on rigorous benchmarks for performance, accuracy, and sensitivity. The new pipeline has been developed with the nf-core pipeline template, which focuses on modularity, allowing a high degree of parameterization. EURYALE provides easier resource management, enforcing strict memory and CPU requirements based on its Nextflow configuration. It has also become more versatile, as it can be executed using Docker and Singularity, which further extends its usability across various platforms. It can also natively take advantage of computational infrastructures such as SLURM and Amazon Web Services. EURYALE inherits the sensitivity in taxonomic classification and flexibility in functional annotation of its predecessor, combined with improved versatility. © 2024 ieee.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

The Neural Metric Factorization for computational Drug Repositioning

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第1期20卷 731-741页

作者： Yang, Xinxing Yang, Genke Chu, Jian Shanghai Jiao Tong Univ Ningbo Artificial Intelligence Inst Dept Automat Shanghai 200240 Peoples R China

computational drug repositioning aims to discover new therapeutic diseases for marketed drugs and has the advantages of low cost, short development cycle, and high controllability compared to traditional drug development. The matrix factorization model has become the cornerstone technique for computational drug repositioning due to its ease of implementation and excellent scalability. However, the matrix factorization model uses the inner product operation to represent the association between drugs and diseases, which is lacking in expressive ability. Moreover, the degree of similarity of drugs or diseases could not be implied on their respective latent factor vectors, which is not satisfy the common sense of drug discovery. Therefore, a neural metric factorization model for computational drug repositioning (NMFDR) is proposed in this work. We novelly consider the latent factor vector of drugs and diseases as a point in the high-dimensional coordinate system and propose a generalized euclidean distance to represent the association between drugs and diseases to compensate for the shortcomings of the inner product operation. Furthermore, by embedding multiple drug (disease) metrics information into the encoding space of the latent factor vector, the information about the similarity between drugs (diseases) can be reflected in the distance between latent factor vectors. Finally, we conduct wide analysis experiments on three real datasets to demonstrate the effectiveness of the above improvement points and the superiority of the NMFDR model.

关键词： computational drug repositioning matrix factorization euclidean distance metrics information

来源：评论

学校读者我要写书评

暂无评论

The computational Drug Repositioning Without Negative Sampling

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第2期20卷 1506-1517页

作者： Yang, Xinxing Yang, Genke Chu, Jian Shanghai Jiao Tong Univ Ningbo Artificial Intelligence Inst Dept Automation Shanghai 200240 Peoples R China

computational drug repositioning technology is an effective tool to accelerate drug development. Although this technique has been widely used and successful in recent decades, many existing models still suffer from multiple drawbacks such as the massive number of unvalidated drug-disease associations and the inner product. The limitations of these works are mainly due to the following two reasons: firstly, previous works used negative sampling techniques to treat unvalidated drug-disease associations as negative samples, which is invalid in real-world settings;secondly, the inner product cannot fully take into account the feature information contained in the latent factor of drug and disease. In this paper, we propose a novel PUON framework for addressing the above deficiencies, which models the risk estimator of computational drug repositioning only using validated (Positive) and unvalidated (Unlabelled) drug-disease associations without employing negative sampling techniques. The PUON also proposed an Outer Neighborhood-based classifier for modeling the cross-feature information of the latent facotor. For a comprehensive comparison, we considered 6 popular baselines. Extensive experiments in four real-world datasets showed that PUON model achieved the best performance based on 6 evaluation metrics.

关键词： Drugs Diseases computational modeling Predictive models Collaboration Training Shape Drug discovery computational drug repositioning outer product positive-unlabeled learning

来源：评论

学校读者我要写书评

暂无评论

MVDINET: A Novel Multi-Level Enzyme Function Predictor With Multi-View Deep Interactive Learning

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第1期21卷 84-94页

作者： Tang, Wenliang Deng, Zhaohong Zhou, Hanwen Zhang, Wei Hu, Fuping Choi, Kup-Sze Wang, Shitong Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Hong Kong Polytech Univ Ctr Smart Hlth Hong Kong Peoples R China

As a class of extremely significant of biocatalysts, enzymes play an important role in the process of biological reproduction and metabolism. Therefore, the prediction of enzyme function is of great significance in biomedicine fields. Recently, computational methods for predicting enzyme function have been proposed, and they effectively reduce the cost of enzyme function prediction. However, there are still deficiencies for effectively mining the discriminant information for enzyme function recognition in existing methods. In this study, we present MVDINET, a novel method for multi-level enzyme function prediction. First, the initial multi-view feature data is extracted by the enzyme sequence. Then, the above initial views are fed into various deep specific network modules to learn the depth-specificity information. Further, a deep view interaction network is designed to extract the interaction information. Finally, the specificity information and interaction information are fed into a multi-view adaptively weighted classification. We compressively evaluate MVDINET on benchmark datasets and demonstrate that MVDINET is superior to existing methods.

关键词： Multi-level enzyme function prediction multi-view deep interactive learning multi-view adaptively weighted classification

来源：评论

学校读者我要写书评

暂无评论

TeaTFactor: A Prediction Tool for Tea Plant Transcription Factors Based on BERT

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第6期21卷 2123-2132页

作者： Tang, Qinan Xiang, Ying Gao, Wanling Zhu, Liqiang Xu, Zishu Li, Yeyun Yue, Zhenyu Anhui Agr Univ Sch Informat & Artificial Intelligence Hefei 230036 Peoples R China Anhui Agr Univ State Key Lab Tea Plant Biol & Utilizat Hefei 230036 Peoples R China Anhui Agr Univ Sch Informat & Artificial Intelligence State Key Lab Tea Plant Biol & Utilizat Hefei 230036 Peoples R China

A transcription factor (TF) is a sequence-specific DNA-binding protein, which plays key roles in cell-fate decision by regulating gene expression. Predicting TFs is key for tea plant research community, as they regulate gene expression, influencing plant growth, development, and stress responses. It is a challenging task through wet lab experimental validation, due to their rarity, as well as the high cost and time requirements. As a result, computational methods are increasingly popular to be chosen. The pre-training strategy has been applied to many tasks in natural language processing (NLP) and has achieved impressive performance. In this paper, we present a novel recognition algorithm named TeaTFactor that utilizes pre-training for the model training of TFs prediction. The model is built upon the BERT architecture, initially pre-trained using protein data from UniProt. Subsequently, the model was fine-tuned using the collected TFs data of tea plants. We evaluated four different word segmentation methods and the existing state-of-the-art prediction tools. According to the comprehensive experimental results and a case study, our model is superior to existing models and achieves the goal of accurate identification. In addition, we have developed a web server at http://***, which we believe will facilitate future studies on tea transcription factors and advance the field of crop synthetic biology.

关键词： Proteins Predictive models Data models Encoding Bidirectional control Task analysis Biological system modeling BERT pre-training transcription factor k-mer tea

来源：评论

学校读者我要写书评

暂无评论

Consensus Clustering Strategy for Cell Type Assignments of scRNA-seq Data 20

Consensus Clustering Strategy for Cell Type Assignments of s...

引用

20th ieee Conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Riva, Simone G. Myers, Brynelle Cazzaniga, Paolo Buffa, Francesca M. Tangherloni, Andrea Univ Oxford Radcliffe Dept Med Weatherall Inst Mol Med Oxford England Univ Oxford Weatherall Inst Mol Med Oxford England Univ Bergamo Dept Human & Social Sci Bergamo Italy Bicocca Bioinformat Biostat & Bioimaging Ctr B4 Milan Italy Univ Oxford Dept Oncol Oxford England Bocconi Univ Bocconi Inst Data Sci & Analyt Dept Comp Sci Milan Italy

ISBN: (纸本)9798350310177

Cell type annotation is a crucial step for analyzing single-cell RNA sequencing data. Among others, singlecell Automatic Labeling of cell POpulations (scALPO) is a computational pipeline developed to automatically assign the cell types to the identified clusters in scRNA-seq data. Different from most of the approaches, scALPO relies only on the information on marker genes from published literature. Specifically, after the definition of the dataset obtained from gene information retrieved from online databases, the Leiden clustering algorithm is executed to partition cells that are finally annotated. Since the Leiden algorithm might struggle to obtain a reliable outcome under certain circumstances, in this work, we include several clustering algorithms in scALPO, and we propose a pseudo-voting consensus approach that combines the outcome of a set of clustering algorithms. The results obtained on three different datasets show that the consensus approach can improve the cell type annotation without selecting a specific clustering algorithm that best suits the data under investigation.

关键词： Artificial intelligence Deep Learning Consensus clustering bioinformatics scRNA-seq data

来源：评论

学校读者我要写书评

暂无评论

SAGCN: Using Graph Convolutional Network With Subgraph-Aware for circRNA-Drug Sensitivity Identification

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第6期21卷 1765-1774页

作者： Sun, Weicheng Ren, Chengjuan Xu, Jinsheng Zhang, Ping Huazhong Agr Univ Coll informat Wuhan 430070 Peoples R China Sichuan Int Studies Univ Coll Language Intelligence Chongqing 400031 Peoples R China

Circular RNAs (circRNAs) play a significant role in cancer development and therapy resistance. There is substantial evidence indicating that the expression of circRNAs affects the sensitivity of cells to drugs. Identifying circRNAs-drug sensitivity association (CDA) is helpful for disease treatment and drug discovery. However, the identification of CDA through conventional biological experiments is both time-consuming and costly. Therefore, it is urgent to develop computational methods to predict CDA. In this study, we propose a new computational method, the subgraph-aware graph convolutional network (SAGCN), for predicting CDA. SAGCN first constructs a heterogeneous network composed of circRNA similarity network, drug similarity network, and circRNA-drug bipartite network. Then, a subgraph extractor is proposed to learn the latent subgraph structure of the heterogeneous network using a graph convolutional network. The extractor can capture 1-hop and 2-hop information and then a fusing attention mechanism is designed to integrate them adaptively. Simultaneously, a novel subgraph-aware attention mechanism is proposed to detect intrinsic subgraph structure. The final node feature representation is obtained to make the CDA prediction. Experimental results demonstrate that SAGCN obtained an average AUC of 0.9120 and AUPR of 0.8693, exceeding the performance of the most advanced models under 10-fold cross-validation. Case studies have demonstrated the potential of SAGCN in identifying associations between circRNA and drug sensitivity.

关键词： subgraph extractor subgraph perceptron CircRNAs-drug sensitivity association cancer treatment cancer treatment subgraph perceptron cancer treatment graph convolu- tional network

来源：评论

学校读者我要写书评

暂无评论

exp2GO: Improving Prediction of Functions in the Gene Ontology With Expression Data

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第2期20卷 999-1008页

作者： Di Persia, Leandro Lopez, Tiago Arce, Agustin Milone, Diego H. Stegmayer, Georgina Consejo Nacl Invest Cient & Tecn FICH UNL Sinc I Res Inst Signals Syst & Computat Intelligence Ciudad Univ UNLS3000ADQ Santa Fe Argentina Consejo Nacl Invest Cient & Tecn UNL IAL Inst Agrobiotecnol Litoral Ciudad Univ UNLS3000ADQ Santa Fe Argentina

The computational methods for the prediction of gene function annotations aim to automatically find associations between a gene and a set of Gene Ontology (GO) terms describing its functions. Since the hand-made curation process of novel annotations and the corresponding wet experiments validations are very time-consuming and costly procedures, there is a need for computational tools that can reliably predict likely annotations and boost the discovery of new gene functions. This work proposes a novel method for predicting annotations based on the inference of GO similarities from expression similarities. The novel method was benchmarked against other methods on several public biological datasets, obtaining the best comparative results. exp2GO effectively improved the prediction of GO annotations in comparison to state-of-the-art methods. Furthermore, the proposal was validated with a full genome case where it was capable of predicting relevant and accurate biological functions. The repository of this project withh full data and code is available at https://***/sinc-lab/exp2GO.

关键词： Annotations Semantics Ontologies Genomics bioinformatics Proteins Bayes methods Gene ontology semantic distance gene function prediction non negative matrix factorization bayesian inference

来源：评论

学校读者我要写书评

暂无评论

Guest Editorial for Special Section on the 16th International Conference on Intelligent Computing (ICIC)

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第6期19卷 3124-3125页

作者： Huang, De-Shuang Han, Kyungsook Akutsu, Tatsuya Guangxi Acad Sci Big Data & Intelligent Comp Res Ctr Nanning Peoples R China Inha Univ Dept Comp Engn Incheon 22212 South Korea Kyoto Univ Inst Chem Res Bioinformat Ctr Kyoto 6068501 Japan

The eight papers in this special section were presented at the Sixteenth International Conference on Intelligent Computing (ICIC) that was held in Bari, Italy, on October 2-5, 2020. ICIC was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computational biology, etc. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems and solutions related to the multifaceted aspects of intelligent computing.

关键词： Special issues and sections Meetings Machine learning bioinformatics computational biology

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：