Phenobridge WP 7. Crossing the species bridge between mouse and human. 17 February 2015, Helmholtz Zentrum München
|
|
- Pauline Walters
- 5 years ago
- Views:
Transcription
1 Phenobridge WP 7 Crossing the species bridge between mouse and human 17 February 2015, Helmholtz Zentrum München Michael Raess on behalf of the WP7 collaborators
2 Who is WP7? Helen Parkinson, Nathalie Conte, Terry Meehan, Gautier Koscielny - European Bioinformatics Insitute Michael Raess - INFRAFRONTIER GmbH Frauke Neff, Philipp Gormanns, Christoph Lengger, Manuala Östereicher, Elida Schneltzer, Christine Schütt, Martin Hrabě de Angelis - Helmholtz Zentrum München Heimo Müller, Natalie Bordag, Kurt Zatloukal Medical University Graz Lucia Banci, Claudia Andreini, Leonardo Tenori, Claudio Luchinat CIRMMP Harald Staiger, Andreas Fritsche - Diabetes Experts, Medical University of Tübingen
3 Work package goal WP7-Phenobridge aims to bridge the gap between the phenotype information available from mouse model studies and from human clinical data in the field of obesity and diabetes, enabling a new level of interspecies analysis of disease datasets Linking BBMRI, ELXIR, INFRAFRONTIER, INSTRUCT
4 What are the Phenobridge objectives? Identify and develop a set of annotations, necessary terminologies, and mappings between terminologies for human and mouse models of diabetes and obesity Identify and group related interacting parameters in human and mouse which are involved in the development of clinical and molecular phenotypes Formalise rules for phenotypic annotation in human and mouse to work towards automation of phenotypic discovery and develop a related prototype service
5 How are we meeting our objectives? Identify and develop a set of annotations, necessary terminologies, and mappings Manual review of DO, HPO, OMIM, EFO by diabetes and ontology experts Development of a merged and extended component of DO, HP and Orphanet to incorporate diabetes phenotypes, frequency of symptoms and disease progression Identification of type 2 diabetes phenotype associations using data mining of diabetes journal abstracts, leading to a list of >570 potentially relevant MP and HP terms
6 How are we meeting our objectives? Identify and group related interacting parameters Two reviews of the terms obtained by the text mining approach, involving diabetes experts at the Helmholtz Zentrum München and the University of Tübingen Exclusion of clinically irrelevant terms Organisation of relevant terms into disease stage categories (IFG/IGT=Prediabetes, Manifest Diabetes, Symptoms, Consequences/ Complications, Type 1 / Type 2 diabetes, Associations with other diseases)
7 Expert curated list of HP and MP terms relevant to diabetes
8 HP MP New Total terms terms terms terms Diabetes Cause IFG/IGT (Prediabetes) Manifest Diabetes Diabetes Symptom Consequences/ Complications Type 1 Diabetes Type 2 Diabetes Assoc. w/ other diseases too Total (any temporal stage)
9 How are we meeting our objectives? Identify and group related interacting parameters Transforming the classification into the DIAB ontology using OWL Co-annotation of mouse and human datasets with the DIAB ontology BMB partners sample datasets Metabolights dataset Biosamples dataset
10
11 How are we meeting our objectives? Identify and group related interacting parameters Transforming the classification into the DIAB ontology using OWL Co-annotation of mouse and human datasets with the DIAB ontology BMB partners sample datasets Metabolights dataset Biosamples dataset
12 Partner sample datasets maping to DIAB ontology results Ontology ID Term Pre-Diabetic Manifest Diabetes Consequences Complications Associate with other disease (and diabetes) Diabetes Cause Diabetes Symptom Type 1 Diabetes Type 2 Diabetes WP7 Data HP_ cirrhosis x x x x x Graz Mouse MP_ carcinoma x x x x x x Graz Mouse HP_ cholestasis x x x x Graz Mouse HP_ hepatic steatosis x x x x x Graz Mouse HP_ cirrhosis x x x x x Graz Human MP_ carcinoma x x x x x x Graz Human HP_ cholestasis x x x x Graz Human HP_ hepatic steatosis x x x x x Graz Human HP_ hepatitis x x x x x Graz Human MP_ obese x x x x x x Florence Human HP_ obesity x x x x x x Florence Human HP_ hypertension x x x x x x x Florence Human MP_ diabetes x x x x x x x Florence Human
13 Biosample data mapping to DIAB ontology statistical summary Type of Mapping Human Mouse % of each Average (true or false) sample % per type sample % per type type mapping number per type Type 2 True Type 1 True Diabetes True False pos False
14 How are we meeting our objectives? Work towards automation of phenotypic discovery (D7.3) Tool development: M3: Mining mouse models Tool development: Bridging genomes systematic mapping between syntnic regions in mouse and humans Test DIAB ontology with PhenoDigm
15 M3 Mining Mouse Models Intuitive access to mouse phenotyping data Various entry points (e.g diseases, genes, interactions) Integration with EBI RDF resources (together with WP4)
16 Bridging Genomes systematic mapping between syntenic regions Synteny: two or more genomic regions are derived from a single ancestral region Mapping between syntenic regions in mouse and humans allows discovery of funtional conservation and helps prioritise candidate genes / regions Provides integrated genetic and phenotypic data from different resources (Ensembl, GWAS catalogue, MGI, IMPC) Retrieves syntenic regions, associated overlapping or nearest genes, regulatory elements, phenotypic annotations
17 Bridging Genomes systematic mapping between syntenic regions
18
19 Impact Comprehensive diabetes specific DIAB ontology for capturing mouse and human phenotypes Mapping different datasets to DIAB shows that relevant datasets are being captured Publication on DIAB development process is in preparation Tool development to access and integrate genotypic and phenotypic data in mouse and human
20 Sustainability DIAB Ontology RI to carry the tool/service/resource forward: INFRAFRONTIER / ELIXIR-EBI DIAB ontology will be integrated in the INFRAFRONTIER and IMPC resource databases INFRAFRONTIER / ELIXIR-EBI will continue DIAB development M3 tool will be maintained by INFRAFRONTIER Bridging genomes tool will be maintained by ELIXIR-EBI
21 Lessons learned Small focused workshops with domain experts work well to receive concise feedback on existing ontologies and their coverage of particular disease areas The methodology applied for building the DIAB ontology (data mining > expert curation of terms > ontology building) can be transferred to other disease areas
SNOMED CT and Orphanet working together
SNOMED CT and Orphanet working together Ian Green Business Services Executive, IHTSDO Dr. Romina Armando INSERM Session outline What is Orphanet? Rare disorders Orphanet nomenclature Mappings to other
More informationHow to code rare diseases with international terminologies?
How to code rare diseases with international terminologies? Ana Rath Inserm US14, Paris, France ana.rath@inserm.fr Special thanks to Prof Paul Landais for his kind presentation. Needs for terminologies
More information38 Int'l Conf. Bioinformatics and Computational Biology BIOCOMP'16
38 Int'l Conf. Bioinformatics and Computational Biology BIOCOMP'16 PGAR: ASD Candidate Gene Prioritization System Using Expression Patterns Steven Cogill and Liangjiang Wang Department of Genetics and
More informationData mining with Ensembl Biomart. Stéphanie Le Gras
Data mining with Ensembl Biomart Stéphanie Le Gras (slegras@igbmc.fr) Guidelines Genome data Genome browsers Getting access to genomic data: Ensembl/BioMart 2 Genome Sequencing Example: Human genome 2000:
More informationLiterature databases OMIM
Literature databases OMIM Online Mendelian Inheritance in Man OMIM OMIM is a database that catalogues all the known diseases with a genetic component, and when possible links them to the relevant genes
More informationBuilding a Diseases Symptoms Ontology for Medical Diagnosis: An Integrative Approach
Building a Diseases Symptoms Ontology for Medical Diagnosis: An Integrative Approach Osama Mohammed, Rachid Benlamri and Simon Fong* Department of Software Engineering, Lakehead University, Ontario, Canada
More informationPredicting disease associations via biological network analysis
Sun et al. BMC Bioinformatics 2014, 15:304 RESEARCH ARTICLE Open Access Predicting disease associations via biological network analysis Kai Sun 1, Joana P Gonçalves 1, Chris Larminie 2 and Nataša Pržulj
More informationFacts from text: Automated gene annotation with ontologies and text-mining
1. Workshop des GI-Arbeitskreises Ontologien in Biomedizin und Lebenswissenschaften (OBML) Facts from text: Automated gene annotation with ontologies and text-mining Conrad Plake Schroeder Group (Bioinformatics),
More informationSupplementary Figure 1
Supplementary Figure 1 An example of the gene-term-disease network automatically generated by Phenolyzer web server for 'autism'. The largest word represents the user s input term, Autism. The pink round
More informationKnowledge networks of biological and medical data An exhaustive and flexible solution to model life sciences domains
Knowledge networks of biological and medical data An exhaustive and flexible solution to model life sciences domains Dr. Sascha Losko, Dr. Karsten Wenger, Dr. Wenzel Kalus, Dr. Andrea Ramge, Dr. Jens Wiehler,
More informationThe Focused Exome service at Bristol Genetics Laboratory
The Focused Exome service at Bristol Genetics Laboratory Chris Buxton Maggie Williams July 2016 Bristol Clinical exome Service to mid July Validation: Agilent FE kit, NextSeq 500 and new pipeline 1st reports
More informationUsing the NIH Collaboratory's and PCORnet's distributed data networks for clinical trials and observational research - A preview
Using the NIH Collaboratory's and PCORnet's distributed data networks for clinical trials and observational research - A preview Millions of people. Strong collaborations. Privacy first. Jeffrey Brown,
More informationNational Academies Next Generation SAMPLE Researchers TITLE Initiative HERE
National Academies Next Generation SAMPLE Researchers TITLE Initiative HERE Dennis A. Dean, II, PhD Sanofi Auditorium July 13, 2017 sevenbridges.com A little about me Research Experience Analytics and
More informationA framework for the study of diseases and adverse drug reactions
A framework for the study of diseases and adverse drug reactions Laura I. Furlong IBI group Research Programme on Biomedical Informatics (GRIB) Hospital del Mar Research Institute (IMIM) Information on
More informationEvaluating Classifiers for Disease Gene Discovery
Evaluating Classifiers for Disease Gene Discovery Kino Coursey Lon Turnbull khc0021@unt.edu lt0013@unt.edu Abstract Identification of genes involved in human hereditary disease is an important bioinfomatics
More informationSchema-Driven Relationship Extraction from Unstructured Text
Wright State University CORE Scholar Kno.e.sis Publications The Ohio Center of Excellence in Knowledge- Enabled Computing (Kno.e.sis) 2007 Schema-Driven Relationship Extraction from Unstructured Text Cartic
More informationPhenotype linkages to NMD common data elements
TREAT-NMD Global Database Oversight Committee and Curators Meeting 19 th -20th September 2016 - Leuven, Belgium Phenotype linkages to NMD common data elements Prof. C. Béroud & Dr. D. Salgado "Genetics
More informationImproving genomic diagnoses through accurate, specific phenotype information
Improving genomic diagnoses through accurate, specific phenotype information Lisa Ewans Clinical Geneticist, RPAH, Sydney PhD student in genomics, KCCG, Garvan; UNSW https://vlab.org Overview Phenotype
More informationOMIM The Online Mendelian Inheritance in Man Knowledgebase: A Wardrobe Full of Genes. Ada Hamosh, MD, MPH
OMIM The Online Mendelian Inheritance in Man Knowledgebase: A Wardrobe Full of Genes Ada Hamosh, MD, MPH OMIM THE ONLINE MENDELIAN INHERITANCE IN MAN KNOWLEDGEBASE: A WARDROBE FULL OF GENES The OMIM knowledgebase
More informationA Simple Pipeline Application for Identifying and Negating SNOMED CT in Free Text
A Simple Pipeline Application for Identifying and Negating SNOMED CT in Free Text Anthony Nguyen 1, Michael Lawley 1, David Hansen 1, Shoni Colquist 2 1 The Australian e-health Research Centre, CSIRO ICT
More informationBig Data Training for Translational Omics Research. Session 1, Day 3, Liu. Case Study #2. PLOS Genetics DOI: /journal.pgen.
Session 1, Day 3, Liu Case Study #2 PLOS Genetics DOI:10.1371/journal.pgen.1005910 Enantiomer Mirror image Methadone Methadone Kreek, 1973, 1976 Methadone Maintenance Therapy Long-term use of Methadone
More informationAccessing and Using ENCODE Data Dr. Peggy J. Farnham
1 William M Keck Professor of Biochemistry Keck School of Medicine University of Southern California How many human genes are encoded in our 3x10 9 bp? C. elegans (worm) 959 cells and 1x10 8 bp 20,000
More informationSVIM: Structural variant identification with long reads DAVID HELLER MAX PLANCK INSTITUTE FOR MOLECULAR GENETICS, BERLIN JUNE 2O18, SMRT LEIDEN
SVIM: Structural variant identification with long reads DAVID HELLER MAX PLANCK INSTITUTE FOR MOLECULAR GENETICS, BERLIN JUNE 2O18, SMRT LEIDEN Structural variation (SV) Variants larger than 50bps Affect
More informationELIMINATION OF VIRAL HEPATITIS IN ROMANIA LESSONS LEARNT AND THE WAY FORWARD
ELIMINATION OF VIRAL HEPATITIS IN ROMANIA LESSONS LEARNT AND THE WAY FORWARD 17 May 2018 Bucharest, Romania Organisers Associations Collaborating on Hepatitis to Immunize and Eliminate the Viruses in Europe
More informationThe Deciphering Development Disorders (DDD) project: What a genomic approach can achieve
The Deciphering Development Disorders (DDD) project: What a genomic approach can achieve RCP ADVANCED MEDICINE, LONDON FEB 5 TH 2018 HELEN FIRTH DM FRCP DCH, SANGER INSTITUTE 3,000,000,000 bases in each
More informationDISCOVERING IMPLICIT ASSOCIATIONS BETWEEN GENES AND HEREDITARY DISEASES
DISCOVERING IMPLICIT ASSOCIATIONS BETWEEN GENES AND HEREDITARY DISEASES KAZUHIRO SEKI Graduate School of Science and Technology, Kobe University 1-1 Rokkodai, Nada, Kobe 657-8501, Japan E-mail: seki@cs.kobe-u.ac.jp
More informationPhenotype analysis in humans using OMIM
Outline: 1) Introduction to OMIM 2) Phenotype similarity map 3) Exercises Phenotype analysis in humans using OMIM Rosario M. Piro Molecular Biotechnology Center University of Torino, Italy 1 MBC, Torino
More informationRare Diseases Nomenclature and classification
Rare Diseases Nomenclature and classification Annie Olry ORPHANET - Inserm US14, Paris, France annie.olry@inserm.fr Using standards and embedding good practices to promote interoperable data sharing in
More informationClinical terms and ICD 11
Clinical terms and ICD 11 Family of Classifications Robert Jakob, WHO Detail of health information -> Classification Reality (individual detail) Free Text (medical information) Terminologies / ICD-11 URI
More informationSemantic Alignment between ICD-11 and SNOMED-CT. By Marcie Wright RHIA, CHDA, CCS
Semantic Alignment between ICD-11 and SNOMED-CT By Marcie Wright RHIA, CHDA, CCS World Health Organization (WHO) owns and publishes the International Classification of Diseases (ICD) WHO was entrusted
More informationBjoern Peters La Jolla Institute for Allergy and Immunology Buenos Aires, Oct 31, 2012
www.iedb.org Bjoern Peters bpeters@liai.org La Jolla Institute for Allergy and Immunology Buenos Aires, Oct 31, 2012 Overview 1. Introduction to the IEDB 2. Application: 2009 Swine-origin influenza virus
More informationAnnotating Temporal Relations to Determine the Onset of Psychosis Symptoms
Annotating Temporal Relations to Determine the Onset of Psychosis Symptoms Natalia Viani, PhD IoPPN, King s College London Introduction: clinical use-case For patients with schizophrenia, longer durations
More informationIntroduction to the Partners Biobank Portal. December 2016
Introduction to the Partners Biobank Portal December 2016 Agenda About the Partners Biobank About the Biobank Portal Data Types in the Biobank Sample types DNA. Plasma and Serum Consent Genomic Data Health
More informationText mining for lung cancer cases over large patient admission data. David Martinez, Lawrence Cavedon, Zaf Alam, Christopher Bain, Karin Verspoor
Text mining for lung cancer cases over large patient admission data David Martinez, Lawrence Cavedon, Zaf Alam, Christopher Bain, Karin Verspoor Opportunities for Biomedical Informatics Increasing roll-out
More informationHands-On Ten The BRCA1 Gene and Protein
Hands-On Ten The BRCA1 Gene and Protein Objective: To review transcription, translation, reading frames, mutations, and reading files from GenBank, and to review some of the bioinformatics tools, such
More informationI. Setup. - Note that: autohgpec_v1.0 can work on Windows, Ubuntu and Mac OS.
autohgpec: Automated prediction of novel disease-gene and diseasedisease associations and evidence collection based on a random walk on heterogeneous network Duc-Hau Le 1,*, Trang T.H. Tran 1 1 School
More informationPrinciples of phylogenetic analysis
Principles of phylogenetic analysis Arne Holst-Jensen, NVI, Norway. Fusarium course, Ås, Norway, June 22 nd 2008 Distance based methods Compare C OTUs and characters X A + D = Pairwise: A and B; X characters
More informationPersonalis ACE Clinical Exome The First Test to Combine an Enhanced Clinical Exome with Genome- Scale Structural Variant Detection
Personalis ACE Clinical Exome The First Test to Combine an Enhanced Clinical Exome with Genome- Scale Structural Variant Detection Personalis, Inc. 1350 Willow Road, Suite 202, Menlo Park, California 94025
More informationThe PhenX Toolkit: Standard Measures for Collaborative Research
The PhenX Toolkit: Standard Measures for Collaborative Research Clinical Common Data Elements Task Force (CDETF) November 4, 2016 Carol M. Hamilton, PhD RTI International is a trade name of Research Triangle
More informationStandardize and Optimize. Trials and Drug Development
Informatics Infrastructure to Standardize and Optimize Quantitative Imaging in Clinical Trials and Drug Development Daniel L. Rubin, MD, MS Assistant Professor of Radiology Member, Stanford Cancer Center
More informationSingle SNP/Gene Analysis. Typical Results of GWAS Analysis (Single SNP Approach) Typical Results of GWAS Analysis (Single SNP Approach)
High-Throughput Sequencing Course Gene-Set Analysis Biostatistics and Bioinformatics Summer 28 Section Introduction What is Gene Set Analysis? Many names for gene set analysis: Pathway analysis Gene set
More informationSemi-Automatic Construction of Thyroid Cancer Intervention Corpus from Biomedical Abstracts
jsci2016 Semi-Automatic Construction of Thyroid Cancer Intervention Corpus from Biomedical Wutthipong Kongburan, Praisan Padungweang, Worarat Krathu, Jonathan H. Chan School of Information Technology King
More informationHHS Public Access Author manuscript Hum Mutat. Author manuscript; available in PMC 2016 April 16.
GeneMatcher: A Matching Tool for Connecting Investigators with an Interest in the Same Gene Nara Sobreira 1,*, François Schiettecatte 2, David Valle 1, and Ada Hamosh 1 1 Institute of Genetic Medicine,
More informationData Mining in Bioinformatics Day 4: Text Mining
Data Mining in Bioinformatics Day 4: Text Mining Karsten Borgwardt February 25 to March 10 Bioinformatics Group MPIs Tübingen Karsten Borgwardt: Data Mining in Bioinformatics, Page 1 What is text mining?
More informationDecision Support in Radiation Therapy. Summary. Clinical Decision Support 8/2/2012. Overview of Clinical Decision Support
Decision Support in Radiation Therapy August 1, 2012 Yaorong Ge Wake Forest University Health Sciences Summary Overview of Clinical Decision Support Decision support for radiation therapy Introduction
More informationSupplementary Materials to
Supplementary Materials to The Emerging Landscape of Epidemiological Research Based on Biobanks Linked to Electronic Health Records: Existing Resources, Analytic Challenges and Potential Opportunities
More informationAutism Pathways Analysis: A Functional Framework and Clues for Further Investigation. Martha Herbert PhD MD Ya Wen PhD July 2016
Autism Pathways Analysis: A Functional Framework and Clues for Further Investigation Martha Herbert PhD MD Ya Wen PhD July 2016 1 Report on pathway network analyses in autism, based on open-access paper
More informationSPICE: Semantic Propositional Image Caption Evaluation
SPICE: Semantic Propositional Image Caption Evaluation Presented to the COCO Consortium, Sept 2016 Peter Anderson1, Basura Fernando1, Mark Johnson2 and Stephen Gould1 1 Australian National University 2
More informationFollowing virus recombination and evolution
Following virus recombination and evolution Coping with the avalanche of sequencing data Dmitry Kusnetsov, Anne Gleizes, Robin Liechti, Ioannis Xenarios, Philippe Le Mercier Vital-IT/Swiss-Prot group SIB
More informationAutomated Annotation of Biomedical Text
Automated Annotation of Biomedical Text Kevin Livingston, Ph.D. Postdoctoral Fellow Pharmacology Department, School of Medicine University of Colorado Anschutz Medical Campus Kevin.Livingston@ucdenver.edu
More informationCHR POS REF OBS ALLELE BUILD CLINICAL_SIGNIFICANCE
CHR POS REF OBS ALLELE BUILD CLINICAL_SIGNIFICANCE is_clinical dbsnp MITO GENE chr1 13273 G C heterozygous - - -. - DDX11L1 chr1 949654 A G Homozygous 52 - - rs8997 - ISG15 chr1 1021346 A G heterozygous
More informationCollaborative Project of the 7th Framework Programme. WP6: Tools for bio-researchers and clinicians
G.A. nº 270086 Collaborative Project of the 7th Framework Programme WP6: Tools for bio-researchers and clinicians Deliverable 6.1: Design of Inference Engine v.1.0 31/10/2011 www.synergy-copd.eu Document
More informationIMI2 T1DM Call Topic Text: Translational approaches to disease modifying therapy of T1DM
IMI2 T1DM Call Topic Text: Translational approaches to disease modifying therapy of T1DM Dr Anke M Schulte, Head of Islet Biology Department Sanofi-Diabetes Frankfurt, Germany 11th of July, 2014 General
More informationHow can Natural Language Processing help MedDRA coding? April Andrew Winter Ph.D., Senior Life Science Specialist, Linguamatics
How can Natural Language Processing help MedDRA coding? April 16 2018 Andrew Winter Ph.D., Senior Life Science Specialist, Linguamatics Summary About NLP and NLP in life sciences Uses of NLP with MedDRA
More informationLong-term Care in Motion
Long-term Care in Motion Katrin Claßen 1, Hans-Werner Wahl 1, Carl-Philipp Jansen 1,2 & Klaus Hauer 1,2 1 Department of Psychological Ageing Research, Heidelberg University 2 Agaplesion Bethanien Hospital,
More informationDisclaimer. The following report contains a description of the request, request specifications, and results from the modular program run(s).
Disclaimer The following report(s) provides findings from an FDA initiated query using its Mini Sentinel pilot. While Mini Sentinel queries may be undertaken to assess potential medical product safety
More informationComputational Thinking in Genome and Proteome Analysis: A Logician s Adventures in Computational Biology. Wong Limsoon
Computational Thinking in Genome and Proteome Analysis: A Logician s Adventures in Computational Biology Wong Limsoon 2 what computational thinking is 3 An example of computational thinking Suppose 20%
More informationVariant Detection & Interpretation in a diagnostic context. Christian Gilissen
Variant Detection & Interpretation in a diagnostic context Christian Gilissen c.gilissen@gen.umcn.nl 28-05-2013 So far Sequencing Johan den Dunnen Marja Jakobs Ewart de Bruijn Mapping Victor Guryev Variant
More informationEUROPEAN JOINT PROGRAMME ON RARE DISEASES DARIA JULKOWSKA INSERM, FRANCE
EUROPEAN JOINT PROGRAMME ON RARE DISEASES DARIA JULKOWSKA INSERM, FRANCE RARE DISEASES LANDSCAPE IN EUROPE STRATEGY FUNDING RARE DISEASES RESEARCH INFRA STRUCTURES HEALTH CARE + PATIENTS NEEDS 2 IRDiRC
More informationA Framework for Conceptualizing, Representing, and Analyzing Distributed Interaction. Dan Suthers
1 A Framework for Conceptualizing, Representing, and Analyzing Distributed Interaction Dan Suthers Work undertaken with Nathan Dwyer, Richard Medina and Ravi Vatrapu Funded in part by the U.S. National
More informationOutline. BIONT Goals. Work so Far. Collaborative BIONT BIORDF use case. Next Steps
Outline BIONT Goals Work so Far Collaborative BIONT BIORDF use case Next Steps BIONT Goals Develop best practices around crucial questions related to creation and use of ontologies: What is an ontology?
More informationBirdBase Update: Progress towards standardized gene nomenclature and tissue specific gene expression.
BirdBase Update: Progress towards standardized gene nomenclature and tissue specific gene expression. Fiona McCarthy, Carl Schmidt, Parker Antin, Shane Burgess Summary 1. What s in a name? A CGNC update.
More informationRD-ACTION DISSEMINATION PLAN TABLE I - STAKEHOLDER ANALYSIS
RD-ACTION DISSEMINATION PLAN TABLE I - STAKEHOLDER ANALYSIS Stakeholder Group Interest in RD-Action Dissemination purpose Channels of Dissemination (WHO) (WHY) (WHY) (HOW) Partners of RD-Action & members
More informationHuman health. Molecular mechanisms of biological systems. Teaching at. Research at. Brandeis University. Marine Biological Laboratory
Human health Molecular mechanisms of biological systems Research at Marine Biological Laboratory Bay Paul Center for Comparative Molecular Biology and Evolution Woods Hole, MA Teaching at Brandeis University
More informationOncoPPi Portal A Cancer Protein Interaction Network to Inform Therapeutic Strategies
OncoPPi Portal A Cancer Protein Interaction Network to Inform Therapeutic Strategies 2017 Contents Datasets... 2 Protein-protein interaction dataset... 2 Set of known PPIs... 3 Domain-domain interactions...
More informationUSEFULNESS OF ONTOLOGIES FOR RARE DISEASES
USEFULNESS OF ONTOLOGIES FOR RARE DISEASES M a n u e l P o s a d a, Ve r ó n i c a A l o n s o a n d E s t r e l l a L ó p e z M a r t í n I n s t i t u t e o f R a r e D i s e a s e s R e s e a r c h
More informationLong non-coding RNAs
Long non-coding RNAs Dominic Rose Bioinformatics Group, University of Freiburg Bled, Feb. 2011 Outline De novo prediction of long non-coding RNAs (lncrnas) Genome-wide RNA gene-finding Intrinsic properties
More informationExploiting deduction and abduction services for information retrieval. Ralf Moeller Hamburg University of Technology
Exploiting deduction and abduction services for information retrieval Ralf Moeller Hamburg University of Technology Seman&c Technologies: Data Descrip&ons Ontologies (specified in, e.g., OWL, UML) (aka
More informationSmall RNA-Seq and profiling
Small RNA-Seq and profiling Y. Hoogstrate 1,2 1 Department of Bioinformatics & Department of Urology ErasmusMC, Rotterdam 2 CTMM Translational Research IT (TraIT) BioSB: 5th RNA-seq data analysis course,
More informationIdentifying Potential Prognostic Biomarkers by Analyzing Gene Expression for Different Cancers
Identifying Potential Prognostic Biomarkers by Analyzing Gene Expression for Different Cancers Pragya Verma Department of Mathematics, Bioinformatics and Computer Applications Maulana Azad National Institute
More informationNature Methods: doi: /nmeth.3115
Supplementary Figure 1 Analysis of DNA methylation in a cancer cohort based on Infinium 450K data. RnBeads was used to rediscover a clinically distinct subgroup of glioblastoma patients characterized by
More informationB.I.R.O. Best Information through Regional Outcomes
B.I.R.O. Best Information through Regional Outcomes The role of BIRO in the implementation of the Diabetes EU Policy Recommendations Fabrizio Carinci, BIRO Technical Coordinator, University of Perugia
More informationThe Genetic Epidemiology of Rheumatoid Arthritis. Lindsey A. Criswell AURA meeting, 2016
The Genetic Epidemiology of Rheumatoid Arthritis Lindsey A. Criswell AURA meeting, 2016 Overview Recent successes in gene identification genome wide association studies (GWAS) clues to etiologic pathways
More informationUndiagnosed Rare Diseases: a bilateral project between. Italy (Istituto Superiore di Sanità) and USA (NIH)
Undiagnosed Rare Diseases: a bilateral project between Italy (Istituto Superiore di Sanità) and USA (NIH) Domenica Taruscio & Marco Salvatore National Centre for Rare Diseases Istituto Superiore di Sanità,
More informationEquipping Allied Health for Activity Based Funding -A marriage between clinical and technical partners-
Equipping Allied Health for Activity Based Funding -A marriage between clinical and technical partners- Paula Caffrey, Christine Fan, Patricia Bradd, Steven Bowden June 2014 The Brave New World of Health
More informationLecture 20. Disease Genetics
Lecture 20. Disease Genetics Michael Schatz April 12 2018 JHU 600.749: Applied Comparative Genomics Part 1: Pre-genome Era Sickle Cell Anaemia Sickle-cell anaemia (SCA) is an abnormality in the oxygen-carrying
More informationLipididentifizierung in der LC-MS-basierten Lipidomik mittels einer Kombination aus SWATH und DMS
Lipididentifizierung in der LC-MS-basierten Lipidomik mittels einer Kombination aus SWATH und DMS Michael Witting, et al. Helmholtz Zentrum München Research Unit Analytical BioGeoChemistry Berlin, 14/3/17
More informationObesity, Inflammation and Liver Cancer
Obesity, Inflammation and Liver Cancer Richard Moreau, M.D., 1 INSERM U773, Centre de Recherche Biomédicale Bichat-Beaujon CRB3, 2 Université Denis Diderot Paris 7, 3 Service d Hépatologie, Hôpital Beaujon,
More informationImproving efficiency in health. Hepatitis C virus session
Improving efficiency in health Hepatitis C virus session Aims of this session Optima is a tool to assist in reducing the burden of diseases by optimizing resource allocation. This session will discuss:
More informationApplication Note # MT-111 Concise Interpretation of MALDI Imaging Data by Probabilistic Latent Semantic Analysis (plsa)
Application Note # MT-111 Concise Interpretation of MALDI Imaging Data by Probabilistic Latent Semantic Analysis (plsa) MALDI imaging datasets can be very information-rich, containing hundreds of mass
More informationMini-Sentinel Common Data Model
info@mini-sentinel.org 1 Mini-Sentinel Common Data Model Lesley Curtis on behalf of the Mini-Sentinel Data Core May 8, 2013 info@mini-sentinel.org 2 Guiding principles (selected) Accommodates all requirements
More informationReal world Outcomes across the AD spectrum for better care: Multi-modal data Access Platform
Real world Outcomes across the AD spectrum for better care: Multi-modal data Access Platform Catherine Reed, Lilly on behalf of ROADMAP consortium 3 rd Nordic conference on Real World Data, 28-29 November
More informationPBZ FT01_PBZ FT01_TZ FT01_NZ. interface zone (I) tumor zone (TZ) necrotic zone (NZ)
Oncotarget, Supplementary Materials www.impactjournals.com/oncotarget/ SUPPLEMENTRY FLES ndividuals factor map (P) FT_ FT_ FT_ Dim (.%) Dim (.%) >% peripheral brain zone () around % interface zone () FT
More informationPractical challenges that copy number variation and whole genome sequencing create for genetic diagnostic labs
Practical challenges that copy number variation and whole genome sequencing create for genetic diagnostic labs Joris Vermeesch, Center for Human Genetics K.U.Leuven, Belgium ESHG June 11, 2010 When and
More informationBiomedical resources for text mining
August 30, 2005 Workshop Terminologies and ontologies in biomedicine: Can text mining help? Biomedical resources for text mining Olivier Bodenreider Lister Hill National Center for Biomedical Communications
More informationCommon Procedural Execution Failure Modes during Abnormal Situations
Common Procedural Execution Failure Modes during Abnormal Situations 2010 International Symposium Beyond Regulatory Compliance, Making Safety Second Nature Dr. Peter Bullemer Human Centered Solutions Liana
More informationEpiCARE a network for rare and complex epilepsies
Grant agreement no. 769051 EpiCARE a network for rare and complex epilepsies HP-ERN-2016 European Reference Networks / Framework Partnership Agreement D15.1: Proposal for EpiCARE clinical database Work
More informationModular Program Report
Disclaimer The following report(s) provides findings from an FDA initiated query using Sentinel. While Sentinel queries may be undertaken to assess potential medical product safety risks, they may also
More informationDr Rick Tearle Senior Applications Specialist, EMEA Complete Genomics Complete Genomics, Inc.
Dr Rick Tearle Senior Applications Specialist, EMEA Complete Genomics Topics Overview of Data Processing Pipeline Overview of Data Files 2 DNA Nano-Ball (DNB) Read Structure Genome : acgtacatgcattcacacatgcttagctatctctcgccag
More informationiplex genotyping IDH1 and IDH2 assays utilized the following primer sets (forward and reverse primers along with extension primers).
Supplementary Materials Supplementary Methods iplex genotyping IDH1 and IDH2 assays utilized the following primer sets (forward and reverse primers along with extension primers). IDH1 R132H and R132L Forward:
More informationPersonalized, Evidence-based, Outcome-driven Healthcare Empowered by IBM Cognitive Computing Technologies. Guotong Xie IBM Research - China
Personalized, Evidence-based, Outcome-driven Healthcare Empowered by IBM Cognitive Computing Technologies Guotong Xie IBM Research - China Explosion of Healthcare Data Exogenous data 1,100 Terabytes Generated
More informationTowards Biomedical Data Integration for Analyzing the Evolution of Cognition
Towards Biomedical Data Integration for Analyzing the Evolution of Cognition Amrapali Zaveri *, Katja Nowick **, and Jens Lehmann * * University of Leipzig, Institute of Computer Science, AKSW Group,,
More informationData Mining Scenarios. for the Discoveryof Subtypes and the Comparison of Algorithms
Data Mining Scenarios for the Discoveryof Subtypes and the Comparison of Algorithms Data Mining Scenarios for the Discoveryof Subtypes and the Comparison of Algorithms PROEFSCHRIFT ter verkrijging van
More informationUK - CGCM UKCGCM. Chairman Professor Ian Sutherland. Dr Jin Xu. 1) Nottingham University
UK - CGCM Chairman Professor Ian Sutherland Co-Chair Chair Dr Tai-Ping Fan Co-chair Dr Jin Xu 1) Nottingham University UKCGCM Kenneth Muir now moved to Warwick University 2) Oxford University Gerry Bodeker
More informationBreak-out session: Nordic Collaboration within Biobank Sciences
1 Break-out session: Nordic Collaboration within Biobank Sciences Kristian Hveem, MD, PhD, Professor in clinical epidemiology, NTNU Leader HUNT Biobank and Biobank Norway/BBMRI.se Leader Nordic Biobank
More informationWorkshop on Extrapolation and Evidence Synthesis in the Development and Therapeutic Use of Medicines in Children
Workshop on Extrapolation and Evidence Synthesis in the Development and Therapeutic Use of Medicines in Children Dr Oscar Della Pasqua WP 4 11th June 2013 Strathclyde University, Glasgow, UK Project AIMS
More informationAutomatic Pathology Software for Diagnosis of Non-Alcoholic Fatty Liver Disease
Automatic Pathology Software for Diagnosis of Non-Alcoholic Fatty Liver Disease (OTT ID 1236) Inventors: Joseph Bockhorst and Scott Vanderbeck, Department of Computer Science and Electrical Engineering,
More informationSemantic Web Applications in Financial Industry, Government, Health Care and Life Sciences
Wright State University CORE Scholar Kno.e.sis Publications The Ohio Center of Excellence in Knowledge- Enabled Computing (Kno.e.sis) 3-28-2006 Semantic Web Applications in Financial Industry, Government,
More informationNew biomarkers for diagnosis
Diabetesforschung 07.11.2016 The presence of certain proteins in blood samples can predict incipient type 1 diabetes. The researchers identify these in their measurements using so-called peptide peaks
More informationCollection of Statistics on Causes of Death Azza Badr, PhD, Vital Statistics and Country Support WHO/EMRO
Collection of Statistics on Causes of Death Azza Badr, PhD, Vital Statistics and Country Support WHO/EMRO ESCWA-UNSD GCCSTAT Meeting Muscat-Oman, 14-17 November 2016 Relevance of cause of death information
More information