检索结果-内蒙古大学图书馆

Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets

引用

BMC Systems Biology 2014年第1期8卷 1-19页

作者： Christadore, Lisa M. Pham, Lisa Kolaczyk, Eric D. Schaus, Scott E. Department of Chemistry Boston University Boston MA United States Program in Bioinformatics and Mathematics Boston University Boston MA United States Department of Mathematics and Statistics Boston University Boston MA United States

Background: Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. Results: S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug's transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions - exposure time and concentration and (ii) Network training conditions - training compendium modifications. Two analyses of SSEM-Lasso output - gene set and single gene - were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. Conclusions: This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved. © 2014 Chri

关键词：

来源：评论

学校读者我要写书评

暂无评论

Bayesian group factor analysis with structured sparsity

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Shiwen Zhao Chuan Gao Sayan Mukherjee Barbara E. Engelhardt Google MPI for Intelligent Systems Computational Biology and Bioinformatics Program Department of Statistical Science Duke University Durham NC Department of Statistical Science Duke University Durham NC Departments of Statistical Science Computer Science Mathematics Duke University Durham NC Department of Computer Science Center for Statistics and Machine Learning Princeton University Princeton NJ

Latent factor models are the canonical statistical tool for exploratory analyses of low-dimensional linear structure for a matrix of p features across n samples. We develop a structured Bayesian group factor analysis model that extends the factor model to multiple coupled observation matrices; in the case of two observations, this reduces to a Bayesian model of canonical correlation analysis. Here, we carefully define a structured Bayesian prior that encourages both element-wise and column-wise shrinkage and leads to desirable behavior on high-dimensional data. In particular, our model puts a structured prior on the joint factor loading matrix, regularizing at three levels, which enables element-wise sparsity and unsupervised recovery of latent factors corresponding to structured variance across arbitrary subsets of the observations. In addition, our structured prior allows for both dense and sparse latent factors so that covariation among either all features or only a subset of features can be recovered. We use fast parameter-expanded expectation-maximization for parameter estimation in this model. We validate our method on simulated data with substantial structure. We show results of our method applied to three high-dimensional data sets, comparing results against a number of state-of-the-art approaches. These results illustrate useful properties of our model, including i) recovering sparse signal in the presence of dense effects; ii) the ability to scale naturally to large numbers of observations; iii) flexible observation- and factor-specific regularization to recover factors with a wide variety of sparsity levels and percentage of variance explained; and iv) tractable inference that scales to modern genomic and text data sizes.

关键词： bayesian structured sparsity canonical correlation analysis mixture models parameter expansion sparse and low-rank matrix decomposition sparse priors

来源：评论

学校读者我要写书评

暂无评论

Creation and analysis of biochemical constraint-based models: The COBRA Toolbox v3.0

arXiv

引用

arXiv 2017年

作者： Heirendt, Laurent Arreckx, Sylvain Pfau, Thomas Mendoza, Sebastián N. Richelle, Anne Heinken, Almut Haraldsdóttir, Hulda S. Wachowiak, Jacek Keating, Sarah M. Vlasov, Vanja Magnusdóttir, Stefania Ng, Chiam Yu Preciat, German Žagare, Alise Chan, Siu H.J. Aurich, Maike K. Clancy, Catherine M. Modamio, Jennifer Sauls, John T. Noronha, Alberto Bordbar, Aarash Cousins, Benjamin El Assal, Diana C. Valcarcel, Luis V. Apaolaza, Iñigo Ghaderi, Susan Ahookhosh, Masoud Guebila, Marouen Ben Kostromins, Andrejs Sompairac, Nicolas Le, Hoai M. Ma, Ding Sun, Yuekai Wang, Lin Yurkovich, James T. Oliveira, Miguel A.P. Vuong, Phan T. El Assal, Lemmer P. Kuperstein, Inna Zinovyev, Andrei Scott Hinton, H. Bryant, William A. Aragón Artacho, Francisco J. Planes, Francisco J. Stalidzans, Egils Maass, Alejandro Vempala, Santosh Hucka, Michael Saunders, Michael A. Maranas, Costas D. Lewis, Nathan E. Sauter, Thomas Palsson, Bernhard Ø. Thiele, Ines Fleming, Ronan M.T. Luxembourg Centre for Systems Biomedicine University of Luxembourg 6 avenue du Swing BelvauxL-4367 Luxembourg Life Sciences Research Unit University of Luxembourg 6 avenue du Swing BelvauxL-4367 Luxembourg University of Chile Blanco Encalada 2085 Santiago Chile Department of Pediatrics University of California San Diego School of Medicine San DiegoCA92093 United States Hinxton CambridgeCB10 1SD United Kingdom Department of Chemical Engineering Pennsylvania State University University University ParkPA16802 United States Department of Physics University of California San Diego 9500 Gilman Dr. San DiegoCA92093 United States Bioinformatics and Systems Biology Program University of California San Diego San DiegoCA United States Sinopia Biosciences San DiegoCA United States School of Computer Science Algorithms and Randomness Center Georgia Institute of Technology AtlantaGA United States Biomedical Engineering and Sciences Department TECNUN University of Navarra Paseo de Manuel Lardizabal 13 San Sebastian20018 Spain Institute of Microbiology and Biotechnology University of Latvia Jelgavas iela 1 RigaLV-1004 Latvia Department of Management Science and Engineering Stanford University StanfordCA94305-4026 United States Bioengineering Department University of California San Diego San DiegoCA United States Utah State University Research Foundation 1695 North Research Park Way North LoganUT84341 United States Centre for Integrative Systems Biology and Bioinformatics Department of Life Sciences Imperial College London London United Kingdom Department of Mathematics University of Alicante Spain California Institute of Technology Computing and Mathematical Sciences MC 305-16 1200 E. California Blvd. PasadenaCA91125 United States Mathomics Center for Mathematical Modeling University of Chile Beauchef 851 Santiago Chile Novo Nordisk Foundation Center for Biosustainability University of California San Diego San DiegoCA92093 Uni

COnstraint-Based Reconstruction and Analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive software suite of interoperable COBRA methods. It has found widespread applications in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. Version 3.0 includes new methods for quality controlled reconstruction, modelling, topological analysis, strain and experimental design, network visualisation as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimisation solvers for multi-scale, multi-cellular and reaction kinetic modelling, respectively. This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios. This protocol is an update to the COBRA Toolbox 1.0 and 2.0. The COBRA Toolbox 3.0 provides an unparalleled depth of constraint-based reconstruction and analysis methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Data integration

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to "Dissecting the genetic heterogeneity of gastric cancer"

引用

EBioMedicine 2023年 94卷 104709页

作者： Timo Hess Carlo Maj Jan Gehlen Oleg Borisov Stephan L Haas Ines Gockel Michael Vieth Guillaume Piessen Hakan Alakus Yogesh Vashist Carina Pereira Michael Knapp Vitalia Schüller Alexander Quaas Heike I Grabsch Jessica Trautmann Ewa Malecka-Wojciesko Anna Mokrowiecka Jan Speller Andreas Mayr Julia Schröder Axel M Hillmer Dominik Heider Florian Lordick Ángeles Pérez-Aísa Rafael Campo Jesús Espinel Fernando Geijo Concha Thomson Luis Bujanda Federico Sopeña Ángel Lanas María Pellisé Claudia Pauligk Thorsten Oliver Goetze Carolin Zelck Julian Reingruber Emadeldin Hassanin Peter Elbe Sandra Alsabeah Mats Lindblad Magnus Nilsson Nicole Kreuser René Thieme Francesca Tavano Roberta Pastorino Dario Arzani Roberto Persiani Jin-On Jung Henrik Nienhüser Katja Ott Ralf R Schumann Oliver Kumpf Susen Burock Volker Arndt Anna Jakubowska Małgorzta Ławniczak Victor Moreno Vicente Martín Manolis Kogevinas Marina Pollán Justyna Dąbrowska Antonio Salas Olivier Cussenot Anne Boland-Auge Delphine Daian Jean-Francois Deleuze Erika Salvi Maris Teder-Laving Gianluca Tomasello Margherita Ratti Chiara Senti Valli De Re Agostino Steffan Arnulf H Hölscher Katharina Messerle Christiane Josephine Bruns Armands Sīviņš Inga Bogdanova Jurgita Skieceviciene Justina Arstikyte Markus Moehler Hauke Lang Peter P Grimminger Martin Kruschewski Nikolaos Vassos Claus Schildberg Philipp Lingohr Karsten Ridwelski Hans Lippert Nadine Fricker Peter Krawitz Per Hoffmann Markus M Nöthen Lothar Veits Jakob R Izbicki Adrianna Mostowska Federico Martinón-Torres Daniele Cusi Rolf Adolfsson Geraldine Cancel-Tassin Aksana Höblinger Ernst Rodermann Monika Ludwig Gisela Keller Andres Metspalu Hermann Brenner Joerg Heller Markus Neef Michael Schepke Franz Ludwig Dumoulin Lutz Hamann Renato Cannizzaro Michele Ghidini Dominik Plaßmann Michael Geppert Peter Malfertheiner Olivier Glehen Tomasz Skoczylas Marek Majewski Jan Lubiński Orazio Palmieri Stefania Boccia Anna Latiano Nuria Aragones Thomas Schmidt Mário Dinis-Ribeiro Rui Medeiros Salah-Eddin Al-Batran Mārcis Leja Juozas Institute of Human Genetics University of Marburg Marburg Germany Institute of Human Genetics University of Bonn School of Medicine & University Hospital Bonn Bonn Germany. Institute for Genomic Statistics and Bioinformatics Medical Faculty University of Bonn Bonn Germany. Institute of Human Genetics University of Marburg Marburg Germany. Department of Upper GI Diseases Karolinska University Hospital and Unit of Gastroenterology and Rheumatology Karolinska Institutet Stockholm Sweden. Department of Visceral Transplant Thoracic and Vascular Surgery University Hospital Leipzig Leipzig Germany. Institute for Pathology Friedrich-Alexander-University Erlangen-Nuernberg Klinikum Bayreuth Bayreuth Germany. Department of Digestive and Oncological Surgery Claude Huriez Hospital CHU Lille Lille France. Department of General Visceral Cancer and Transplant Surgery University of Cologne Cologne Germany. Department of General Visceral and Thoracic Surgery University Medical Center Hamburg-Eppendorf University of Hamburg Hamburg Germany Centre for Surgical Oncology Medias Klinikum Burghausen Burghausen Germany. Molecular Oncology and Viral Pathology Group IPO Porto Research Center (CI-IPOP) Portuguese Institute of Oncology of Porto (IPO Porto) 4200-072 Porto Portugal Porto Comprehensive Cancer Center & RISE @ CI-IPO University of Porto 4200-450 Porto Portugal. Institute of Medical Biometrics Informatics and Epidemiology (IMBIE) Medical Faculty University of Bonn Bonn Germany. Institute of Pathology University Hospital Cologne Medical Faculty University of Cologne Germany. Department of Pathology GROW School for Oncology and Developmental Biology Maastricht University Medical Center Maastricht the Netherlands Pathology and Data Analytics Leeds Institute of Medical Research at St James's University of Leeds Leeds United Kingdom. Department of Digestive Tract Diseases Medical University of Lodz Lodz Poland. Department of Mathematics and Computer Science Unive

来源：评论

学校读者我要写书评

暂无评论

Gene expression imputation across multiple brain regions provides insights into schizophrenia risk (vol 51, pg 659, 2019)

引用

NATURE GENETICS 2019年第6期51卷 1068-1068页

作者： Huckins, Laura M. Dobbyn, Amanda Ruderfer, Douglas M. Hoffman, Gabriel Wang, Weiqing Pardinas, Antonio F. Rajagopal, Veera M. Als, Thomas D. Nguyen, Hoang T. Girdhar, Kiran Boocock, James Roussos, Panos Fromer, Menachem Kramer, Robin Domenici, Enrico Gamazon, Eric R. Purcell, Shaun Demontis, Ditte Borglum, Anders D. Walters, James T. R. O'Donovan, Michael C. Sullivan, Patrick Owen, Michael J. Devlin, Bernie Sieberts, Solveig K. Cox, Nancy J. Im, Hae Kyung Sklar, Pamela Stahl, Eli A. Pamela Sklar Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai New York NY USA Vanderbilt University Medical Center Nashville TN USA MRC Centre for Neuropsychiatric Genetics and Genomics Cardiff University Cardiff UK Department of Biomedicine Aarhus University Aarhus Denmark The Lundbeck Foundation Initiative for Integrative Psychiatric Research iPSYCH Denmark Center for Integrative Sequencing Aarhus University Aarhus Denmark Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Human Brain Collection Core National Institute of Mental Health Bethesda MD USA Laboratory of Neurogenomic Biomarkers Centre for Integrative Biology (CIBIO) University of Trento Trento Italy Clare Hall University of Cambridge Cambridge UK University of North Carolina at Chapel Hill Chapel Hill NC USA Karolinska Institutet Stockholm Sweden Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Systems Biology Sage Bionetworks Seattle WA USA Section of Genetic Medicine Department of Medicine University of Chicago Chicago IL USA Integrated Technology Research Laboratories Pharmaceutical Research Division Takeda Pharmaceutical Company Limited Fujisawa Japan Neuropsychiatry Section Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Neuropsychiatric Signaling Program Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Psychiatry JJ Peters Virginia Medical Center Bronx NY USA Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York NY USA Friedman Brain Institute

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

关键词： Gene expression Genome-wide association studies Schizophrenia Transcriptomics

来源：评论

学校读者我要写书评

暂无评论

Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits (vol 50, pg 1412, 2018)

引用

NATURE GENETICS 2018年第12期50卷 1755-1755页

作者： Evangelou, Evangelos Warren, Helen R. Mosen-Ansorena, David Mifsud, Borbala Pazoki, Raha Gao, He Ntritsos, Georgios Dimou, Niki Cabrera, Claudia P. Karaman, Ibrahim Fu Liang Ng Evangelou, Marina Witkowska, Katarzyna Tzanis, Evan Hellwege, Jacklyn N. Giri, Ayush Edwards, Digna R. Velez Sun, Yan V. Cho, Kelly Gaziano, J. Michael Wilson, Peter W. F. Tsao, Philip S. Kovesdy, Csaba P. Esko, Tonu Magi, Reedik Milani, Lili Almgren, Peter Boutin, Thibaud Debette, Stephanie Ding, Jun Giulianini, Franco Holliday, Elizabeth G. Jackson, Anne U. Li-Gao, Ruifang Lin, Wei-Yu Luan, Jian'an Mangino, Massimo Oldmeadow, Christopher Prins, Bram Peter Qian, Yong Sargurupremraj, Muralidharan Shah, Nabi Surendran, Praveen Theriault, Sebastien Verweij, Niek Willems, Sara M. Zhao, Jing-Hua Amouyel, Philippe Connell, John de Mutsert, Renee Doney, Alex S. F. Farrall, Martin Menni, Cristina Morris, Andrew D. Noordam, Raymond Pare, Guillaume Poulter, Neil R. Shields, Denis C. Stanton, Alice Thom, Simon Abecasis, Goncalo Amin, Najaf Arking, Dan E. Ayers, Kristin L. Barbieri, Caterina M. Batini, Chiara Bis, Joshua C. Blake, Tineka Bochud, Murielle Boehnke, Michael Boerwinkle, Eric Boomsma, Dorret I. Bottinger, Erwin P. Braund, Peter S. Brumat, Marco Campbell, Archie Campbell, Harry Chakravarti, Aravinda Chambers, John C. Chauhan, Ganesh Ciullo, Marina Cocca, Massimiliano Collins, Francis Cordell, Heather J. Davies, Gail de Borst, Martin H. de Geus, Eco J. Deary, Ian J. Deelen, Joris Del Greco, Fabiola M. Demirkale, Cumhur Yusuf Dorr, Marcus Ehret, Georg B. Elosua, Roberto Enroth, Stefan Erzurumluoglu, A. Mesut Ferreira, Teresa Franberg, Mattias Franco, Oscar H. Gandin, Ilaria Gasparini, Paolo Giedraitis, Vilmantas Gieger, Christian Girotto, Giorgia Goel, Anuj Gow, Alan J. Gudnason, Vilmundur Guo, Xiuqing Gyllensten, Ulf Hamsten, Anders Harris, Tamara B. Harris, Sarah E. Hartman, Catharina A. Havulinna, Aki S. Hicks, Andrew A. Hofer, Edith Hofman, Albert Hottenga, Jouke-Jan Huffman, Jennifer E. Hwang, Shih-Jen Ingelsson, Erik James, Alan Jansen Department of Epidemiology and Biostatistics Imperial College London London UK Department of Hygiene and Epidemiology University of Ioannina Medical School Ioannina Greece William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK National Institute for Health Research Barts Cardiovascular Biomedical Research Center Queen Mary University of London London UK MRC-PHE Centre for Environment and Health Imperial College London London UK Department of Mathematics Imperial College London London UK Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Medical Center Tennessee Valley Healthcare System (626)/Vanderbilt University Nashville TN USA Vanderbilt Genetics Institute Vanderbilt Epidemiology Center Department of Obstetrics and Gynecology Vanderbilt University Medical Center Tennessee Valley Health Systems VA Nashville TN USA Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA Department of Biomedical Informatics Emory University School of Medicine Atlanta GA USA Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA Division of Aging Department of Medicine Brigham and Women’s Hospital Department of Medicine Harvard Medical School Boston MA USA Atlanta VAMC and Emory Clinical Cardiovascular Research Institute Atlanta GA USA VA Palo Alto Health Care System Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA Nephrology Section Memphis VA Medical Center and University of Tennessee Health Science Center Memphis TN USA Estonian Genome Center University of Tartu Tartu Estonia Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Department Clinical Sciences Malmö Lund University Malmö Sweden MRC Human Genetics Un

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

关键词： Cardiovascular diseases Genome informatics Genome-wide association studies

来源：评论

学校读者我要写书评

暂无评论

Biomarker development for pancreatic ductal adenocarcinoma using integrated analysis of mRNA and miRNA expression

Biomarker development for pancreatic ductal adenocarcinoma u...

引用

IEEE International Conference on bioinformatics and Biomedicine (BIBM)

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 Healthcare group SK telecom Bioinformatics Tech. Lab. Seoul Korea Department of Surgery Seoul National University Hospital Seoul Korea

ISBN: (纸本)9781479956708

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, which has dismal prognosis because of its silent early symptoms, high metastatic potential, and resistance to conventional therapies. Although a PDAC patient who is diagnosed at an early stage would have a substantial increase in chance of survival, the survival rate is poor because there is no efficient non-invasive diagnostic test in the early stage. In this study, we developed an efficient prediction models to detect PDAC in its early stages. Our prediction models use both mRNA and miRNA expression data from 104 PDAC tissues and 17 normal pancreatic tissues using microarray technology. After quality control, we built prediction models based on support vector machine (SVM) from mRNA and miRNA expressions for detecting early PDAC. To prevent over-fitting effect, we conducted leave-one-out cross validation (LOOCV) and 5-fold cross validation (CV). For independent validation of prediction models, we performed evaluation on independent datasets from Gene Expression Omnibus (GEO). After the validation, we identified 28 single markers and 231 combinations of markers with powerful prediction performance. In addition, the marker candidates are annotated with cancer pathways using gene ontology analysis. Our prediction models for PDAC may have potential for early diagnosis of PDAC.

关键词： Cancer Barium Blood Lungs Predictive models Ontologies Reliability

来源：评论

学校读者我要写书评

暂无评论

An integrative approach to investigate the respective roles of single-nucleotide variants and copy-number variants in Attention-Deficit/Hyperactivity Disorder (vol 6, 22851, 2016)

引用

SCIENTIFIC REPORTS 2016年第1期6卷 22851-22851页

作者： Lima, Leandro de Araujo Feio-dos-Santos, Ana Cecilia Belangero, Sintia Iole Gadelha, Ary Bressan, Rodrigo Affonseca Salum, Giovanni Abrahao Pan, Pedro Mario Moriyama, Tais Silveira Graeff-Martins, Ana Soledade Tamanaha, Ana Carina Alvarenga, Pedro Krieger, Fernanda Valle Fleitlich-Bilyk, Bacy Jackowski, Andrea Parolin Brietzke, Elisa Sato, Joao Ricardo Polanczyk, Guilherme Vanoni Mari, Jair de Jesus Manfro, Gisele Gus do Rosario, Maria Conceiao Miguel, Euripedes Constantino Puga, Renato David Tahira, Ana Carolina Souza, Viviane Neri Chile, Thais Gouveia, Gisele Rodrigues Simoes, Sergio Nery Chang, Xiao Pellegrino, Renata Tian, Lifeng Glessner, Joseph T. Hashimoto, Ronaldo Fumio Rohde, Luis Augusto Sleiman, Patrick M. A. Hakonarson, Hakon Brentani, Helena Inter-institutional Grad Program on Bioinformatics University of São Paulo São Paulo SP Brazil Center for Applied Genomics The Children’s Hospital of Philadelphia Philadelphia PA USA Department & Institute of Psychiatry University of São Paulo Medical School São Paulo SP Brazil National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq) São Paulo SP Brazil Department of Psychiatry Federal University of São Paulo São Paulo SP Brazil National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq) São Paulo SP Brazil Department of Psychiatry Federal University of São Paulo São Paulo SP Brazil National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq) São Paulo SP Brazil Department of Psychiatry Federal University of São Paulo São Paulo SP Brazil National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq) São Paulo SP Brazil Department of Psychiatry Hospital de Clínicas de Porto Alegre Federal University of Rio Grande do Sul Porto Alegre RS Brazil National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq) São Paulo SP Brazil Department of Psychiatry Federal University of São Paulo São Paulo SP Brazil Department & Institute of Psychiatry University of São Paulo Medical School São Paulo SP Brazil National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq) São Paulo SP Brazil Department of Psychiatry Federal University of São Paulo São Paulo SP Brazil Department & Institute of Psychiatry University of São Paulo Medical School São Paulo SP Brazil National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq) São Paulo SP Brazil National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq) São Paulo SP Brazil Department of Psychiatry Federal University of São Paulo São Paulo SP Brazil Department & Institute of Psychiatry University of S

Many studies have attempted to investigate the genetic susceptibility of Attention-Deficit/Hyperactivity Disorder (ADHD), but without much success. The present study aimed to analyze both single-nucleotide and copy-number variants contributing to the genetic architecture of ADHD. We generated exome data from 30 Brazilian trios with sporadic ADHD. We also analyzed a Brazilian sample of 503 children/adolescent controls from a High Risk Cohort Study for the Development of Childhood Psychiatric Disorders, and also previously published results of five CNV studies and one GWAS meta-analysis of ADHD involving children/adolescents. The results from the Brazilian trios showed that cases with de novo SNVs tend not to have de novo CNVs and vice-versa. Although the sample size is small, we could also see that various comorbidities are more frequent in cases with only inherited variants. Moreover, using only genes expressed in brain, we constructed two “in silico” protein-protein interaction networks, one with genes from any analysis, and other with genes with hits in two analyses. Topological and functional analyses of genes in this network uncovered genes related to synapse, cell adhesion, glutamatergic and serotoninergic pathways, both confirming findings of previous studies and capturing new genes and genetic variants in these pathways.

关键词：

来源：评论

学校读者我要写书评

暂无评论

11th German Conference on Chemoinformatics (GCC 2015) Abstracts

引用

JOURNAL OF CHEMINFORMATICS 2016年第SUPPL 1期8卷 1-27页

作者： [Anonymous] GDCh-CIC Division Associated Board Member Beilstein-Institut zur Förderung der Chemischen Wissenschaften Trakehner Str. 7-9 60487 Frankfurt Germany. ufechner@beilstein-institut.de. Division Medicinal Chemistry Amsterdam Institute for Molecules Medicines and Systems (AIMMS) VU University Amsterdam The Netherlands. Centre for Bioinformatics Uni Hamburg Bundesstr. 43 20146 Hamburg Germany. Sanofi-Aventis Deutschland GmbH 65926 Frankfurt am Main Germany. stefan.guessregen@***. Sanofi-Aventis Deutschland GmbH 65926 Frankfurt am Main Germany. GlaxoSmithKline Stevenage SG1 2NY UK. nicola.j.richmond@***. Discngine Paris 75011 France. Organisch-Chemisches Institut Westfälische Wilhelms-Universität Münster Germany. marwin.segler@wwu.de. Organisch-Chemisches Institut Westfälische Wilhelms-Universität Münster Germany. Bundeskriminalamt Wiesbaden Central Analytics II 65173 Wiesbaden Germany. Department of Chemistry and Biochemistry University of California Santa Barbara CA 93111 USA. shea@chem.ucsb.edu. Department of Chemistry and Biochemistry University of California Santa Barbara CA 93111 USA. Chemistry Department Ludwig-Maximilians-Universität München Butenandtstr. 7 81377 Munich Germany. Inorganic Chemistry and Center for Nanointegration University of Duisburg-Essen Essen Germany. CAM-D Technologies Essen Germany. Institute for Bioinformatics and Chemoinformatics Westphalian University of Applied Sciences Recklinghausen Germany. Institute for Bioinformatics and Chemoinformatics Westphalian University of Applied Sciences Recklinghausen Germany. achim.zielesny@w-hs.de. Leiden University Leiden Netherlands. j.fraaije@chem.leidenuniv.nl. Culgi BV Berlin Germany. Physikalische Chemie III TU Dortmund 44227 Dortmund Germany. stefan.kast@tu-dortmund.de. Centre for Molecular Informatics Department of Chemistry University of Cambridge Lensfield Road Cambridge CB2 1EW United Kingdom. kcb27@cam.ac.uk. Centre for Molecular Informatics Department of Chemistry

来源：评论

学校读者我要写书评

暂无评论

ENERGY-BASED ARCHITECTURE FOR CLASSIFICATION OF PUBLICATION FIGURES

ENERGY-BASED ARCHITECTURE FOR CLASSIFICATION OF PUBLICATION ...

引用

Annual ORNL Biomedical Sciences and Engineering Conference on Collaborative Biomedical Innovations

作者： P. E. Barbano M. L. Nagy M. Krauthammer Department of Mathematics Yale University Program for Computational Biology and Bioinformatics Yale University

ISBN: (纸本)9781479921195

We present an implementation of the experimental and theoretical results obtained in the analysis of text and image content of biomedical publications. Particularly, we propose a novel optical recognition system using an adaptive algorithm for the classification and analysis of highly heterogeneous images in research papers. When compared with conventional algorithms, our technology substantially increases the probability of detection and classification of images buried in text or obscured by other images. We report successful testing of the new architecture using PubMed publications.

关键词： publications images ARCHITECTURE Classification of algorithms new buildings Taxonomy texts optical identification WWOX gene Image classification

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：