Scope and Aims

With the vast improvements in computational resources, from a hardware, software as well as conceptual perspective, it has become possible to advance and accelerate fundamental biological research, as well as biomedical research. AI and advanced computational methods have become a fundamental pillar of pertinent research, rendering the scientific method more efficient and facilitating collaboration across disciplines. In particular, experimental and medical/clinical researchers can effectively work with computational experts since computational models have increasingly gained in detail, accuracy and realism. Along those lines, biological systems such as the brain, the immune system or specific organs can be captured based on experimental data from different spatial and temporal scales. Moreover, state-of-the-art AI and bioinformatics models can be employed using large-scale data-sets, which is further facilitated by the increasing availability of public databases and practicality for collaboration across labs.

The scope of this research topic includes innovative AI-assisted methods that are applied to biological and medical problems. Such problems should ideally focus on the fundamental processes pertinent to a given topic (e.g., biophysical, genetic or physiological). A broad range of biological and medical applications are relevant, such as for instance the brain, the immune system, cancer, or neurodegenerative disorders. The application can be with regards to fundamental science, to better understand the underlying disease factors, or for computational diagnosis as well as treatment optimisation. We expect the participants to consider relevance for different research communities, and formulate the research in a language that can be communicated within interdisciplinary settings.

The aims of this workshop are to present, discuss and exchange ideas on applications of state-of-the-art AI methods and techniques, for biology and medicine. Importantly, the employed approaches must incorporate contributions that go beyond classical and/or standard AI methods. In particular, given the crucial point of explainability and interpretability in biomedical modeling, we would like to study approaches that address current flaws in black-box AI techniques. We would like to achieve a general meeting where an open discussion of current challenges in AI for biology & medicine, and existing gaps is encouraged.

A focus of the meeting will be on the presentation of existing approaches, platforms and software that facilitate explainable model generation, comparison and testing. Ideally, these should be available as open-source, and support reproducibility, extendability and collaboration. Ultimately, we would like to see this meeting as a stepping stone for wider, international collaboration and grant proposals.