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arXiv 2019年

作者： van der Wijst, M.G. de Vries, D.H. Groot, H.E. Trynka, G. Hon, C.C. Nawijn, M.C. Idaghdour, Y. van der Harst, P. Ye, C.J. Powell, J. Theis, F.J. Mahfouz, A. Heinig, M. Franke, L. Department of Genetics Oncode Institute University of Groningen University Medical Center Groningen Groningen Netherlands Department of Cardiology University of Groningen University Medical Center Groningen Groningen Netherlands Wellcome Trust Sanger Institute Open Targets Wellcome Genome Campus Hinxton Cambridge United Kingdom RIKEN Center for Integrative Medical Sciences Yokohama Japan Department of Pathology and Medical Biology GRIAC Research institute University of Groningen University Medical Center Groningen Netherlands Program in Biology Public Health Research Center New York University Abu Dhabi United Arab Emirates Institute for Human Genetics Bakar Computational Health Sciences Institute Bakar ImmunoX Initiative Division of Rheumatology Department of Medicine Department of Bioengineering and Therapeutic Sciences Department of Epidemiology and Biostatistics Chan Zuckerberg Biohub University of California San Francisco San Francisco United States Garvan‐Weizmann Centre for Cellular Genomics Garvan Institute Sydney Australia UNSW Cellular Genomics Futures Institute University of New South Wales Sydney Australia Institute of Computational Biology Helmholtz Zentrum München Neuherberg Germany Department of Mathematics Technical University of Munich Garching bei München Germany Leiden Computational Biology Center Leiden University Medical Center Leiden Netherlands Delft Bioinformatics Lab Delft University of Technology Delft Netherlands Institute of Computational Biology Helmholtz Zentrum München Neuherberg Germany Department of Informatics Technical University of Munich Garching bei München Germany

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease‐associated genetic risk factors through so‐called expression quantitative trait locus (eQTL) analysis. Single‐cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. The enormous increase in throughput and reduction in cost per cell now allow this technology to be applied to large‐scale population genetics studies. Therefore, we have founded the single‐cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing disease‐causing genetic variants and identifying the cellular contexts in which they affect gene expression. Ultimately, this information can enable development of personalized medicine. Here, we outline the goals, approach, potential utility and early proofs‐of-concept of the sc‐eQTLGen consortium. We also provide a set of study design considerations for future single‐cell eQTL studies. Copyright © 2019, The Authors. All rights reserved.

关键词： RNA

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Correction to: Dynamic linear models guide design and analysis of microbiota studies within artificial human guts

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Microbiome 2018年第1期6卷 212页

作者： Justin D Silverman Heather K Durand Rachael J Bloom Sayan Mukherjee Lawrence A David Program in Computational Biology and Bioinformatics Duke University CIEMAS Room 2171 101 Science Drive Box 3382 Durham NC 27708 USA. Medical Scientist Training Program Duke University Durham NC 27708 USA. Center for Genomic and Computational Biology Duke University Durham NC 27708 USA. Department of Molecular Genetics and Microbiology Duke University Durham NC 27708 USA. University Program in Genetics and Genomics Duke University Durham NC 27708 USA. Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University Durham NC 27708 USA. Program in Computational Biology and Bioinformatics Duke University CIEMAS Room 2171 101 Science Drive Box 3382 Durham NC 27708 USA. lawrence.david@duke.edu. Center for Genomic and Computational Biology Duke University Durham NC 27708 USA. lawrence.david@duke.edu. University Program in Genetics and Genomics Duke University Durham NC 27708 USA. lawrence.david@duke.edu. Department of Molecular Genetics and Microbiology Duke University Durham NC 27708 USA. lawrence.david@duke.edu.

AbstractFollowing publication of the original article [1], the authors noticed an error in the presentation of equations in the PDF version.

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Author Correction: Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

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Nature communications 2020年第1期11卷 6232页

作者： Matthew H Bailey William U Meyerson Lewis Jonathan Dursi Liang-Bo Wang Guanlan Dong Wen-Wei Liang Amila Weerasinghe Shantao Li Yize Li Sean Kelso Gordon Saksena Kyle Ellrott Michael C Wendl David A Wheeler Gad Getz Jared T Simpson Mark B Gerstein Li Ding The McDonnell Genome Institute at Washington University St. Louis MO 63108 USA. Division of Oncology Department of Medicine Washington University School of Medicine St. Louis MO 63108 USA. Alvin J. Siteman Cancer Center Washington University School of Medicine St. Louis MO 63108 USA. Yale School of Medicine Yale University New Haven CT 06520 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. Computational Biology Program Ontario Institute for Cancer Research Toronto ON M5G 0A3 Canada. The Hospital for Sick Children Toronto ON M5G 1X8 Canada. Broad Institute of MIT and Harvard Cambridge MA 02142 USA. Biomedical Engineering Oregon Health and Science University Portland OR 97239 USA. Department of Mathematics Washington University in St. Louis St. Louis MO 63130 USA. Department of Genetics Washington University School of Medicine St.Louis MO 63110 USA. Human Genome Sequencing Center Baylor College of Medicine Houston TX 77030 USA. Department of Molecular and Human Genetics Baylor College of Medicine Houston TX 77030 USA. Harvard Medical School Boston MA 02115 USA. Center for Cancer Research Massachusetts General Hospital Boston MA 02114 USA. Department of Pathology Massachusetts General Hospital Boston MA 02114 USA. Department of Computer Science University of Toronto Toronto ON M5S Canada. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. mark.gerstein@yale.edu. Department of Computer Science Yale University New Haven CT 06520 USA. mark.gerstein@yale.edu. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. mark.gerstein@yale.edu. The McDonnell Genome Institute at Washington University St. Louis MO 63108 USA. lding@wustl.edu. Division of Oncology Department of Medicine Washington University School of Medicine St. Louis MO 63108 USA. lding@wustl.edu. Alvin J. Siteman Cancer Center Wash

Correction to this paper has been published: https://***/10.1038/s41467-020-20128-w.

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Differentiation and Integration of Machine Learning Feature Vectors

Differentiation and Integration of Machine Learning Feature ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Xinying Mu Ana B. Pavel Mark Kon Department of Mathematics and Statistics Boston University Boston MA Graduate Program in Bioinformatics Boston University Boston MA Section of Computational Biomedicine School of Medicine Boston MA

ISBN: (纸本)9781509061686

This paper presents a new approach to the production of feature maps for the improvement of classification in machine learning. The idea is based on a calculus of differentiation and integration of feature vectors, which can be viewed as functions on a metric space or network. Based on this we propose a novel network-based binary machine learning classifier. We illustrate our method using molecular networks alone to distinguish phenotypes, including cancer types and subtypes. We include feature sets derived from disease-specific gene co-expression networks in different cancer data sets using The Cancer Genome Atlas (TCGA) along with other previously published studies. We also illustrate our network-based predictor on another data type, based on infrared spectroscopy of lung cancer tissue.

关键词： Cancer Gene expression Correlation Lungs Kernel Laplace equations Proteins

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Incorporation of protein binding effects into likelihood ratio test for exome sequencing data

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BMC proceedings 2016年第SUPPL 7期10卷 275-281页

作者： Dongni Zhang Hongzhu Cui Dmitry Korkin Zheyang Wu Mathematics Department Worcester Polytechnic Institute 100 Institute Road Worcester MA 01609-2280 USA. Computer Science Department Bioinformatics and Computational Biology Program Worcester Polytechnic Institute 100 Institute Road Worcester MA 01609-2280 USA.

Statistical association studies are an important tool in detecting novel disease genes. However, for sequencing data, association studies confront the challenge of low power because of relatively small data sample size and rare variants. Incorporating biological information that reflects disease mechanism is likely to strengthen the association evidence of disease genes, and thus increase the power of association studies. In this paper, we annotate non-synonymous single-nucleotide variants according to protein binding sites (BSs) by using a more accurate BS prediction method. We then incorporate this information into association study through a statistical framework of likelihood ratio test (LRT) based on weighted burden score of single-nucleotide variants (SNVs). The strategy is applied to Genetic Analysis Workshop 19 exome-sequencing data for detecting novel genes associated to hypotension. The SNV-weighting LRT idea is empirically verified by the simulated phenotypes (336 cases and 1607 controls), and the weights based on BS annotation are applied to the real phenotypes (394 cases and 1457 controls). Such strategy of weighting the prior information on protein functional sites is shown to be superior to the unweighted LRT and serves as a good complement to the existing association tests. Several putative genes are reported; some of them are functionally related to hypertension according to the previous evidence in the literature.

关键词： Likelihood Ratio Test Real Data Analysis Burden Score Data Sample Size Genomic Inflation Factor

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Author Correction: Determination of RNA structural diversity and its role in HIV-1 RNA splicing

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Nature 2020年第7837期588卷 E16页

作者： Phillip J Tomezsko Vincent D A Corbin Paromita Gupta Harish Swaminathan Margalit Glasgow Sitara Persad Matthew D Edwards Lachlan Mcintosh Anthony T Papenfuss Ann Emery Ronald Swanstrom Trinity Zang Tammy C T Lan Paul Bieniasz Daniel R Kuritzkes Athe Tsibris Silvi Rouskin Whitehead Institute for Biomedical Research Cambridge MA USA. Program in Virology Harvard Medical School Boston MA USA. Brigham and Women's Hospital Boston MA USA. Bioinformatics Division Walter and Eliza Hall Institute Parkville Victoria Australia. Department of Medical Biology The University of Melbourne Melbourne Victoria Australia. Massachusetts Institute of Technology Cambridge MA USA. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA. Peter MacCallum Cancer Centre Melbourne Victoria Australia. Department of Mathematics and Statistics University of Melbourne Melbourne Victoria Australia. Sir Peter MacCallum Department of Oncology The University of Melbourne Melbourne Victoria Australia. Curriculum in Genetics and Molecular Biology University of North Carolina at Chapel Hill Chapel Hill NC USA. Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Microbiology and Immunology University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Biochemistry and Biophysics University of North Carolina at Chapel Hill Chapel Hill NC USA. Laboratory of Retrovirology The Rockefeller University New York NY USA. Howard Hughes Medical Institute The Rockefeller University New York NY USA. Department of Medicine Harvard Medical School Boston MA USA. Whitehead Institute for Biomedical Research Cambridge MA USA. srouskin@wi.mit.edu.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Drug side effect prediction through linear neighborhoods and multiple data source integration

Drug side effect prediction through linear neighborhoods and...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Wen Zhang Yanlin Chen Shikui Tu Feng Liu Qianlong Qu School of Computer Wuhan University Wuhan China School of Mathematics and Statistics Wuhan University Wuhan China Program in Bioinformatics and Integrative Biology University of Massachusetts Medical School Worcester MA USA International School of software Wuhan University Wuhan China

ISBN: (纸本)9781509016129

Predicting drug side effects is a critical task in the drug discovery, which attracts great attentions in both academy and industry. Although lots of machine learning methods have been proposed, great challenges arise with boom of precision medicine. On one hand, many methods are based on the assumption that similar drugs may share same side effects, but measuring the drug-drug similarity appropriately is challenging. One the other hand, multi-source data provide diverse information for the analysis of side effects, and should be integrated for the high-accuracy prediction. In this paper, we tackle the side effect prediction problem through linear neighborhoods and multi-source data integration. In the feature space, linear neighborhoods are constructed to extract the drug-drug similarity, namely “linear neighborhood similarity”. By transferring the similarity into the side effect space, known side effect information is propagated through the similarity-based graph. Thus, we propose the linear neighborhood similarity method (LNSM), which utilizes single-source data for the side effect prediction. Further, we extend LNSM to deal with multi-source data, and propose two data integration methods: similarity matrix integration method (LNSM-SMI) and cost minimization integration method (LNSM-CMI), which integrate drug substructure data, drug target data, drug transporter data, drug enzyme data, drug pathway data and drug indication data to improve the prediction accuracy. The proposed methods are evaluated on the benchmark datasets. The linear neighborhood similarity method (LNSM) can produce satisfying results on the single-source data. Data integration methods (LNSM-SMI and LNSM-CMI) can effectively integrate multi-source data, and outperform other state-of-the-art side effect prediction methods in the cross validation and independent test. The proposed methods are promising for the drug side effect prediction.

关键词： Drugs Data integration Biochemistry Chemicals Predictive models Databases Learning systems

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The Importance of Surface-Binding Site towards Starch-Adsorptivity Level in α -Amylase: A Review on Structural Point of View

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Enzyme Research 2017年第1期2017卷 4086845页

作者： Baroroh, Umi Yusuf, Muhammad Rachman, Saadah Diana Ishmayana, Safri Syamsunarno, Mas Rizky A. A. Levita, Jutti Subroto, Toto Master of Biotechnology Program Postgraduate School Universitas Padjadjaran Jl. Dipati Ukur 35 Bandung West Java Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Jl. Raya Bandung-Sumedang Km 21 Jatinangor Sumedang West Java 45363 Indonesia Research Center of Molecular Biotechnology and Bioinformatics Universitas Padjadjaran Jl. Singaperbangsa 2 Bandung West Java 40133 Indonesia Department of Pharmacology and Clinical Pharmacy Faculty of Pharmacy Universitas Padjadjaran Jl. Raya Bandung-Sumedang Km 21 Jatinangor Sumedang West Java 45363 Indonesia

Starch is a polymeric carbohydrate composed of glucose. As a source of energy, starch can be degraded by various amylolytic enzymes, including α-amylase. In a large-scale industry, starch processing cost is still expensive due to the requirement of high temperature during the gelatinization step. Therefore, α-amylase with raw starch digesting ability could decrease the energy cost by avoiding the high gelatinization temperature. It is known that the carbohydrate-binding module (CBM) and the surface-binding site (SBS) of α-amylase could facilitate the substrate binding to the enzyme's active site to enhance the starch digestion. These sites are a noncatalytic module, which could interact with a lengthy substrate such as insoluble starch. The major interaction between these sites and the substrate is the CH/pi-stacking interaction with the glucose ring. Several mutation studies on the Halothermothrix orenii, SusG Bacteroides thetaiotamicron, Barley, Aspergillus Niger, and Saccharomycopsis fibuligera α-amylases have revealed that the stacking interaction through the aromatic residues at the SBS is essential to the starch adsorption. In this review, the SBS in various α-amylases is also presented. Therefore, based on the structural point of view, SBS is suggested as an essential site in α-amylase to increase its catalytic activity, especially towards the insoluble starch. © 2017 Umi Baroroh et al.

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GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

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Nature genetics 2023年第9期55卷 1471-1482页

作者： Remi Stevelink Dorothee Kasteleijn-Nolst Trenite Bobby P. C. Koeleman Dick Lindhout Anja C. M. Sonsma Ciarán Campbell Gianpiero L. Cavalleri Norman Delanty Hany El-Naggar Austin Lacey Peter Widdess-Walsh Colin P. Doherty Siwei Chen Claire Churchhouse Mark J. Daly Yen-Chen A. Feng Daniel P. Howrigan Mitja I. Kurki Benjamin M. Neale Aarno Palotie Wei Zhou Namrata Gupta Henrike O. Heyne Bassel Abou-Khalil Kevin F. Haas R. S. Taneja Oluyomi M. Adesoji Michael Nothnagel Peter Nürnberg Thomas Sander Zaid Afawi Ilan Blatt Amos D. Korczyn Elisabetta Amadori Ganna Balagura Antonella Riva Marcello Scala Paolo Scudieri Pasquale Striano Federico Zara Michele Iacomino Francesca Madia Alison Anderson Patrick Kwan Terence J. O’Brien Slavé Petrovski K. Meng Tan Marian Todaro Joseph Anderson Danielle M. Andrade Quratulain Zulfiqar Ali Grazia Annesi Pauls Auce Andreja Avbersek Simona Balestrini Vera Braatz Claudia B. Catarino Krishna Chinthapalli Jennifer Jamnadas-Khoda Dalia Kasperavičiūte Costin Leu Josemir W. Sander Sanjay M. Sisodiya Sara Zagaglia Melanie Bahlo Karen L. Oliver Mark D. Baker Beata Fonferko-Shadrach William O. Pickrell Carmen Barba Claudia Bianchini Viola Doccini Renzo Guerrini Davide Mei Martino Montomoli Elena Parrini Annalisa Vetro Karen Barboza Fabrice Bartolomei Thomas Bast Bernhard J. Steinhoff Larry Baum Stacey S. Cherny Pak Sham Tobias Baumgartner Christian E. Elger Kerstin Hallmann Wolfram S. Kunz Rainer Surges Randi von Wrede Gábor Zsurka Betül Baykan Nerses Bebek Yeşim Kesim Albert J. Becker Susanne Schoch Felicitas Becker Caitlin A. Bennett Samuel F. Berkovic Stephanie L. Leech Brigid M. Regan Ingrid E. Scheffer Bianca Berghuis Ahmad Beydoun Wassim Nasreddine Francesca Bisulli Lidia Di Vito Lorenzo Ferri Tania Giangregorio Laura Licchetta Raffaella Minardi Barbara Mostacci Carlotta Stipa Paolo Tinuper Lorenzo Muccioli Federica Pondrelli Dheeraj R. Bobbili Emadeldin Hassanin Roland Krause Zied Landoulsi Patrick May Ingo Borggraefe Christian Bosselmann Christian Hengsbach Josua Kegele Stephen Lauxmann Department of Genetics University Medical Center Utrecht Utrecht The Netherlands Stichting Epilepsie Instellingen Nederland (SEIN) Heemstede The Netherlands School of Pharmacy and Biomolecular Sciences The Royal College of Surgeons in Ireland Dublin Ireland The FutureNeuro Research Centre Dublin Ireland Department of Neurology Beaumont Hospital Dublin Ireland Department of Neurology St. James’s Hospital Dublin Ireland Analytic and Translational Genetics Unit Department of Medicine Massachusetts General Hospital and Harvard Medical School Boston MA USA Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of Harvard and M.I.T Cambridge MA USA Institute for Molecular Medicine Finland (FIMM) University of Helsinki Helsinki Finland Psychiatric & Neurodevelopmental Genetics Unit Department of Psychiatry Massachusetts General Hospital and Harvard Medical School Boston MA USA Division of Biostatistics Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei Taiwan Hasso Plattner Institute Digital Health Center University of Potsdam Potsdam Germany Hasso Plattner Institute Mount Sinai School of Medicine New York City NY USA Department of Neurology Vanderbilt University Medical Center Nashville TN USA Cologne Center for Genomics (CCG) University of Cologne Faculty of Medicine and University Hospital Cologne Cologne Germany University Hospital Cologne Cologne Germany Department of Neurology Charité Universitaetsmedizin Berlin Campus Virchow-Clinic Berlin Germany Tel-Aviv University Sackler Faculty of Medicine Ramat Aviv Israel Department of Neurology Sheba Medical Center Ramat Gan Israel Department of Neurosciences Rehabilitation Ophthalmology Genetics Maternal and Child Health University of Genova Genova Italy IRCCS Istituto Giannina Gaslini Genova Italy Department of Medicine University of Melbour

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment.

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Erratum to: Integrative analysis of multi-omics data for identifying multi-markers for diagnosing pancreatic cancer

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BMC genomics 2017年第1期18卷 88页

作者： Min-Seok Kwon Yongkang Kim Seungyeoun Lee Junghyun Namkung Taegyun Yun Sung Gon Yi Sangjo Han Meejoo Kang Sun Whe Kim Jin-Young Jang Taesung Park Interdisciplinary program in Bioinformatics Seoul National University Seoul Korea. Department of Statistics Seoul National University Seoul Korea. Department of Mathematics and Statistics Sejong University Seoul Korea. Immunodiagnostics R&D Team IVD Business Unit New Business Division SK telecom Co Seongnam Korea. Department of Surgery Seoul National University Hospital Seoul Korea. Department of Surgery Seoul National University Hospital Seoul Korea. jangjy4@***. Interdisciplinary program in Bioinformatics Seoul National University Seoul Korea. tspark@stats.snu.ac.kr. Department of Statistics Seoul National University Seoul Korea. tspark@stats.snu.ac.kr.

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