TransMed 2024
Description
Knowledge-based translational medicine is a rapidly growing discipline in biomedical research and aims to expedite the discovery of new diagnostic tools and treatments by using a multi-disciplinary, highly collaborative, "bench-to-bedside" approach. Large amounts of multi-omics, imaging (medical and molecular) and clinical data can now be captured for given patient populations. In addition to the challenges of data curation and harmonisation, new computational methods are required to identify molecular signatures that suggest disease subtype. These signatures may be predictive of outcome or progression, and impact on disease management by suggesting personalised therapeutic strategies for patients. Such approaches will further the development of a new taxonomy of disease.
In the TransMed COSI meeting, we will explore the current status of computational biology approaches within the field of translational medicine.
Information of previous TransMed meeting(s) can be found here.
Link to the ISMB 2024 website is: here.
Please note:
The TransMed 2024 meeting takes place in Montreal, Canada, on July 16, 2024, during the ISMB 2024 conference.
Topics of interest
Topics of interest include, but are not limited to:
* Clinical and molecular data storage and integration infrastructure, including: data warehousing for translational medicine, multi-‘omics and clinical data integration, data visualization in translational medicine
* Curation and harmonization of clinical, ‘omics and imaging data, including: standards and ontologies in translational medicine, biomedical text mining and semantic representation
* Data analytics for patient stratification, biomarker and target discovery, including: disease subtype discovery, Electronic Health Records integration, translational imaging, multi-scale modelling, high performance and cloud computing in translational medicine, mathematical modelling for disease processes, pathways and networks
* Computational approaches for target selection and drug discovery, including: druggability assessment and target selection, polypharmacology, drug reuse, chemical library design, virtual screening technologies, drug discovery enabler pipelines and databases, chemical tool analysis
* ADME/PK and Tox models, including: databases and modeling approaches for ADME and PK, machine learning approaches to predicting toxicity, modeling of pharmacokinetics to man and model organism utility models
* Translational Medicine Informatics Applications/Case Studies, including: Next generation sequencing annotation and biomedicine applications, clinical data integration and application
Keynote speakers
Prof. Heidi Rehm, Massachusetts General Hospital, Broad Institute, Harvard Medical School, USA
Biography: Heidi Rehm is an investigator in the Center for Genomic Medicine at MGH, Co-Director of the Program in Medical and Population Genetics at the Broad Institute as well as the Medical Director at Broad Clinical Laboratories. She is a principal investigator of ClinGen and gnomAD, providing resources to support the interpretation of genes and variants. Rehm co-leads the Broad Center for Mendelian Genomics and the Matchmaker Exchange focused on discovering causes of rare disease. She is a strong advocate and pioneer of open science and data sharing, working to extend these approaches as vice chair of the Global Alliance for Genomics and Health.
Title: Advancing Genomic Medicine through Clinical and Research Strategies
Abstract of the talk: Supporting genomics in research and medicine requires infrastructure, including standards, knowledgebases and global data sharing, as well as a rich interface between research and clinical care as new discoveries are made. This talk will present strategies to identify novel causes of rare disease including the application of new technologies and analysis methods as well as building innovative approaches to global data sharing in collaboration with AnVIL and the Global Alliance for Genomics and Health. It will end on novel approaches to support genetics and genomics in medical practice.
Dr. Quaid Morris, Memorial Sloan Kettering Cancer Center, USA
Biography: Quaid Morris is a Member of the Computational and System Biology program at Sloan Kettering Institute and co-Director of the Graduate Program in Computational Biology and Medicine at Weill-Cornell Graduate School. He is also a full professor at the University of Toronto in Molecular Genetics and Computer Science. During his PhD, Dr Morris trained at Massachusetts Institute of Technology and Gatsby Unit in machine learning and computational neuroscience. Morris lab (http://www.morrislab.ai/) uses techniques from machine learning and artificial intelligence to do biomedical research, focusing on cancer genomics, gene regulation, and clinical informatics.
Title: The challenges of clinical deployment of automated cancer type classification for routine use
Abstract of the talk: Accurate cancer type classifiers would have profound impact on the success of cancer treatment. Each year, in the US, more than 30,000 people present with new cancers of unknown primary (CUP), for which treatment options are very limited. Up to half of these patients could be matched with FDA-approved therapies if their cancer type were known. Cancer type classifiers can also distinguish new cancers from reoccurrences and resolve difficult diagnostic challenges. We recently deployed a highly accurate cancer type classifier, GDD-ENS, at Memorial Sloan Kettering Cancer Center (MSKCC) based on inputs derived from an FDA-approved, and routinely applied, targeted DNA sequencing panel called MSK-IMPACT. GDD-ENS, based on ENSembles of multilayer perceptrons, and replaced a pre-existing MSKCC system, GDD-RF. To make GDD-ENS well-suited to the clinical setting, based on lessons learned from GDD-RF, we made specific design choice in the classifier, in its training and evaluation, and how its outputs are integrated with other routinely available clinical data. I will present GDD-ENS, these choices and their impacts, as well as, GDD-ENS’ successes and some areas of improvement. I will also discuss our efforts to generalize GDD-ENS to other targeted cancer gene panels. Joint work with Dr Michael Berger and our labs.
Agenda
Please check it here [TBD].
Abstract submission
Please follow the abstract submission indications from the ISMB 2024 website.
Key dates
Detailed information can be found: here.
Registration
Please follow the registration page on the ISCB website.
Organizing committee
Venkata Satagopam - Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
Reinhard Schneider - Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
Maria Secrier - University College London, UK
Irina Balaur - Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
Irene Ong - University of Wisconsin-Madison, USA
Wei Gu - Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
Heba Sailem - King’s College London, UK
Mansoor Saqi - King’s College London, UK
Evert Bosdriesz - Vrije Universiteit Amsterdam, NL
Sikander Hayat - Uniklinik RWTH Aachen, Germany
Meet the Organizing committee here.
Programme committee (alphabetical order)
Marcio Luis Acencio - Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg
Mirek Kratochvil - Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg
Xinhui Wang - Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg
Contact us
If you have any questions, please do not hesitate to contact us by our slack workspace.
Follow us
Twitter: @TransMedISMB
Linkedin: Translational Medical Informatics Group
Open positions
Please check our page on open positions here. Alternatively, please feel free to contact us directly.
Acknowledgement
The current TransMed logo was designed by Belinda Hanson.