The Phenotype day is an initiative developed jointly with the Bio-Ontologies Special Interest Group.
The systematic description of phenotype variation has gained increasing importance since the discovery of the causal relationship between a genotype placed in a certain environment and a phenotype. It plays not only a role when accessing and mining medical records but also for the analysis of model organism data, genome sequence analysis and translation of knowledge across species. Accurate phenotyping has the potential to be the bridge between studies that aim to advance the science of medicine (such as a better understanding of the genomic basis of diseases), and studies that aim to advance the practice of medicine (such as phase IV surveillance of approved drugs).
Various research activities that attempt to understand the underlying domain knowledge exist, but they are rather restrictively applied and not very well synchronized. In this Phenotype Day we propose to trigger a comprehensive and coherent approach to studying (and ultimately facilitating) the process of knowledge acquisition and support for Deep Phenotyping by bringing together researchers and practitioners that include but are not limited to the following fields:
- biology as well as computational biology
- genomics, clinical genetics, pharmacogenomics, healthcare
- text/data mining and knowledge discovery
- knowledge representation and ontology engineering
Example topics include but are not limited to:
- Representation of phenotypes
- Controlled vocabularies
- Ontologies (pre- and post-composed)
- Data standards
- Acquisition of phenotype descriptions
- NLP annotation tools and pipelines
- Tools and methods to support data curation for phenotypes
- Integration of textual data and controlled vocabularies/ontologies
- Phenotype discovery
- Collaborative development and peer-review
- Guidelines for phenotype data curation
- Quality control and evaluation
- Application of phenotypes to real world problems
- Methods for phenotype alignment and interoperability
- Drug repurposing / development
- Genotype-environment/phenotype-genotype/phenotype-disorder relation discovery
- Personalised medicine
(This is a tentative schedule and it may still suffer changes.)
Draft program for download: HERE
Prof. Tim Hubbard
Title: The 100,000 genomes project: Phenotype and Genome data for treatment and research within the UK National Health Service
Bio: Professor Tim Hubbard is Head of the Department of Medical & Molecular Genetics at King's College London and Director of Bioinformatics for King's Health Partners. Until 2013 he was Head of Informatics at the Wellcome Trust Sanger Institute where he was one of the organisers of the sequencing of the human genome. In 1999 he co-founded the Ensembl project to analysis, organise and provide access to the human genome and from 2007 led the GENCODE project to annotate the structure of all human genes. Tim is also Head of Bioinformatics at Genomics England, the company set up by the UK Department of Health to execute the 100,000 genomes project, which aims to mainstream the use of whole genome sequence analysis for treatment in the UK National Health Service (NHS). Eleven NHS hospital consortia have been designated as Genome Medicine Centres, consenting patients and collecting both samples and phenotype information for treatment and research. Genomics England is contracting centralized genome sequencing and clinical interpretation services for the NHS as well as a secure data environment for research.
Dr. Olivier Bodenreider
Title: Ontological, epistemological and terminological aspects of phenotypes
Bio: Olivier Bodenreider is a Senior Scientist and Chief of the Cognitive Science Branch of the Lister Hill National Center for Biomedical Communications at the U.S. National Library of Medicine. His research focuses on terminology and ontology in the biomedical domain, both from a theoretical perspective (quality assurance, interoperability) and in their application to natural language processing, knowledge discovery and information integration. Dr. Bodenreider is a Fellow of the American College of Medical Informatics. He received a M.D. degree from the University of Strasbourg, France in 1990 and a Ph.D. in Medical Informatics from the University of Nancy, France in 1993. Before joining NLM in 1996, he was assistant professor for Biostatistics and Medical Informatics at the University of Nancy, France, Medical School.
Dr. Robert Whelan
Title: The role of neuroimaging in phenotype characterization
Bio: Dr. Whelan is a faculty member in the Department of Psychology at University College Dublin, Ireland. Dr. Whelan’s research is directed towards answering clinically relevant questions, using both structural and functional magnetic resonance imaging and high-density electroencephalography. Current research includes the development of methods to interrogate high-dimensional datasets, with the goal of better characterizing psychiatric phenotypes.
Nigel Collier, University of Cambridge and the European Bioinformatics Institute (EMBL-EBI). Nigel is Principal Research Associate at the University of Cambridge and Visiting Scientist at the European Bioinformatics Institute. He has been active in many projects related to natural language processing for biomedical knowledge acquisition and data integration. He developed the BioCaster system for early alerting of infectious diseases from Web and social media data which has been widely used by international human and animal health agencies. In 2012 he was awarded an EC Marie Curie Fellowship to conduct research into the acquisition and linking of phenotypes in scientific and clinical texts and in 2014 he was awarded an EPSRC fellowship to conduction research into the Semantic Interpretation of Personal Health messages (SIPHS).
Anika Oellrich, Wellcome Trust Sanger Institute, UK. Anika conducted PhD studies in Bioinformatics at the University of Cambridge under supervision at the European Bioinformatics Institute, Rebholz group. She was then appointed as a Senior Bioinformatician in the Mouse Genome Informatics group at the Wellcome Trust Sanger Institute, Hinxton. Her research work focuses on aspects of phenotype mining, in large data sets as well as scientific literature. Having investigated the different representations of phenotypes, she applies this knowledge to data integration and human genetic disorders with the aim of improving the understanding about the molecular mechanisms underlying human diseases.
Tudor Groza is Phenomics Team Leader in the Kinghorn Centre for Clinical Genomics, at the Garvan Institute of Medical Research, Sydney Australia. Previously, he was a Research Fellow in the e-Research Group of the School of ITEE, at The University of Queensland. Tudor received his PhD in Computer Science from the Digital Enterprise Research Institute (DERI) Galway, National University of Ireland, Galway in 2010. In 2012 he has been awarded an ARC Discovery Early Career Researcher Award to investigate novel ways of extracting, consolidating and linking scientific artefacts present in biomedical publications, with a focus on evidence-based medicine. His current research covers the entire phenotype analytics stack, from representation to acquisition (from publications or clinical reports) and from cross-species integration to decision making (including disorder prediction, patient matchmaking or variant prioritisation).
Karin Verspoor is Associate Professor in the Department of Computing and Information Systems at the University of Melbourne. She was formerly the Scientific Director of Health and Life Sciences at NICTA Victoria Research Laboratory, Principal Researcher and leader of the NICTA Biomedical Informatics team. Her research addresses the development of knowledge-based methods to support biological discovery and clinical decision making, with recent work in protein function prediction and genetic variant interpretation, in addition to projects investigating the role of structured vocabularies for information retrieval in the clinical context. Karin has also been active in efforts to develop text annotation standards, both in terms of software architectures and data representations, to facilitate interoperability and reuse of tools and resources.
Nigam H. Shah
Dr. Nigam H. Shah is an Assistant Professor of Medicine (Biomedical Informatics) at the Stanford School of Medicine. Dr. Shah's research is focused on developing applications of bio-ontologies to annotate, index and analyze large unstructured datasets available in biomedicine. A key focus is to combine machine learning and text-mining with prior knowledge encoded in medical ontologies to discover hidden trends from the unstructured portion of the medical record and enable data-driven medicine. Dr. Shah holds an MBBS from Baroda Medical College, India, a PhD from Penn State University, USA and completed post-doctoral training at the Stanford Medical School.