检索结果-内蒙古大学图书馆

第五届全国生物信息学与系统生物学学术大会

作者： Fengji Liang Xionghui Zhou Wenyan Qiao Xingyi Hang Ling Yin Juan Liu Yinghui Li Shanguang Chen Jianghui Xiong Telemedicine Center Institute of Medical Informatics General Hospital of Chinese PLA Beijing China School of Computer Science Wuhan University 430072 China Bioinformatics Group and Data Coordination Center State Key Lab of Space Medicine Fundamentals and Application (SMFA) China Astronaut Research and Training Center (ACC) Beijing China School of Computer Science Wuhan University 430072 China Bioinformatics Group and Data Coordination Center State Key Lab of Space Medicine Fundamentals and Application (SMFA) China Astronaut Research and Training Center (ACC) Beijing China National Scientific Data Sharing Platform for Population and Health Beijing China

A core task of drug discovery study is to identify the dependency between the genetic/ molecular makeups of the human body and disease *** we proposed a novel method for systematically screening the microRNAs regulating cancer prognosis via driving a specific ppi (protein-protein interaction) *** this method, we integrated information from microRNAs expression, mRNAs expression and patient outcome, and tried to mine the in vivo dependency between microRNAs and ppi *** conditional dependency of the mRNAs expression (G), microRNAs expression (M) and phenotype (P) was calculated, and the permutation was conduct to screen the significant Conditional Mutual Information Ⅰ (G, P|M).In this study we test the proposed method on autophagy protein networks in ovarian cancer as *** is a cellular process involved the degradation of cell's own components, in which cytoplasmic contents are sequestered into a double-membrane called autophagosome and then subsequently delivered to lysosome for *** identified a group of microRNAs, which could regulate the hub nodes of autophagy ppi *** experimentally demonstrated that the transfection of the mimics of these microRNAs could inhibit autophagy via inhibiting the target gene: ***, the miRNA-target gene pair is significantly associated with prognosis in a cohort of ovarian cancer patients .

关键词：

来源：评论

学校读者我要写书评

暂无评论

An integrated approach to identify the microRNAs regulation network modulating autophagy and cancer prognosis

An integrated approach to identify the microRNAs regulation ...

引用

第五届全国生物信息学与系统生物学学术大会暨国际生物信息学前沿研讨会高通量时代的生物信息学与系统生物学

作者： Fengji Liang Xionghui Zhou Wcnyan Qiao Xingyi Hang Ling Yin Juan Liu Yinghui Li Shanguang Chen Jianghui Xiong Bioinformatics Group and Data Coordination Center State Key Lab of Space Medicine Fundamentals and Application(SMFA)China Astronaut Research and Training Center(ACC) School of Computer Science Wuhan University430072China National Scientific Data Sharing Platform for Population and Health Telemedicine Center Institute of Medical InformaticsGeneral Hospital of Chinese PLA

A core task of drug discovery study is to identify the dependency between the genetic/ molecular makeups of the human body and disease *** we proposed a novel method for systematically screening the microRNAs regulating cancer prognosis via driving a specific ppi(protein-protein interaction) *** this method,we integrated information from microRNAs expression,mRNAs expression and patient outcome,and tried to mine the in vivo dependency between microRNAs and ppi *** conditional dependency of the mRNAs expression(G),microRNAs expression(M) and phenotype(P) was calculated, and the permutation was conduct to screen the significant Conditional Mutual Information I (G,P|M).In this study we test the proposed method on autophagy protein networks in ovarian cancer as *** is a cellular process involved the degradation of cell's own components,in which cytoplasmic contents are sequestered into a double-membrane called autophagosome and then subsequently delivered to lysosome for *** identified a group of microRNAs,which could regulate the hub nodes of autophagy ppi *** experimentally demonstrated that the transfection of the mimics of these microRNAs could inhibit autophagy via inhibiting the target gene:***,the miRNA-target gene pair is significantly associated with prognosis in a cohort of ovarian cancer patients.

关键词： microRNAs An integrated approach to identify the microRNAs regulation network modulating autophagy and cancer prognosis

来源：评论

学校读者我要写书评

暂无评论

Whole Genome Gene Discovery in the Pathogenic Oomycete Pythium insidiosum by 454 Sequencing Technology

Whole Genome Gene Discovery in the Pathogenic Oomycete Pythi...

引用

The Annual Oomycete Molecular Genetics Network Meeting(国际卵菌分子遗传学年会)

作者： Theerapong Krajaejun Shoba Ranganathan Rommanee Khositnithikul Tassanee Lerksuthirat Tassanee Lowhnoo Thidarat Rujirawat Wanta Yingyong Prapat Suriyaphol Sittichoke Tangphatsornruang Gagan Garg Department of Pathology Mahidol University Bangkok Department of Chemistry and Biomolecular Sciences Macquarie University Australia Molecular Medicine Program Multidisciplinary Unit Faculty of Science Mahidol University Bangkok Research Center Faculty of Medicine Ramathibodi HospitalMahidol University Bangkok Bioinformatics and Data Management for Research Office for Research and Development Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Genome Sequencing Laboratory National Center for Genetic Engineering and Biotechnology National Science and Technology Development Agency PathumthaniThailand

来源：评论

学校读者我要写书评

暂无评论

Dynamic remodeling of context-specific miRNAs regulation networks facilitate in silico cancer drug screening

Dynamic remodeling of context-specific miRNAs regulation net...

引用

IEEE International Conference on Systems Biology

作者： Lida Zhu Fengji Liang Juan Liu Simon Rayner Yinghui Li Shanguang Chen Jianghui Xiong School of Computer Science Wuhan University Wuhan China Bioinformatics Group and Data Coordination Center State Key Laboratory of Space Medicine Fundamentals and Application (SMFA) China Astronaut Research and Training Center Beijing China Bioinformatics Group State Key Laboratory of Virology Wuhan Institute of Virology Chinese Academy of Sciences (CAS) Wuhan China

Background: Much effort has been expended in exploring the connections between transcriptome, disease and drug, based on the premise that drug induced perturbations in the transcriptome will affect the phenotype and finally help to cure a disease. MicroRNAs (miRNAs) play a key role in the regulation of the transcriptome and have been identified as a key mediator in human disease and drug response. However, even if miRNA expression can be precisely detected, the information regarding miRNAs action on a particular part of the transcriptome is still lacking. Here, we introduced a novel concept, the Context-specific MiRNA activity (CoMi activity), to reflect a miRNA's regulation effect on a context specific gene set, by calculating the statistical difference between the distributions of its target gene expression and non-target gene expression. In this study we investigate whether CoMi activity could provide a novel perspective on miRNA mechanisms of action in disease and drug response, and facilitate in silico drug screening. Results: Using breast cancer as an example, we examined the CoMi activity based on a Gene Ontology (GO) term as context. Then we constructed a differential CoMi activity network (cancer vs. normal), based on the comi activity represents as a link between miRNAs and GO terms, (e.g. hsa-miR-27a's regulation on GO term "apoptosis"). The topological analysis of the generated network demonstrated that the cancer specific CoMi network is a scale free network. The highly connected nodes highlighted a group of known onco-miRNAs (e.g. hsa miR-183*) and tumor suppressor miRNAs (e.g. hsa-miR-34a), as well as some well-known cancer related GO biological processes (e.g. apoptosis). Interestingly, we found that chemotherapeutic drug treatment can counteract the dis regulated CoMi activity in the cancer-specific network. For instance, 100% of down-regulated CoMi activities in a "core" breast cancer network contains apoptosis-related GO terms that could be counte

关键词： Drugs Gene expression Breast cancer Libraries Silicon Indexes

来源：评论

学校读者我要写书评

暂无评论

Context-specific miRNA regulation network predicts cancer prognosis

Context-specific miRNA regulation network predicts cancer pr...

引用

IEEE International Conference on Systems Biology

作者： Xionghui Zhou Juan Liu Changning Liu Simon Rayner Fengji Liang Jingfang Ju Yinghui Li Shanguang Chen Jianghui Xiong School of Computer Science Wuhan University Wuhan China Bioinformatics Research Group Key Laboratory of Intelligent Information Processing Advanced Computing Research Laboratory Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China Bioinformatics Group State Key Laboratory of Virology Wuhan Institute of Virology Chinese Academy of Sciences (CAS) Wuhan China Bioinformatics Group and Data Coordination Center State Key Laboratory of Space Medicine Fundamentals and Application China Astronaut Research and Training Center Beijing China Department of Pathology Stony Brook University Medical Center New York USA

MicroRNAs can regulate hundreds of target genes and play a pivotal role in a broad range of biological process. However, relatively little is known about how these highly connected miRNAs-target networks are remodelled in the context of various diseases. Here we examine the dynamic alteration of context-specific miRNA regulation to determine whether modified microRNAs regulation on specific biological processes is a useful information source for predicting cancer prognosis. A new concept, Context-specific miRNA activity (CoMi activity) is introduced to describe the statistical difference between the expression level of a miRNA's target genes and non-targets genes within a given gene set (context).

关键词： Breast cancer Context Correlation Biological processes Metastasis Tumors

来源：评论

学校读者我要写书评

暂无评论

Proximity user identification using correlogram

引用

作者： Shahidi, Shervin Mazrooei, Parisa Esfahani, Navid Nasr Saraee, Mohammad Intelligent Databases Data Mining and Bioinformatics Research Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156-83111 Iran Isfahan Mathematics House Isfahan 81645-356 Iran School of Computing Science and Engineering University of Salford Greater Manchester United Kingdom

ISBN: (纸本)3642163262

This paper represents a technique, applying user action patterns in order to distinguish between users and identify them. In this method, users' actions sequences are mapped to numerical sequences and each user's profile is generated using autocorrelation values. Next, cross-correlation is used to compare user profiles with a test data. To evaluate our proposed method, a dataset known as Greenberg's dataset is used. The presented approach is succeeded to detect the correct user with as high as 82.3% accuracy over a set of 52 users. In comparison to the existing methods based on Hidden Markov Model or Neural Networks, our method needs less computation time and space. In addition, it has the ability of getting updated iteratively which is a main factor to facilitate transferability. © 2010 IFIP.

关键词： Pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Organizing our knowledge of biodiversity

引用

Bulletin of the Association for Information science and Technology 2011年第4期37卷

作者： Hilmar Lapp Robert A. Morris Terry Catapano Donald Hobern Norman Morrison Assistant director of informatics National Evolutionary Synthesis Center (NESCent) Professor emeritus of computer science University of Massachusetts/Boston and biodiversity informatics staff Harvard University Herbaria librarian Columbia University Libraries and vice president Plazi Director Atlas of Living Australia and former TDWG chair Ontologies and data standards manager Natural Environment Research Council Environmental Bioinformatics Centre (NEBC) & The University of Manchester United Kingdom

来源：评论

学校读者我要写书评

暂无评论

CLASS: A general approach to classifying categorical sequences

CLASS: A general approach to classifying categorical sequenc...

引用

作者： Kelil, Abdellali Nordell-Markovits, Alexei Ousman Yassine Zaralahy, Parakh Wang, Shengrui ProspectUS Data Mining and Bioinformatics Laboratory Department of Computer Science Université de Sherbrooke Sherbrooke QC J1K 2R1 Canada

The rapid burgeoning of available data in the form of categorical sequences, such as biological sequences, natural language texts, network and retail transactions, makes the classification of categorical sequences increasingly important. The main challenge is to identify significant features hidden behind the chronological and structural dependencies characterizing their intrinsic properties. Almost all existing algorithms designed to perform this task are based on the matching of patterns in chronological order, but categorical sequences often have similar features in non-chronological order. In addition, these algorithms have serious difficulties in outperforming domain-specific algorithms. In this paper we propose CLASS, a general approach for the classification of categorical sequences. By using an effective matching scheme called SPM for Signifi cant Patterns Matching, CLASS is able to capture the intrinsic properties of categorical sequences. Furthermore, the use of Latent Semantic Analysis allows capturing semantic relations using global information extracted from large number of sequences, rather than comparing merely pairs of sequences. Moreover, CLASS employs a classifier called SNN for Significant Nearest Neighbours, inspired from the K Nearest Neighbours approach with a dynamic estimation of K, which allows the reduction of both false positives and false negatives in the classification. The extensive tests performed on a range of datasets from different fields show that CLASS is oftentimes competitive with domain-specific approaches. © 2009 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

CLASS: a general approach to classifying categorical sequences

引用

Canadian Journal of Electrical and Computer Engineering 2009年第4期34卷 158-166页

作者： Abdellali Kelil Alexei Nordell-Markovits Parakh Ousman Yassine Zaralahy Shengrui Wang Prospect US Data Mining and Bioinformatics laboratory Department of Computer Science Université de Sherbrook Sherbrooke QUE Canada

The rapid burgeoning of available data in the form of categorical sequences, such as biological sequences, natural language texts, network and retail transactions, makes the classification of categorical sequences increasingly important. The main challenge is to identify significant features hidden behind the chronological and structural dependencies characterizing their intrinsic properties. Almost all existing algorithms designed to perform this task are based on the matching of patterns in chronological order, but categorical sequences often have similar features in non-chronological order. In addition, these algorithms have serious difficulties in outperforming domain-specific algorithms. In this paper we propose CLASS, a general approach for the classification of categorical sequences. By using an effective matching scheme called SPM for Significant Patterns Matching, CLASS is able to capture the intrinsic properties of categorical sequences. Furthermore, the use of Latent Semantic Analysis allows capturing semantic relations using global information extracted from large number of sequences, rather than comparing merely pairs of sequences. Moreover, CLASS employs a classifier called SNN for Significant Nearest Neighbours, inspired from the K Nearest Neighbours approach with a dynamic estimation of K, which allows the reduction of both false positives and false negatives in the classification. The extensive tests performed on a range of datasets from different fields show that CLASS is oftentimes competitive with domain-specific approaches.

关键词： Proteins Matrix decomposition Indexes Training Semantics Testing

来源：评论

学校读者我要写书评

暂无评论

Machine learning identification of zeolite framework types

Machine learning identification of zeolite framework types

引用

2009 International Conference on Artificial Intelligence, ICAI 2009

作者： Yang, Shujiang Lach-Hab, Mohammed Vaisman, Iosif I. Blaisten-Barojas, Estela Computational Materials Science Center George Mason University Fairfax VA United States Department of Bioinformatics and Computational Biology George Mason University Manassas VA United States Department of Computational and Data Sciences George Mason University Manassas VA United States

ISBN: (纸本)9781601321091

The characteristic framework types of zeolite crystals are routinely determined by calculating coordination sequences and vertex symbols of the 3D crystal structures. This method has limitations and tends to fail when the synthesized crystals are not close to perfect and present some types of crystallographic disorder. A machine learning based Zeolite-Structure-Predictor (ZSP) model is developed to predict framework types for both near perfect and moderately disordered zeolite crystals. The ZSP uses various attributes, including topological descriptors based on a computational geometry approach and relevant physical, chemical properties of the crystals. Trained with 41 framework types, the ZSP can correctly classify zeolite crystals with over 98% accuracy. Additionally, it is shown that the ZSP model is able to predict the framework types for strongly disordered zeolite crystals with reliable success rate.

关键词： Crystal structure

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：