检索结果-内蒙古大学图书馆

ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Christiana C. Christodoulou George Minadakis Christiana Demetriou Eleni Zamba-Papanicolaou George Spyrou Neurology Clinic D & Bioinformatics Group Cyprus Institute of Neurology and Genetics & Cyprus School of Molecular Medicine Cyprus Bioinformatics Group Cyprus Institute of Neurology and Genetics & Cyprus School of Molecular Medicine Cyprus Cyprus School of Molecular Medicine University of Nicosia Medical School Cyprus Cyprus School of Molecular Medicine Cyprus Institute of Neurology and Genetics Cyprus

Huntington's disease (HD), a rare autosomal dominant disease, affecting the medium spiny neurons of the CNS. Although HD is caused by a trinucleotide repeat in the HTT gene, it is a complex disease. Systems bioinformatics which combines systems biology and bioinformatics, has the ability to reveal synergistic relationships between multiple entities. This approach is vital as it can shed light on the biological behavior and mechanisms of the cell rather than only trying to study and understand a part of the system. Metabolomics is the systematic study and measurement of metabolites within a biological sample. In this work, we employ two approaches to identify metabolites for HD-related pathways, which were previously identified from our previous work on multi-source data integration These include: i) creation of pathway-to-pathway networks based on the reference network of PathwayConnector where pathways are mapped based on connectivity on KEGG, and (ii) creation of pathway-to-pathway networks using a pairwise approach, where a connection between two pathways exists only if they share common metabolites.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach to Protein Folding Prediction based on Long Short-Term Memory Networks: A Preliminary Investigation and Analysis

A Novel Approach to Protein Folding Prediction based on Long...

引用

2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Hattori, Leandro Takeshi Benitez, Cesar Manuel Vargas Gutoski, Matheus Aquino, Nelson Marcelo Romero Lopes, Heitor Silverio Bioinformatics and Computational Intelligence Laboratory Federal University of Technology-Paraná Curitiba Brazil

ISBN: (纸本)9781509060146

The Protein Folding Problem (PFP) is considered one of the most important open challenges in biology and bioinformatics. Long Short-Term Memory (LSTM) methods have risen recently, achieving the state-of-art performance for several bioinformatics problems such as, protein secondary and tertiary protein structure prediction. This paper describes the application of a novel approach based on the LSTM networks to the PFP using a coarse-grained model of proteins. An specific encoding scheme for representing protein folding states is also presented. The proposed approach was evaluated by means of several experiments with a dataset of protein folding, which was obtained by Molecular Dynamics simulations. We also propose a novel method for evaluating the performance of the approach based on measures used in bioinformatics. Furthermore, a new analysis method for protein folding pathways is presented. Results suggest that the proposed approach is able to learn the protein fold transitions. Also, it is promising for the research areas related to bioinformatics and computational intelligence. © 2018 ieee.

关键词： bioinformatics

来源：评论

学校读者我要写书评

暂无评论

A deep neural network approach using distributed representations of RNA sequence and structure for identifying binding site of RNA-binding proteins

A deep neural network approach using distributed representat...

引用

ieee International Conference on bioinformatics and Biomedicine (BIBM)

作者： Lei Deng Youzhi Liu Yechuan Shi Hui Liu School of Computer Science and Engineering Central South University Changsha China Lab of Information Management Changzhou University Changzhou China

ISBN: (纸本)9781728118680

RNA-binding proteins (RBPs) play a crucial role in the post-transcriptional regulation of RNAs. Identification of RBP binding sites is a key step to understand the biological mechanism of post-transcriptional regulation. Although many computational methods have been developed for predicting RNA-protein binding sites, few study considers the k-mer embedding representation of RNA primary sequence and secondary structure specificities. In this paper, we develop a general deep learning framework, named deepRKE, to predict RNA-protein binding sites. deepRKE takes an unsupervised shallow two-layer neural network to automatically learn the distributed representation of k-mers by taking their neighbor context into account. Compared to conventional k-mers approach, distributed representations effectively detect the latent relationship and similarity between k-mers. The distributed representations of the sequences and secondary structures are fed into CNN convolutional neural network (CNN) and a bidirectional long short term memory network (BLSTM) to discriminate the RBP binding sites from unbound sites. We comprehensively evaluate deepRKE on two large-scale RBP binding sites datasets, and the experimental results show that deepRKE achieves better performance than five competitive methods.

关键词： biology computing convolutional neural nets learning (artificial intelligence) macromolecules molecular biophysics proteins recurrent neural nets RNA recurrent neural nets biology computing molecular biology Binding Sites RNA-Binding Proteins macromolecules Post-Transcriptional Regulation RNA Neural network Proteins Distribute RNA Sequence

来源：评论

学校读者我要写书评

暂无评论

Guest Editors' Introduction to the Special Section on bioinformatics Research and Applications

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2017年第3期14卷 576-577页

作者： Harrison, Robert W. Mandoiu, Ion I. Zelikovsky, Alexander Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Univ Connecticut Dept Comp Sci & Engn Storrs CT 06269 USA

The papers from this special section were presented at the 11th International symposium on bioinformatics Research and Application ISBRA, which was held at Old Dominion University in Norfolk, VA on May 7-10, 2015. The ISBRA symposium provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and computational biology and their applications.

关键词： Special issues and sections bioinformatics Meetings Informatics Research and development computational biology

来源：评论

学校读者我要写书评

暂无评论

Detecting Essential Proteins Based on Network Topology, Gene Expression Data, and Gene Ontology Information (vol 15, pg 109, 2018)

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第3期15卷 1035-1035页

作者： Zhang, Wei Xu, Jia Li, Yuanyuan Zou, Xiufen East China Jiaotong Univ Sch Sci Nanchang 330013 Jiangxi Peoples R China East China Jiaotong Univ Sch Mechatron Engn Nanchang 330013 Jiangxi Peoples R China Wuhan Univ Sch Math & Stat Wuhan 430072 Hubei Peoples R China Wuhan Inst Technol Sch Sci Wuhan 430073 Hubei Peoples R China

The identification of essential proteins in protein-protein interaction PPI networks is of great significance for understanding cellular processes. With the increasing availability of large-scale PPI data, numerous centrality measures based on network topology have been proposed to detect essential proteins from PPI networks. However, most of the current approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology annotation information. In this paper, we propose a novel centrality measure, called TEO, for identifying essential proteins by combining network topology, gene expression profiles, and GO information. To evaluate the performance of the TEO method, we compare it with five other methods degree, betweenness, NC, Pec, and CowEWC in detecting essential proteins from two different yeast PPI datasets. The simulation results show that adding GO information can effectively improve the predicted precision and that our method outperforms the others in predicting essential proteins.

关键词： bioinformatics biology Computing Cellular Biophysics Genetics Molecular Biophysics Network Topology Ontologies Artificial intelligence Proteins Network Topology Gene Expression Data Gene Ontology Information Protein Protein Interaction Networks Large Scale PPI Data PPI Networks Essential Proteins Detection Cellular Processes Centrality Measure Gene Expression Profiles Proteins Network Topology Gene Expression Ontologies Simulation Electronics Packaging Protein Protein Interaction Network Essential Proteins Gene Ontology Gene Expression Profile

来源：评论

学校读者我要写书评

暂无评论

Data driven point packing for fast clustering

Data driven point packing for fast clustering

引用

ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Matthew Stoodley Daniel Ashlock Steffen Graether The Bioinformatics Program at the University of Guelph The Department of Mathematics and Statistics The Bioinformatics Program at the University of Guelph The Department of Molecular and Cellular Biology The Bioinformatics Program at the University of Guelph

Modern data acquisition has forced the field of large data on the scientific community. This papers gives a rapid technique for clustering data. The technique is based on an off-line process for packing points chosen from a data space. Once the off-line process has been run, the clustering may be re-run on different data sets of the same type in linear time. The clustering takes the form of a Voronoi tiling of the data space with the tile centres being the elements of the point packing. The data items within each tile form the clusters. The evolutionary algorithm is an adaptation of one, based on the Conway crossover operator, that has been used to create error correcting codes over the Levenstein metric; the tile centres are a form of code, but over the Euclidean metric. The technique generalizes smoothly to other metric spaces and may be used on any type of data for which a distance metric can be devised. The data set used in this study captures information about codon usage bias in human genes. The clustering is validated by looking for GO term over- representation in the clusters, with significant results.

关键词： Amino acids Clustering algorithms RNA DNA Measurement bioinformatics Hypercubes

来源：评论

学校读者我要写书评

暂无评论

Predicting virus-host association by Kernelized logistic matrix factorization and similarity network fusion

引用

BMC bioinformatics 2019年第Sup16期20卷 594-594页

作者： Liu, Dan Ma, Yingjun Jiang, Xingpeng He, Tingting Cent China Normal Univ Sch Comp Wuhan Hubei Peoples R China Cent China Normal Univ Hubei Prov Key Lab Artificial Intelligence & Smar Wuhan Hubei Peoples R China

Background Viruses are closely related to bacteria and human diseases. It is of great significance to predict associations between viruses and hosts for understanding the dynamics and complex functional networks in microbial community. With the rapid development of the metagenomics sequencing, some methods based on sequence similarity and genomic homology have been used to predict associations between viruses and hosts. However, the known virus-host association network was ignored in these methods. Results We proposed a kernelized logistic matrix factorization with integrating different information to predict potential virus-host associations on the heterogeneous network (ILMF-VH) which is constructed by connecting a virus network with a host network based on known virus-host associations. The virus network is constructed based on oligonucleotide frequency measurement, and the host network is constructed by integrating oligonucleotide frequency similarity and Gaussian interaction profile kernel similarity through similarity network fusion. The host prediction accuracy of our method is better than other methods. In addition, case studies show that the host of crAssphage predicted by ILMF-VH is consistent with presumed host in previous studies, and another potential host Escherichia coli is also predicted. Conclusions The proposed model is an effective computational tool for predicting interactions between viruses and hosts effectively, and it has great potential for discovering novel hosts of viruses.

关键词： Virus-host association Logistic matrix factorization Similarity network fusion Oligonucleotide frequency Gaussian interaction profile

来源：评论

学校读者我要写书评

暂无评论

Zazz: Variant Annotation and Exploration of Next Generation Sequencing Variants

Zazz: Variant Annotation and Exploration of Next Generation ...

引用

ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Maria Astrinaki Alexandros Kanterakis Helen Latsoudis George Potamias Dimitris Kafetzopoulos Medical school University of Crete Heraklion Greece Foundation for Research and Technology Institute of Computer Science Heraklion Greece Foundation for Research and Technology Institute of Molecular Biology and Biotechnology Heraklion Greece

Over the last 10 years, Next-Generation Sequencing (NGS) has become a powerful tool in clinical genetics and precision medicine. Techniques like Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES) and Target Sequencing are commonly used for the elucidation of common and rare variants in mendelian and complex diseases. One of the most vital parts of NGS pipelines is the prioritization of annotated variants according to their clinical significance. During this process, a clinical geneticist is presented with annotation information from external databases for each of the thousands of potential variants. The vast amounts of data and the vague nature of existing guidelines for variant reporting, like ACMG (American College of Medical Genetics) can make this procedure very cumbersome and time consuming. Here we present the main computational challenges and existing solutions for this task. We also present Zazz, an online environment for variant annotation, query and exploration. Zazz can efficiently support the submission of complex and dynamically generated queries to hundreds of millions of variants each having hundreds of annotation fields. Zazz also leverages the capabilities of modern browsers to dynamically filter, explore and visualize multidimensional data.

关键词： Annotations bioinformatics Databases Genomics Tools Sequential analysis Diseases

来源：评论

学校读者我要写书评

暂无评论

ieee Conference on computational intelligence in bioinformatics and computational biology

引用

ieee computational intelligence Magazine 2018年第1期13卷 C3-C3页

Presents information on the ieee Conference on computational intelligence in bioinformatics and computational biology.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multi-Classification of Cancer Samples Based on Co-Expression Analyses

Multi-Classification of Cancer Samples Based on Co-Expressio...

引用

ieee International Conference on bioinformatics and Biomedicine (BIBM)

作者： Hongyang Jiang Qiang Huang Liang Chen Zhi Li Ying Xu Huiyan Sun Yi Chang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China College of Engineering Key Laboratory of Intelligent ManufacturingTechnology of Ministry of Education Shantou University Shantou China Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province The First Hospital of China Medical University Shenyang China The China-Japan Union Hospital Cancer Systems Biology Center Jilin University Changchun China The China-Japan Union Hospital School of Artificial Intelligence Cancer Systems Biology Center Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China

Cancer staging, grading and subtyping all represent important problems for precision diagnosis, treatment and mechanistic studies of cancer. The majority of the existing computational methods solve this problem via multi-classification of differential gene-expressions of cancer samples of specific classes (Stages, Grades and subtypes) vs. controls. However, the performance of such classification techniques is generally not satisfactory since the discerning power of differential expression patterns in such classifications is limited. We present here a multi-classification technique, based on co-expression patterns specific to individual subclasses in provided training data as co-expression patterns tend to be more conserved than differential expressions within each subclass. A challenge in implementing this strategy lies in how to effectively derive co-expression patterns in individual samples, which is solved through comparing co-expression patterns within a subclass and those in the subclass plus a new sample. Compared with the state-of-the-art gene expression-based classification methods, our method outperforms them in cancer staging, grading and subtyping of cancer samples from TCGA in almost all the measures used. In addition, the co-expressed genes computationally selected for classifications are biologically meaningful, which will prove important for diagnostic biomarker design, treatment plan selection and possibly mechanistic studies of cancer.

关键词：

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：