检索结果-内蒙古大学图书馆

Development and evaluation of a hospital-wide healthcare-associated surgical site infection detection algorithm

Journal of Microbiology, Immunology and Infection 2015年第2期48卷 S43-S43页

作者： Yi-Ju Tseng Bo-Chiang Huang Hui-Chi Lin Ming-Yuan Chen Rung-Ji Shang Wang-Huei Sheng Yee-Chun Chen Feipei Lai Shan-Chwen Chang Children's Hospital Informatics Program Boston Children's Hospital Boston MA USA Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan Center for Infection Control National Taiwan University Hospital (NTUH) Taiwan Information Systems Office NTUH Taipei Taiwan Department of Internal Medicine NTUH and College of Medicine Taiwan National Institute of Infectious Diseases and Vaccinology National Health Research Institutes Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan

来源：评论

学校读者我要写书评

暂无评论

Cerulean: A hybrid assembly using high throughput short and long reads

Cerulean: A hybrid assembly using high throughput short and ...

引用

13th Workshop on Algorithms in bioinformatics, WABI 2013

作者： Deshpande, Viraj Fung, Eric D. K. Pham, Son Bafna, Vineet Department of Computer Science and Engineering University of California San Diego CA United States Bioinformatics Undergraduate Program Department of Bioengineering University of California San Diego CA United States

ISBN: (纸本)9783642404528

Genome assembly using high throughput data with short reads, arguably, remains an unresolvable task in repetitive genomes, since when the length of a repeat exceeds the read length, it becomes difficult to unambiguously connect the flanking regions. The emergence of third generation sequencing (Pacific Biosciences) with long reads enables the opportunity to resolve complicated repeats that could not be resolved by the short read data. However, these long reads have high error rate and it is an uphill task to assemble the genome without using additional high quality short reads. Recently, Koren et al. 2012 [1] proposed an approach to use high quality short reads data to correct these long reads and, thus, make the assembly from long reads possible. However, due to the large size of both dataset (short and long reads), error-correction of these long reads requires excessively high computational resources, even on small bacterial genomes. In this work, instead of error correction of long reads, we first assemble the short reads and later map these long reads on the assembly graph to resolve repeats. Contribution: We present a hybrid assembly approach that is both computationally effective and produces high quality assemblies. Our algorithm first operates with a simplified version of the assembly graph consisting only of long contigs and gradually improves the assembly by adding smaller contigs in each iteration. In contrast to the state-of-the-art long reads error correction technique, which requires high computational resources and long running time on a supercomputer even for bacterial genome datasets, our software can produce comparable assembly using only a standard desktop in a short running time. © 2013 Springer-Verlag.

关键词： Error correction

来源：评论

学校读者我要写书评

暂无评论

Using dnase digestion data to accurately identify transcription factor binding sites 18

Using dnase digestion data to accurately identify transcript...

引用

18th Pacific Symposium on Biocomputing, PSB 2013

作者： Luo, Kaixuan Hartemink, Alexander J. Program in Computational Biology and Bioinformatics Duke University DurhamNC27708 United States Department of Computer Science Duke University DurhamNC27708 United States

Identifying binding sites of transcription factors (TFs) is a key task in deciphering transcriptional regulation. ChIP-based methods are used to survey the genomic locations of a single TF in each experiment. But methods combining DNase digestion data with TF binding specificity information could potentially be used to survey the locations of many TFs in the same experiment, provided such methods permit reasonable levels of sensitivity and specificity. Here, we present a simple such method that outperforms a leading recent method, centipede, marginally in human but dramatically in yeast (average auROC across 20 TFs increases from 74% to 94%). Our method is based on logistic regression and thus benefits from supervision, but we show that partially and completely unsupervised variants perform nearly as well. Because the number of parameters in our method is at least an order of magnitude smaller than centipede, we dub it millipede.

关键词： Transcription factors

来源：评论

学校读者我要写书评

暂无评论

Erratum to: Methods for evaluating medical tests and biomarkers

引用

Diagnostic and prognostic research 2017年第1期1卷 11页

作者： Gowri Gopalakrishna Miranda Langendam Rob Scholten Patrick Bossuyt Mariska Leeflang Anna Noel-Storr James Thomas Iain Marshall Byron Wallace Penny Whiting Clare Davenport Gowri GopalaKrishna Isabel de Salis Sue Mallett Robert Wolff Richard Riley Marie Westwood Jos Kleinen Gary Collins Hans Reitsma Karel Moons Antonia Zapf Annika Hoyer Katharina Kramer Oliver Kuss J Ensor J J Deeks E C Martin R D Riley Gerta Rücker Susanne Steinhauser Martin Schumacher Joie Ensor Kym Snell Brian Willis Thomas Debray Jon Deeks Lavinia Ferrante di Ruffano Sian Taylor-Phillips Chris Hyde Stuart A Taylor Gauraang Batnagar Lavinia Ferrante Di Ruffano Farah Seedat Aileen Clarke Sarah Byron Frances Nixon Rebecca Albrow Thomas Walker Carla Deakin Zhivko Zhelev Harriet Hunt Yaling Yang Lucy Abel James Buchanan Thomas Fanshawe Bethany Shinkins Laure Wynants Jan Verbakel Sabine Van Huffel Dirk Timmerman Ben Van Calster Aeliko Zwinderman Jason Oke Jack O'Sullivan Rafael Perera Brian Nicholson Hannah L Bromley Tracy E Roberts Adele Francis Denniis Petrie G Bruce Mann Kinga Malottki Holly Smith Lucinda Billingham Alice Sitch Oke Gerke Mie Holm-Vilstrup Eivind Antonsen Segtnan Ulrich Halekoh Poul Flemming Høilund-Carlsen Bernard G Francq Jac Dinnes Julie Parkes Walter Gregory Jenny Hewison Doug Altman William Rosenberg Peter Selby Julien Asselineau Paul Perez Aïssatou Paye Emilie Bessede Cécile Proust-Lima Christiana Naaktgeboren Joris de Groot Anne Rutjes Johannes Reitsma Emmanuel Ogundimu Jonathan Cook Yannick Le Manach Yvonne Vergouwe Romin Pajouheshnia Rolf Groenwold Karen Moons Linda Peelen Daan Nieboer Bavo De Cock Micael J Pencina Ewout W Steyerberg Jennifer Cooper Nick Parsons Chris Stinton Steve Smith Andy Dickens Rachel Jordan Alexandra Enocson David Fitzmaurice Peymane Adab Charles Boachie Gaj Vidmar Karoline Freeman Martin Connock Rachel Court Carl Moons Jessica Harris Andrew Mumford Zoe Plummer Kurtis Lee Barnaby Reeves Chris Rogers Veerle Verheyden Gianni D Angelini Gavin J Murphy Jeremy Huddy Melody Ni Katherine Good Graham Cooke 1Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 2Cochrane Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands. 234Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 49Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 3Cochrane Dementia and Cognitive Improvement Group University of Oxford Oxford UK. 4EPPI-Centre Department of Social Science University College London London UK. 5Division of Health and Social Care Research King's College London London UK. 6College of Computer and Information Science Northeastern University Boston USA. 13University Hospitals Bristol NHS Foundation Trust School of Social and Community Medicine Bristol UK. 275Institute of Applied Health Research University of Birmingham Birmingham UK. 9Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 10School of Social and Community Medicine University of Bristol Bristol UK. 179Institute of Applied Health Research University of Birmingham Birmingham UK. 12Kleijnen Systematic Reviews Ltd York UK. 25Research Institute for Primary Care and Health Sciences Keele University Keele UK. 188Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK. 191Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 237Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 18Department of Medical Statistics University Medical Center Göttingen Göttingen Germany. 19Institute for B

[This corrects the article DOI: 10.1186/s41512-016-0001-y.].

关键词：

来源：评论

学校读者我要写书评

暂无评论

Learning natural selection from the site frequency spectrum

引用

17th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2013

作者： Ronen, Roy Udpa, Nitin Halperin, Eran Bafna, Vineet Bioinformatics Graduate Program University of California San Diego CA United States International Computer Science Institute Berkeley CA United States Blavatnik School of Computer Science Tel-Aviv University Tel-Aviv Israel Department of Molecular Microbiology and Biotechnology Tel-Aviv University Tel-Aviv Israel Department of Computer Science and Engineering University of California San Diego CA United States

ISBN: (纸本)9783642371943

Genetic adaptation to external stimuli occurs through the combined action of mutation and selection. A central problem in genetics is to identify loci responsive to specific selective pressures. Over the last two decades, many tests have been proposed to identify genomic signatures of natural selection. However, the power of these tests changes unpredictably from one dataset to another, with no single dominant method. We build upon recent work that connects many of these tests in a common framework, by describing how positive selection strongly impacts the observed site frequency spectrum (SFS). Many of the proposed tests quantify the skew in SFS to predict selection. Here, we show that the skew depends on many parameters, including the selection coefficient, and time since selection. Moreover, for each of the different regimes of positive selection, informative features of the scaled SFS can be learned from simulated data and applied to population-scale variation data. Using support vector machines, we develop a test that is effective over all selection regimes. On simulated datasets, our test outperforms existing ones over the entire parameter space. We apply our test to variation data from Drosophila melanogaster populations adapted to hypoxia, and identify new loci that were missed by previous approaches, but strengthen the role of the Notch pathway in hypoxia tolerance. © 2013 Springer-Verlag.

关键词： Genome

来源：评论

学校读者我要写书评

暂无评论

A randomized algorithm for aligning DNA sequences to reference genomes

A randomized algorithm for aligning DNA sequences to referen...

引用

IEEE International Conference on Computational Advances in Bio and Medical sciences (ICCABS)

作者： Nam S. Vo Quang Tran Nobal Niraula Vinhthuy Phan Department of Computer Science University of Memphis Memphis TN Bioinformatics Program University of Memphis Memphis TN

ISBN: (纸本)9781479907151

The alignment of reads generated by next-generation sequencers is an important problem in many biomedical applications. Although many methods have been proposed, we introduce a new randomized algorithm with the distinction of having high performance across a wide range of read lengths and base error rates. We utilize two FM indices to facilitate efficient bidirectional searching. Randomization allows us to estimate effectively key parameters, which ultimately account for the consistency in performance of the method. Our method by and large outperformed some of the recent and popular methods over a wide range of read lengths and base error rates.

关键词： Genomics Frequency modulation Indexes Microorganisms bioinformatics Transforms DNA

来源：评论

学校读者我要写书评

暂无评论

Cell-cycle phenotyping with conditional random fields: A case study in Saccharomyces cerevisiae

Cell-cycle phenotyping with conditional random fields: A cas...

引用

IEEE International Symposium on Biomedical Imaging

作者： Michael B. Mayhew Alexander J. Hartemink Program in Computational Biology & Bioinformatics Duke University Durham NC USA Department of Computer Science Program in Computational Biology & Bioinformatics Duke University Durham NC USA

ISBN: (纸本)9781467364560

High-resolution, multimodal microscopy grants an intimate view of the inner workings of cells. Complex processes like cell division can be monitored with microscope images, assuming identification of cells and their cell-cycle markers: cellular structures indicative of cell-cycle progress. Here, we explore how spatial relationships between these markers can facilitate their identification. We grew and synchronized Saccharomyces cerevisiae cell cultures and then acquired multimodal image data as the cells proceeded through the cell cycle. We trained a conditional random field model to capture pixel-level spatial relationships among three different cell-cycle markers observable in our images. We observed good predictive performance of this pixel-level model on three held-out test images, and performance improved when we used marker-level information from our training data to prune model predictions. Our results support the use of conditional random fields in bioimage labeling and encourage the use of as much multiscale information as available in training data when identifying cell-cycle markers.

关键词： Labeling Predictive models Biological system modeling Training data Microscopy Data models

来源：评论

学校读者我要写书评

暂无评论

Abstracts from the 3rd International Genomic Medicine Conference (3rd IGMC 2015) : Jeddah, Kingdom of Saudi Arabia. 30 November - 3 December 2015

引用

BMC genomics 2016年第6期17 Suppl 6卷 487页

作者： Jerry W. Shay Noriko Homma Ruyun Zhou Muhammad Imran Naseer Adeel G. Chaudhary Mohammed Al-Qahtani Nobutaka Hirokawa Maryam Goudarzi Albert J. Fornace Saleh Baeesa Deema Hussain Mohammed Bangash Fahad Alghamdi Hans-Juergen Schulten Angel Carracedo Ishaq Khan Hanadi Qashqari Nawal Madkhali Mohamad Saka Kulvinder S. Saini Awatif Jamal Jaudah Al-Maghrabi Adel Abuzenadah Adeel Chaudhary Mohammed Al Qahtani Ghazi Damanhouri Heba Alkhatabi Anne Goodeve Laura Crookes Nikolas Niksic Nicholas Beauchamp Adel M. Abuzenadah Jim Vaught Bruce Budowle Mourad Assidi Abdelbaset Buhmeida Leena Merdad Sudhir Kumar Sayaka Miura Karen Gomez Mahmood Rasool Ahmed Rebai Sajjad Karim Hend F. Nour Eldin Heba Abusamra Elham M. Alhathli Nada Salem Mohammed H. Al-Qahtani Hossam Faheem Ashok Agarwa Eberhard Nieschlag Joachim Wistuba Oliver S. Damm Mohd A. Beg Taha A. Abdel-Meguid Hisham A. Mosli Osama S. Bajouh Serdar Coskun Muhammad Abu-Elmagd Ashraf Dallol Sahar Hakamy Wejdan Al-Qahtani Asia Al-Harbi Shireen Hussain Burak Ozkosem Rick DuBois Safia S. Messaoudi Maryam T. Dandana Touhami Mahjoub Wassim Y. Almawi S. Abdalla M. Nabil Al-Aama Asmaa Elzawahry Tsuyoshi Takahashi Sachiyo Mimaki Eisaku Furukawa Rie Nakatsuka Isao Kurosaka Takahiko Nishigaki Hiromi Nakamura Satoshi Serada Tetsuji Naka Seiichi Hirota Tatsuhiro Shibata Katsuya Tsuchihara Toshirou Nishida Mamoru Kato Sajid Mehmood Naeem Mahmood Ashraf Awais Asif Muhammad Bilal Malik Siddique Mehmood Aadil Hussain Qazi Mohammad Sajid Jamal Mughees Uddin Siddiqui Mohammad A. Alzohairy Mohammad A. Al Karaawi Taoufik Nedjadi Heba Al-Khattabi Adel Al-Ammari Ahmed Al-Sayyad Hédia Zitouni Nozha Raguema Marwa Ben Ali Wided Malah Raja Lfalah Wassim Almawi Mohammed Elanbari Andrey Ptitsyn Sana Mahjoub Rabeb El Ghali Bechir Achour Nidhal Ben Amor Brahim N’siri Hamid Morjani Esam Azhar Vera Chayeb Maryam Dendena Hedia Zitouni Khedija Zouari-Limayem Bassem Refaat Ahmed M. Ashshi Sarah A. Batwa Hazem Ramadan Amal Awad Ahmed Ateya Adel Galal Ahmed El-Shemi Ahmad Ashshi Mohammed Basalamah Youjin Na Cha University of Texas Southwestern Medical Center Dallas USA Department of Cell Biology and Anatomy Graduate School of Medicine the University of Tokyo Tokyo Japan Center of Excellence in Genomic Medicine Research King Abdulaziz University Jeddah Saudi Arabia Department of Biochemistry and Molecular & Cellular Biology Lombardi Comprehensive Cancer Center Georgetown University Washington USA Department of Oncology Lombardi Comprehensive Cancer Center Georgetown University Washington USA Department of Radiation Medicine Lombardi Comprehensive Cancer Center Georgetown University Washington USA Center of Excellence in Genomic Medicine Research (CEGMR) King Abdulaziz University Jeddah Kingdom of Saudi Arabia King Fahd Medical Research Centre King Abdulaziz University Jeddah Saudi Arabia Centre of Excellence in Genomic Medicine Research King Abdulaziz University Jeddah Saudi Arabia Sheffield Diagnostic Genetic Service Sheffield Children’s NHS Foundation Trust Western Bank Sheffield UK Center of Innovations in Personalized Medicine King Abdulaziz University Jeddah Saudi Arabia President ISBER Editor-in-Chief Biopreservation & Biobanking ᅟ USA Institute of Applied Genetics Department of Molecular and Medical Genetics University of North Texas Health Science Center Fort Worth USA Department of Dental Public Health Faculty of Dentistry King Abdulaziz University Jeddah Saudi Arabia Institute for Genomics and Evolutionary Medicine Temple University Philadelphia USA Center of Excellence in Genomic Medicine King Abdulaziz University Jeddah Kingdom of Saudi Arabia Laboratory of Molecular and Cellular Screening Processes (Bioinformatics Group) Centre of Biotechnology of Sfax Sfax Tunisia Fujitsu Technology Solutions International ᅟ ᅟ Case Western Reserve University School of Medicine and Lerner College of Medicine Cleveland Clinic Foundation Cleveland USA American Center for Reproductive Medicine Andrology Center Cleveland USA Center for Reproductive Medicine and

O1 Regulation of genes by telomere length over long distances Jerry W. Shay O2 The microtubule destabilizer KIF2A regulates the postnatal establishment of neuronal circuits in addition to prenatal cell survival, cell migration, and axon elongation, and its loss leading to malformation of cortical development and severe epilepsy Noriko Homma, Ruyun Zhou, Muhammad Imran Naseer, Adeel G. Chaudhary, Mohammed Al-Qahtani, Nobutaka Hirokawa O3 Integration of metagenomics and metabolomics in gut microbiome research Maryam Goudarzi, Albert J. Fornace Jr. O4 A unique integrated system to discern pathogenesis of central nervous system tumors Saleh Baeesa, Deema Hussain, Mohammed Bangash, Fahad Alghamdi, Hans-Juergen Schulten, Angel Carracedo, Ishaq Khan, Hanadi Qashqari, Nawal Madkhali, Mohamad Saka, Kulvinder S. Saini, Awatif Jamal, Jaudah Al-Maghrabi, Adel Abuzenadah, Adeel Chaudhary, Mohammed Al Qahtani, Ghazi Damanhouri O5 RPL27A is a target of miR-595 and deficiency contributes to ribosomal dysgenesis Heba Alkhatabi O6 Next generation DNA sequencing panels for haemostatic and platelet disorders and for Fanconi anaemia in routine diagnostic service Anne Goodeve, Laura Crookes, Nikolas Niksic, Nicholas Beauchamp O7 Targeted sequencing panels and their utilization in personalized medicine Adel M. Abuzenadah O8 International biobanking in the era of precision medicine Jim Vaught O9 Biobank and biodata for clinical and forensic applications Bruce Budowle, Mourad Assidi, Abdelbaset Buhmeida O10 Tissue microarray technique: a powerful adjunct tool for molecular profiling of solid tumors Jaudah Al-Maghrabi O11 The CEGMR biobanking unit: achievements, challenges and future plans Abdelbaset Buhmeida, Mourad Assidi, Leena Merdad O12 Phylomedicine of tumors Sudhir Kumar, Sayaka Miura, Karen Gomez O13 Clinical implementation of pharmacogenomics for colorectal cancer treatment Angel Carracedo, Mahmood Rasool O14 From association to causality: translation of GWAS findings for genomic me

关键词：

来源：评论

学校读者我要写书评

暂无评论

Gene expression network analysis reveals pathway interactions of the IGF axis in breast cancer

Gene expression network analysis reveals pathway interaction...

引用

5th International Conference on bioinformatics and Computational Biology 2013, BICoB 2013

作者： Ramadan, Emad Perincheri, Sudhir Sprecher, Emmett Harris, Lyndsay Tuck, David Information and Computer Science Department King Fahd University Dhahran Saudi Arabia Department of Pathology Yale University New Haven United States Computational Biology and Bioinformatics Program Yale University New Haven United States Division of Hematology and Oncology Case Western Reserve University Cleveland United States

ISBN: (纸本)9781622769711

Background: Understanding crosstalk and feedback among oncogenic pathways is a critical challenge to overcoming resistance to targeted anticancer therapy. The topology of biological networks has increasingly been used to complement studies based on individual genes or gene collections. We apply network analysis to understand interactions among critical biological signaling pathways in breast cancer. Results: An overlapping clustering of the networks showed highly distinct patterns in patients with high IGF versus low IGF axis. We demonstrate through cluster comparison metrics and permutation studies that these differences are unlikely to occur by chance. IRS-1, an IGF adaptor protein, is shown to have a highly central place in the IGF high as compared to the IGF low networks. We further demonstrate that network connections reveal information about interactions of genes in TGF-beta, MAPK, and other pathways known to interact with IGF. Conclusions: Network analyses can provide novel insights and hypotheses about signaling pathways involved in feedback and crosstalk in breast cancer.

关键词： Crosstalk

来源：评论

学校读者我要写书评

暂无评论

Using Folding Ensemble and Stem Probability Maximization Methods to Predict RNA H-Type Pseudoknots

引用

Tsinghua science and Technology 2012年第6期17卷 691-700页

作者： Junilda Spirollari Shawn Xiong Wang Jason T.L. Wang Bioinformatics Program Department of Computer Science New Jersey Institute of Technology University Heights Department of Computer Science California State University

We present in this paper an ab initio method, named KnotFold, for RNA H-type pseudoknot prediction. Our method employs an ensemble of RNA folding tools and a filtering heuristic to generate a set of pseudoknot-free stems, and then predicts pseudoknots by utilizing a search technique with a pseudo-probability scoring scheme. Experimental results show that KnotFold achieves higher sensitivity than existing methods. The KnotFold package with documentation is freely available at http：//***/KnotFold.

关键词： RNA structure pseudoknots tool ensemble

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：