Challenges in Omics Data Integration
Challenges in Omics Data Integration
General context
------------------------------------------------------------------------------------------------------------------
On 15 & 16 October, there is an online demo/hands-on session and online Q&A on Using MOFA for integration of Omics Data.
------------------------------------------------------------------------------------------------------------------
In the era of Big Data, the tsunami of massive ‘omics’ data is revolutionizing the way we do science. Life science researchers are no longer analyzing one data set at a time but are moving towards multi-disciplinary integrative biology. It has been demonstrated that integration of different ‘omics’ data types (such as on genomes, transcriptomes, proteomes, epigenomes, etc..), boosts biological discoveries and improves predictions of the underlying interactions and regulation among molecular entities. Integrating different ‘omics’ datasets is a challenging task that relies heavily on data mining and machine learning algorithms. One must account for the specificities of each data type, solve problems associated with processing data across different platforms, and take into account the variable reliability levels of heterogeneous data.
In the second edition of this training on Multi-Omics data integration, four different topics will be addressed: ‘Multi-Omics Factor Analysis’, ‘Single Cell Data Analysis’, ‘Machine Learning, Deep Learning & Genomics/Proteomics’, and ‘Machine Learning in Drug Discovery and Disease’. We have invited top level speakers to share their insights with you on the latest developments in the field.
This course is organized in collaboration with Helis Academy. More information see https://helisacademy.com/
This training targets scientists interested in learning on omics data processing and integration. It is aimed at PhD students, postdoctoral fellows, technicians or other scientists with a molecular biology background.
Stein Aerts (VIB-KU Leuven Center for Brain & Disease Research)
Steven Maere (VIB-UGent Center for Plant Systems Biology)
Shoshana Wodak (VIB-VUB Center for Structural Biology)
- Participation is free for VIB participants.
- Registration fee for Academics from Universities and Research Centers is € 50 (21% VAT incl.).
- Registration fee for industry is € 300 (21% VAT incl.).
- Note that upon no-show without valid justification you will be blacklisted for the VIB training program for 1 year and for VIB participants the reimbursement of the catering costs (€ 50) will be charged to the research group. Click here for more information.
Trainers
Michel Dumontier
Edward Marcotte
Yvan Saeys
Yvan Saeys is associate professor at Ghent University and principal investigator at VIB, where he is heading an interdisciplinary research team of 25 people.
The Saeys lab studies the design and application of novel data mining and machine learning techniques, motivated by specific questions in biology and medicine. The group develops new computational approaches to unravel the regulatory landscape of immune cell differentiation and functioning.
Carl Herrmann
Julien Gagneur
Department of Informatics, Technical University of Munich, DE
Avi Ma'ayan
Sushmita Roy
Ernest Fraenkel
Stephen Mackinnon
Ricard Argelaguet
Research Scientist, Altos Labs UK.
Program
Welcome
Accelerating biomedical discovery science with an Internet of FAIR data and services
Michel Dumontier, Institute of Data Science, Maastricht University, NL
Session 1: Multi-Omics Factor Analysis
Multi-Omics Factor Analysis (MOFA): a statistical framework for the unsupervised integration of multi-omics data
Ricard Argelaguet, European Molecular Biology Laboratory European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK
Integrating evolution into proteomics: a case study mapping biochemical machinery across plants and beyond
Edward Marcotte, Molecular Biosciences, College of Natural Sciences, University of Texas at Austin, US
Networking break
Session 2: Single Cell Data Integration
Machine learning challenges for multi-modal single-cell data
Yvan Saeys, VIB-UGent Center for Inflammation Research, BE
Carl Herrmann, Health Data Science Unit, Medical Faculty University Heidelberg and BioQuant, Heidelberg, DE
Closing - networking
Welcome
Session 3: Machine Learning/Deep Learning in Genomics & Proteomics
Julien Gagneur, Department of Informatics, Technical University of Munich, DE
The Harmonizome-ML and Drugmonizome-ML Appyters: Web Interfaces to Impute Knowledge about Genes and Drugs with Machine Learning
Avi Ma’ayan, Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, US
Network-based integrative approaches to examine complex systems
Sushmita Roy, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, US
Networking break
Machine Learning in Drug Discovery & Disease
Integrating Multi-Omic and Clinical Data to Understand Neurodegenerative Diseases
Ernest Fraenkel, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, US
Designing Molecules to Satisfy Multiple Predictive Objectives
Stephen MacKinnon, Cyclica, Toronto, CA
Closing - networking
Practical info
Participants will receive more information on how to access the online platform to attend the training a few days before the start of the training.
We will use Hopin as online platform:
- For more information on the use of this platform, check this video.
- Please close other apps to maximize band width and use Chrome, Firefox or Safari (iOS) as browsers.
- Here you can find some additional trouble shooting tips.
Next to the talks of the speakers that will take place on the stage, make sure you don't miss out on the Q&A and interactive discussions with the speakers in the sessions! Recordings will be available and send together with evaluations.
We hope you will take the opportunity to network with the other attendees and with the speakers. Go to networking (open at all time) and meet new people or invite people you want to have a private chat with or a small group chat of up to five persons (click on the 'people' tab and invite whoever you want to meet).