DDLS Annual Conference

November 15, 2022 November 16, 2022
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DDLS Steering group


Eva & Georg Klein
Solnavägen 9
Stockholm, 17165 Sweden
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DDLS Annual Conference

November 15 @ 12:30 November 16 @ 12:30 CET

Welcome to a lunch-to-lunch conference focused on Data-driven Life Science. Listen to the DDLS Fellows and take part in social activities onsite.

The Data-Driven Life Science conference on November 15-16, 2022, is the first in-person conference of the DDLS program. This meeting brings together the community in biomolecular data science and AI, introduces newly appointed DDLS fellows, and provides opportunities for networking across the research community and SciLifeLab infrastructures. We are excited to host two international keynote speakers and welcome the community to join us. We are excited to meet in person at the poster session and other social events.

DDLS Annual Conference Poster session

Lightning talk and Poster session

The lightning talk is 1 minute and will be strictly timed. No slides. Please, indicate in the registration if you like to present a poster with, or without, a 1 min lightning talk.

DDLS Annual Conference Best Poster Award

The DDLS Annual Conference Best Poster Award encourages the submission and exhibition of high-quality posters carried out by young scientists, including Ph.D. students, post-doctoral researchers, etc. The poster should be on a topic related to data-driven life science. The Prize, which is based upon the decision of a Scientific Committee-appointed Jury, consists of a certificate and a travel grant of up to 5 000 SEK. The travel must be booked and ordered through the DDLS Support team and follow regular University travel policy. The trip should be completed before 2023-12-31.

Target group: We welcome all researchers interested in the DDLS program and data-driven life science.

Participants onsite: Venue Eva & Georg Klein, Floor 3 (ground floor), Biomedicum, Stockholm

Participate online: Online participation info, e.g, zoom link will be sent out upon registration

Organizer: SciLifeLab, host of SciLifeLab & Wallenberg National Program for Data-Driven Life Science.

Onsite: Register before November 9 at 12:00, so we can order coffee and lunch.


Tuesday, November 15

Drop-in Registration with Coffee and Wraps
Area: Social area directly to the right of the entrance. You can leave your coat and bag in the Lecture Hall. We ask the participants to be seated at 12:30.
Tuuli Lappalainen, KTH/SciLifeLab
DDLS updates
Olli Kallioniemi, SciLifeLab
Keynote: Samuli Ripatti, Institute for Molecular Medicine, University of Helsinki
Genetic variation and risk of common diseases over the life course
13:45Coffee break
DDLS Fellow Talk: Tom van der Valk, Museum of Natural History
Environmental DNA in the genomic decade

Infrastructures for data-driven life science
Johan Rung, SciLifeLab Data Centre
SciLifeLab Data Centre
Ola Spjuth, SciLifeLab Data Centre
Managing the life cycle of AI models and apps
Matts Karlsson, Linköping University
NAIS, Berzelius and the road ahead
Poster lightning talks (1 minute per poster)
Poster session with snacks and beverage
17:30End of Day 1
Wednesday, November 16
Keynote: Cecilia Clementi, Freie Universität Berlin
Designing molecular models with machine learning and experimental data
DDLS Fellow Talk: Johan Bengtsson-Palme, Chalmers University of Technology
Understanding the Evolution of Pathogenicity: Predicting and Preventing the Disease Threats of the Future
10:05Announcement of DDLS Annual Conference Best Poster Award
10:15Coffee break
DDLS Fellow Talk: Tobias Andermann, Uppsala University
Big data approaches for assessing biodiversity value and potential
DDLS Fellow Talk: Fredrik Edfors, Royal Institute of Technology, KTH
Harnessing the promise of next-generation plasma profiling for pan-cancer diagnostics
11:25DDLS Fellow Talk: Clemens Wittenbecher, Chalmers University of Technology
Metabolomics profiling generates candidate biomarkers for precision nutrition approaches
11:45Panel discussion: Training in Data Driven Life Science
Chair: Carolina Wählby, Uppsala University
Panelists: Leslie Solorzano Vargas, postdoc at KI
Nina Norgren, coordinator for DDLS education and training, Umeå University
Krzysztof Jurdzinski, PhD student at KTH
Daniel Gedon, PhD student at UU
12:30Joint conference lunch
Program updated 2022-11-12


Designing molecular models with machine learning and experimental data. Cecilia Clemens.

The last years have seen an immense increase in high-throughput and high-resolution technologies for experimental observation as well as high-performance techniques to simulate molecular systems at a microscopic level, resulting in vast and ever-increasing amounts of high-dimensional data. However, experiments provide only a partial view of the molecular processes and are limited in their temporal and spatial resolution. On the other hand, simulations are still not able to completely characterize large and/or complex molecular processes over long timescales, thus leaving significant gaps in our ability to study these processes at a physically relevant scale. We present our efforts to bridge these gaps, by combining statistical physics with state-of-the-art machine-learning methods to design optimal coarse models for complex macromolecular systems. We derive simplified molecular models to reproduce the essential information contained both in microscopic simulation and experimental measurements.

Genetic variation and risk of common diseases over the life course. Samuli Ripatti.

Past 15 years have seen tremendous success in identifying genetic loci associated with common diseases and traits. Much of the current activities focus on strategies for inferring causal variants and genes driving these associations and on the potential translational opportunities arising from the genetic discoveries. I will highlight some of the opportunities, risks and gaps in our knowledge related to both causal inference and genetic prediction and translation.  I will provide examples from FinnGen study (www.finngen.fi) consisting of genome wide profiles and longitudinal health registry data for  half a million Finns and from a network on biobanks and machine learning/AI -methods developers in INTERVENE Consortium (https://www.interveneproject.eu).

Predicting the disease threats of the future. Johan Bengtsson-Palme

Our inability to restrain covid-19 to remain a local disease outbreak highlighted an important vulnerability in our preparedness for new infectious diseases – our inability to predict what the future disease agents might look like. This inability makes it impossible to preemptively monitor for potential future pathogens and makes it harder to quickly adapt existing disease surveillance to novel outbreaks. There is no guarantee that the next major outbreak will be viral – with increasing antibiotic resistance, multidrug-resistant bacteria may very well be the next pandemic. In this talk, I will discuss how we can predict which genes to look for to find future pathogens before they become widespread in clinics or among the general population, both in terms of genes responsible for pathogenicity and in terms of antibiotic resistance genes. I will also outline how surveillance for antimicrobial resistance could be adapted to also include markers for pathogenicity and thus allow us to prevent or constrain disease outbreaks early.

Harnessing the promise of next-generation plasma profiling for pan-cancer diagnostics. Fredrik Edfors

Cancer is one of our biggest worldwide health problems, causing almost 10 million deaths annually. Despite significant advancements in cancer therapy over the past three decades, diagnosis and treatment still have a great deal of opportunity for improvement. 

Here, a total of 12 prevalent cancer types represented by more than 1,400 cancer patients’ plasma profiles of 1,463 proteins were evaluated in trace amounts of blood plasma taken at the time of diagnosis and prior to treatment. A group of proteins linked to each of the examined malignancies was found using ML-based disease prediction models. This precision medicine strategy has the potential to benefit from advances in proteomics and precision and personalized medicine.

Big data approaches for assessing biodiversity value and potential. Tobias Andermann

Our human societies worldwide are having a profound negative impact on the natural world surrounding us, leading to experts declaring the current biodiversity crisis. However, to date it is difficult if not impossible to actually quantify the magnitude of our impact on biodiversity. This is largely due to the overwhelming complexity of biodiversity, which is not easily summarized and reduced into manageable metrics. Yet, solving this task is necessary and is arguably one of the biggest and most urgent contemporary challenges for the biological research community. In this talk I will discuss the utility of environmental DNA (eDNA) sequencing as a tool for gathering comprehensive biodiversity data in the field. I will touch upon the challenges and possibilities regarding the application of eDNA data for this purpose, in particular how these data can be applied to train AI models with the ability to model biodiversity and simulate changes thereof.

Scientific Committee

Tuuli Lappalainen, chair and DDLS Steering group member, Clemens Wittenbecher, DDLS Fellow, Simon Olsson, WASP and Johan Rung, Data Centre.

Organizing Committee

Project leader Erika Erkstam, Operations office, and the DDLS support team Heidi T Persson, Ulrika Wallenquist, Titti Ekegren.

Solnavägen 9
Stockholm, 17165 Sweden
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View Venue Website

Last updated: 2022-11-13

Content Responsible: Erika Erkstam(erika.erkstam@scilifelab.uu.se)