Impressions

For two days, international experts in artificial intelligence in the life sciences discussed at the industry symposium "AI in the Life Sciences." They had traveled to Bonn at the invitation of Martin Hofmann-Apitius and Holger Fröhlich, both professors at the Bonn-Aachen International Center for Information Technology (b-it) and heads of the Bioinformatics department at Fraunhofer SCAI. Nevertheless, it was not a scientific conference like any other: The symposium featured a balance of presentations by internationally well-known experts from academia and industry. This led to an exciting exchange about current research results in academia and R&D projects in the pharmaceutical industry. The participants very well received this unique conference format.

At the start of the symposium, Thomas Sattelberger, Former Parliamentary State Secretary from the German Federal Ministry of Education and Research, made it clear that he was no friend of excessive regulation in artificial intelligence. "I stand for experimentation and light-touch regulation," Sattelberger said. Sattelberger opposed the planned AI regulation by the European Parliament and the EU. He firmly believed this would weaken Europe and Germany's innovative power.

Mihaela van der Schaar of the Cambridge Centre for AI in Medicine at the University of Cambridge suggested that "all problems in medicine should be considered time-series problems." One goal of her research is to develop time-series models that can estimate the frequency of occurrence of an event, taking into account competing risks. In this way, predicting the probability of patients' conditions deteriorating during treatment is possible. For example, she has developed machine learning algorithms to estimate the progression of prostate cancer during active surveillance.

The last speaker of the first day, Bryn Roberts' talked about AI in healthcare. He emphasized that it includes: “detection” (e.g., digital biomarkers), “prediction” (risk models and prediction of clinical path), and “interaction,” if recommender systems and ChatBots take care of first-level healthcare support. But one of the main issues is the need for trust in medical AI. He predicted, "AI won't replace physicians, but physicians who use AI will replace those who don't. "

Intense discussions arose after Roland Eils' presentation on "Data save lives – deep learning from health data." Eils, founding director of the Center for Digital Health at the Berlin Institute of Health at Charité, showed how artificial intelligence could predict the course of diseases. For example, analyzing medical records and laboratory values can help generate fairly accurate percentages of whether one will develop certain disease conditions. Of course, such statements also have ethical implications, of which Eils is very aware. However, he expressed his belief, "If I could get a reliable statement that my behavior could reduce my risk of cardiovascular disease by 30 percent: I would change it."

Anna Bauer-Mehring reflected on the future empowerment of pharma research and development through AI. Her topics ranged from chemical spaces constructed and mined by AI via AI-driven optimization of therapeutic antibodies to the replacement of animal studies through in-silico models for ADME (Absorption, Distribution, Metabolism, and excretion) and Toxicity. Finally, she talked about using real-world data, e.g., from social media, to better understand patients' responses to drug treatment.

Prof. Dr. Martin Hofmann-Apitius gives the first warm welcome of the day, special greetings to Dr. Thomas Sattelberger and Uni Bonn, and thanks to the organizers.
 

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Prof. Dr. Jürgen Bajorath recapitulates the success story of the Life Science Informatics (LSI) curriculum at B-IT.

• The key numbers:
  • international curriculum for 20 years
  • Bologna-conform before Bologna was implemented
  • all teaching in English
  • 80% of all Graduates (Master) continue with PhD studies, and approx. 90% of all graduates (Master & PhD) work in the industry later on
  • The demand for this “applied” curriculum is huge: we have 340 applications each year and accept only 25 students.
     

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Prof. Dr. Stefan Wrobel (Head of IAIS) made a strong point on the fact that “scientific excellence and impact on society are not mutually exclusive” and that the B-IT is actually bridging between science and industrial application. Furthermore, he highlighted the fact that the University of Bonn is amongst the top 100 worldwide.

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Prof. Michael Hoch, Rector of the University of Bonn, highlighted that “B-IT has pioneered interdisciplinary research.” From his background as a Professor of Biology, he reflects on the ongoing paradigm change in the Life Sciences: “Computer science and AI are predicting systems behavior, and biologists are confirming the prediction.” 

He also stated clearly that “we need brilliant minds everywhere, not only in Universities” and that “B-IT has pioneered transfer activities in a Comprehensive University that is usually biased towards basic research.” 

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Dr. h. c. Thomas Sattelberger (former Parliamentary State Secretary, BMBF) reflected on the AI and innovation landscape in Germany, the EU and compared that to the situation in leading innovation cultures (UK and US). One of the key insights of his talk was the question of the balance between “Opportunities vs. Need for Regulation.” He outlined the principles of Ex Post and Ex Ante regulation: Germany, in particular, is characterized by anticipating the need to regulate the unforeseen (“Ex Ante”). Sattelberger sees a need to balance Ex Post and Ex Ante better and considers the need to constantly re-negotiate ethics and reaction (ex post) on technology. A “code of conduct” for new innovation will help us to deal with the required ex post regulation.

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Prof. Dr. Roland Eils (Charité, BIH) recapitulated the AI „waves” that had an impact on biomedicine, with the 80s and 90s dominated by machine learning approaches and, more recently, deep learning taking over. He also emphasized on the role of Big Data in translational biomedicine and showed with the example of oncology how large-scale (interoperable) data can provide new insights into the genetic mechanisms underlying cancer. This knowledge is used for better translation, again by stratification of patients based on disease understanding and etiology. 

Large-scale studies across several indication areas showed that “age” and “sex” are amongst the strongest predictors of clinical outcomes for a variety of diseases; he also emphasized the utility of real-world data (such as EHRs) for risk assessment. Not surprisingly, the medical record of an individual is highly predictive of the personalized risk of that individual. Roland also emphasized the need to make clinical routine data better accessible for research and for individualized therapy.

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Dr. Shweta Bagewadi-Kawalia (Alumni, BASF) highlighted the role of BASF in everyday life: chemistry provided by BASF forms the basis of hundreds of products used by us on a day-to-day basis. Shweta focused on the required pragmatism in industrial AI. Sometimes, regular expressions do a perfect job and are much easier to implement than (re-)training a large language model for a specific domain.

Retrieval and classification of legacy documents from more than 100 years of chemical research at BASF is an objective where FAIR data, LLMs, and other AI services come together. Strongest impression with that talk: AI at BASF is solely driven by (internal) customers. No playgrounds.

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KAIRNTECH is a leading AI company in the field of text analytics. They use “AI on text” for information extraction, and Stefan Geißler showed an application use case that the company worked on together with Fraunhofer SCAI. 

The SHERPA workflow developed by KAIRNTECH identifies “cause-and-effect” statements in natural language (publications) and extracts these statements to build large knowledge graphs based on the syntax underlying the Biological Expression Language (BEL).  Stefan showed convincingly that entire indication areas can be represented as computable knowledge graphs covering major cause-and-effect relationships (e.g., pathophysiology mechanisms). 

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With the talk by Philipp Senger (Bayer AG) we “went green”. Patients were replaced by plants and clinical trials by field studies. Similarities between Crop Protection and Pharmaceutical Research were highlighted by Philipp, who provided a wonderful overview on digital farming, digital biomarkers in agriculture and crop protection and the application of AI methods in crop protection research. The “down-to-earth” approach making use of AI in a pragmatic approach that we learned about in the talk of Shweta Bagewadi-Kawalia (BASF) was echoed in Philipp Senger´s talk: pragmatic AI rules in industry. 

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