Machine learning meets multi-omics for precision medicine
Scailyte's 5th anniversary Mini-symposium - Machine learning meets multi-omics for precision medicine
Date: 14.06.2022
Format: Online with panel discussions
Simultaneous analysis of millions of individual cells empowered with machine learning has potential to revolutionize treatments of many challenging human diseases including cancer, inflammatory and neurological disorders.
Biology has become a data science. For instance, recent single-cell profiling technologies are capable of measuring thousands of variables for each of hundreds of thousands to millions of cells in a single sample. Machine learning models are beginning to provide significant improvements in extracting information sensitively and with speed from such datasets which are often high-dimensional, sparse, and complex. Consequently, last year has seen enormous advances in deep learning applications in a variety of single-cell omics assays including genomics, transcriptomics, proteomics, metabolomics and multi-omics integration. It is highly timely to discuss the potential impact of insights generated by multi-omics machine learning platforms on the patient journey, clinical research and wider pharmaceutical sector through this miniconference.
We will cover examples and potential applications of deep learning on single-cell and other high throughput datasets in the context of:
- Early diagnosis
- Differential diagnosis against overlapping etiologies
- Patient stratification for therapy
- Design of clinical trials
- Understanding disease mechanisms
- Drug discovery and repurposing
- Lifestyle decisions
Target audience:
- Drug developers advancing precision medicine and biomarker-driven science
- Industry and academic partners running clinical trials
- Bioinformaticians and data scientists in academia and healthcare industries interested in machine learning approaches for multi-omics data
- Scientists interested in advancing biological understanding of disease and target identification with machine learning, single-cell and other multidimensional data
Programme:
Virtual: Zoom; all Times indicated are CET Times, Lines to open at 14:20 CET time.
TALKS:
14:30 –14:40 Welcome and objectives; Peter Nestorov, CEO, Scailyte
14:40 –15:00 Machine learning on Multiomics
Christopher Yau, Professor of Artificial Intelligence at the University of Oxford and Fellow at the Alan Turing Institute, UK
15:00 –15:20 Multiomics in industry I
Asif Jan, Chief Data Officer, Owkin
15:20 –15:40 Clinical applications of Multiomics
Michael Brenner, Elizabeth Fay Brigham Professor of Medicine at Harvard Medical School, USA
15:40 –16:00 Multiomics in industry II
Philippe Menu, Chief Medical Officer, Sophia Genetics
16:00 –16:20 Break
BRAINSTORMING SESSIONS:
16:20 –16:50 Parallel session I (Translating Multiomics discoveries into applications for Pharma industry and Clinic)
Panel members: Industry: Dr. Philippe Menu (Sophia Genetics), Dr. Marcus Otte (Merck), Dr. Miguel Edwards (DeciBio); Academia: Prof. Michael Brenner (Harvard Medical School), Prof. Marco Ruella (University of Pennsylvania), Prof. Woongyang Park (Sungkyunkwan University, Samsung Genome Institute and Geninus); Dr Diana Stoycheva (moderator, Scailyte)
16:20 –16:50 Parallel session II (Data Science solutions and future prospects for Multiomics and machine learning)
Panel members: Industry: Dr. Asif Jan (Owkin), Academia: Prof. Christopher Yau (University of Oxford); Dr. Kieran Campbell (University of Toronto), Dr. Dennis Göhlsdorf (Scailyte); Sarah Carl (moderator, Scailyte)
16:50 –17:00 Concluding remarks, Corinne Solier, COO, Scailyte
About Scailyte
Scailyte is an ETH Zürich spin-off with a best-in-class artificial intelligence platform for the discovery of complex disease patterns from single-cell data. Our solution provides unprecedented insight into the disease and patients’ biology and enables the discovery of new clinically-relevant biomarker signatures by uncovering human’s hidden “single-cell” secrets.
Scailyte’s proprietary best-in-class data analysis platform ScaiVision™ associates multimodal single-cell datasets (RNA-/TCR-/BCR-seq, proteomics, etc.) with clinical endpoints, such as disease diagnosis, progression, severity, treatment response, and toxicity response to identify ultra-sensitive biomarker signatures and cell functionality states. The performance and clinically-relevant applications of Scailyte’s platform ScaiVision have been demonstrated in well established CAR-T cell therapies and various clinical projects in Oncology and Immunology.
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