training

Challenges in Omics Data Integration

Challenges in Omics Data Integration

08 October 2020 - 09 October 2020

This is an online course

Challenges in Omics Data Integration

omics
online
Location:

This is an online course

Start date:

08 October 2020

Duration:

8 & 9 October 2020 - afternoon sessions

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General context

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/

Afbeelding
Event intended for

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.

Scientific committee

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)

 

 

Course materials
Extra information
  • 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
Institute of Data Science, Maastricht University, NL
Edward Marcotte
Molecular Biosciences, College of Natural Sciences, University of Texas at Austin, US
Yvan Saeys

VIB-UGent Center for Inflammation Research

Carl Herrmann
Health Data Science Unit, Medical Faculty University Heidelberg & BioQuant, Heidelberg, DE
Julien Gagneur

Department of Informatics, Technical University of Munich, DE

Avi Ma'ayan
Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, US
Sushmita Roy
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, US
Ernest Fraenkel
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, US
Stephen Mackinnon
Cyclica, Toronto, CA
Ricard Argelaguet

Predoctoral Fellow in the Stegle  and Marioni research group of the European Bioinformatics Institute

Program

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Welcome

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Michel Dumontier, Institute of Data Science, Maastricht University, NL

Session 1: Multi-Omics Factor Analysis

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Ricard Argelaguet, European Molecular Biology Laboratory European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK

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Edward Marcotte, Molecular Biosciences, College of Natural Sciences, University of Texas at Austin, US

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Networking break

Session 2: Single Cell Data Integration

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Yvan Saeys, VIB-UGent Center for Inflammation Research, BE

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Carl Herrmann, Health Data Science Unit, Medical Faculty University Heidelberg and BioQuant, Heidelberg, DE

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Closing - networking

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Welcome

Session 3: Machine Learning/Deep Learning in Genomics & Proteomics

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Julien Gagneur, Department of Informatics, Technical University of Munich, DE

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Avi Ma’ayan, Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, US

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Sushmita Roy, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, US

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Networking break

Machine Learning in Drug Discovery & Disease

Drug Toxicity Predictions
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Ernest Fraenkel, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, US

Target Deconvolution/multi-pharmacology
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Stephen MacKinnon, Cyclica, Toronto, CA

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Closing - networking