20th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology
29-31 August, 2023 – Eindhoven, The Netherlands
Regina Barzilay – Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology
Regina Barzilay is a School of Engineering Distinguished Professor for AI and Health in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. She is an AI faculty lead for Jameel Clinic, an MIT center for Machine Learning in Health. Her research interests are in applications of deep learning to chemistry and oncology. She is a recipient of various awards including the NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards at NAACL and ACL. In 2017, she received a MacArthur fellowship, an ACL fellowship and an AAAI fellowship. In 2021, she was awarded the AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity, the AACC Wallace H. Coulter Lectureship Award, and the UNESCO/Netexplo Award. In 2022, Regina was elected into the American Academy of Arts and Sciences. She received her PhD in Computer Science from Columbia University, and spent a year as a postdoc at Cornell University. Prof. Barzilay received her undergraduate degree from Ben-Gurion University of the Negev, Israel.
Pietro Liò – Department of Computer Science and Technology, University of Cambridge
Pietro Liò is a Full Professor of Computational Biology in the Artificial Intelligence Division of the Department of Computer Science and Technology at the University of Cambridge; Member of the Cambridge Centre for AI in Medicine; Member of the European Lab for Learning and Intelligent Systems (ELLIS); Member of Academia Europaea; he is. also Fellow of the Asia-Pacific Artificial Intelligent Association. He has a Doctorate in NonLinear dynamics and complex systems and a Doctorate in genomic sciences.
Website: https://www.cst.cam.ac.uk/people/pl219; https://www.cl.cam.ac.uk/~pl219
Yoeri Van de Burgt – Eindhoven University of Technology
Organic neuromorphic electronics for sensory coding and biohybrid systems
Neuromorphic engineering takes inspiration from the efficiency of the brain and focusses on how to utilise its functionality in hardware. However, delivering an efficient technology that is capable of embedding artificial neural networks in hardware remains a significant challenge. Organic electronic materials have shown potential to overcome some of these challenges and at the same time can operate at the interface with biology. This offers promising solutions for the manipulation and the processing of biological signals, with applications ranging from bioinformatics to brain-computer- interfaces and smart robotics.
This talk describes state-of-the-art organic neuromorphic devices and provides an overview of the current challenges in the field and attempts to address them. I demonstrate two device concepts based on novel organic mixed-ionic electronic materials and show how we can use these devices in trainable biosensors and smart autonomous robotics.
Next to that, organic electronic materials have the potential to operate at the interface with biology. This can pave the way for novel architectures with bio-inspired features, offering promising solutions for the manipulation and the processing of biological signals and potential applications ranging from brain-computer-interfaces to bioinformatics and neurotransmitter-mediated adaptive sensing. I will highlight our recent efforts for such hybrid biological memory devices.
Yoeri is associate professor at Eindhoven University of Technology, leading the Neuromorphic Engineering group within the Mechanical Engineering department and as part of the Institute for Complex Molecular Systems (ICMS) and Eindhoven A.I. Systems Institute (EAISI). He has been a visiting professor at the University of Cambridge in 2017 and at Georgia Tech in 2022. Prior to his position in Eindhoven, he was a postdoctoral fellow at Stanford University at the department of Materials Science and Engineering. Yoeri has won various awards including the MIT Innovator Under 35 Europe 2019, Rising Star by the journal Advanced Materials in 2020, and was a finalist for the Dream Chemistry Award 2019 and the New Scientist Talent Award in 2018. He has been awarded with the ERC Starting Grant in 2018 and is co-PI in the NWO Gravitation project IPM (2022). In 2019 he was elected member of the Eindhoven Young Academy, which he chaired from 2021 to 2022.
Marco S. Nobile (Ca’ Foscari University, Italy; Eindhoven University of Technology, The Netherlands)
Daniela Besozzi (University of Milano-Bicocca, Italy)
Paolo Cazzaniga (University of Bergamo, Italy)
Gonzalo Ruz (Universidad Adolfo Ibáñez, Chile)
Richard Allmendinger (University of Manchester, United Kingdom)
Steve Corns (Missouri University Columbia, USA)
Sheridan Houghten (Brock University St. Catharines, Canada)
Sansanee Auephanwiriyakul (Chiang Mai University, Thailand)
Chiara Gallese (LIUC – University of Castellanza, Italy)
Mohsen Abbaspour (Eindhoven University of Technology, The Netherlands)
James Hughes (Saint Francis Xavier University, Canada)
Renan C. Moioli (Federal University of Rio Grande do Norte, Brazil)
Special session chair
Simone Spolaor (Eindhoven University of Technology, The Netherlands)
Daniele Papetti (University of Milano-Bicocca, Italy)
Joseph Brown (Thompson Rivers University, Canada)
Local arrangements chair
Andrea Tangherloni (University of Bergamo, Italy)
Andrea Tangherloni (University of Bergamo, Italy)
Francesca Grisoni (Eindhoven University of Technology, The Netherlands)
Caro Fuchs (Eindhoven University of Technology, The Netherlands)
IEEE CIBCB 2023 solicits proposals for special sessions within the technical scope of the conference. Special session proposals should include the session title, a brief description of the scope, contact information, and a brief CV of the organizers. Please send your special session proposals to the Special Sessions Chair, Joseph Brown (email@example.com).
IEEE CIBCB 2023 will feature tutorials covering fundamental and advanced topics in computational intelligence and its application to problems within the scope of the conference. A tutorial proposal should include a title, outline, enrolment, and presenter/organizer biography. Please send your tutorial proposals to the Tutorial Chair, Simone Spolaor (firstname.lastname@example.org).
Topics of interest
Topics of interest include but are not limited to:
Computational Intelligence in Computational Biology
- Computational epidemiology
- Surrogate modeling and representation
- Systems and Synthetic Biology
- Structure prediction and protein folding
- Modelling, simulation, and optimization of biological systems
Computational Intelligence in Bioinformatics
- scRNAseq analysis
- Evolution, phylogeny
- Comparative genomics
- Gene expression array analysis
- Metabolic pathway analysis
- Sequence alignment
Computational Intelligence in Biomedical Engineering, Bioprocessing, and Healthcare Informatics
- Drug discovery
- Legal aspects of CI in medicine
- Ethics, fairness, biases
- Personalized medicine
- Medical imaging and pattern recognition
- Biomedical data modelling/data mining/model parametrization
- Parallel/high performance computing
- Biomarker discovery and development
- Health data acquisition/analysis/mining
- Healthcare information systems/knowledge representation/reasoning
Accepted Special Sessions
Applied AI Ethics in the biomedical field
In the biomedical field, a large number of sensitive personal data is constantly being processed and the decisions made by AI systems have a substantial impact on patients’ lives. It is important to explore the implications of research in the field of Computational Intelligence in Bioinformatics and Computational Biology and their use in real-life cases. AI systems may, in fact, have a significant impact on multiple stakeholders: researchers, producers, healthcare professionals, patients, and society as a whole, from an ethical, legal, and sociological point of view.
Recent research suggests that multiple individuals, especially marginalized groups, are negatively affected by biases and incorrect use of those systems. Furthermore, in the context of the legislative novelties aiming at regulating AI systems worldwide, the whole AI life cycle generates the need for regulatory and ethical compliance to remove biases and address potential risks. AI Auditing, Value Sensitive Design, and Applied Ethics are becoming a necessity rather than a choice.
However, the research in these fields has not reached an agreement on how to perform a comprehensive ethical assessment of the whole AI life-cycle, from the data collection to the post-marketing phases. Different fairness metrics, debiasing techniques, and auditing frameworks have been presented over the years.
This Special Session aims to gather scholars investigating new directions and ideas in the field of Applied AI Ethics, particularly in the biomedical field.
This Special Session welcomes papers regarding any ethical issue (including interpretability, fairness, and accountability) arising from AI systems; in particular, we are interested in techniques applied in the context of the biological or medical domain.
Examples include but are not limited to:
- Explainable/Interpretable AI systems for medical decision support
- Right of explanation and Trustworthy AI in the biomedical fieldù
- Biases in AI systems and debiasing techniques
- Fair methods for data preprocessing
- Fairness metrics in the context of biomedicine
- Unbiased data augmentation and privacy-preserving generative models
- Anonymization and pseudonymization of biobanks
- How personalized medicine through AI systems can contribute to
- mitigating discrimination and inequalities in healthcare
- The use of synthetic data to protect personal data
- Legal and ethical issues of data collection, data cleaning, feature/variable selection, and other phases of the development
- Ethical framework for medical AI systems
- Ownership of patients’ data and conditions for reuse
- Social consequences of a biased AI model
- AI regulation proposal and AI models in biomedical research
- Legal and ethical issues concerning the use of smart robots for surgery
- Ethical issues of open-source medical AI systems
- Comparative perspectives on the above issues in different areas (papers
exploring the legal system of under-represented countries are very welcome)
- Chiara Gallese (Eindhoven University of Technology, The Netherlands)
- Elena Falletti (Carlo Cattaneo University LIUC, Italy)
- Luca Manzoni (University of Trieste, Italy)
- Elisaveta Gromova (South Ural State University, Russia)
- Abeer Dyoub (University of L’Aquila, Italy)
Computational and statistical methods for the integrative analysis of biomedical datasets
The constant innovation of novel measurement technologies has granted the availability of large datasets describing, with increasing accuracy, complex biological phenomena. These datasets call for the development of computationally scalable algorithms and statistical models that can provide accurate clinical and biological insights in a timely manner.
In the last few years, omic sciences have witnessed the development of multiple technological advances that integrate spatiotemporal information in the data-collecting process. These novel measurements are characterized by extraordinary resolution resulting, for instance, in highly detailed images or high-frequency time series. For example, spatial transcriptomics is a recent class of groundbreaking technologies that allows the measurement of the activity of thousands of genes in a tissue sample and maps where the activity occurs in space. The additional spatial information brought by these protocols grants for the discovery of genes that show spatial patterns of expression variation across the analyzed tissues. Similarly, modern mass spectrometry techniques provide protein abundance measures and their corresponding time-of-flight for thousands of pixels in a biopsy. This precision enables the detection of small cell subpopulations based on their different protein profiles, even within regions that are indistinguishable at the microscopic level by pathologists.
It is clear that integrating additional external information, such as spatiotemporal features deriving from the experiment, constitutes an essential task for omic sciences and is a highly relevant asset when analyzing biological data. Therefore, advanced statistical models and efficient bioinformatic algorithms that integrate external sources of information in the analysis are fundamental.
This special session seeks to bring together the different perspectives of researchers operating in heterogenous, albeit interrelated, fields. Thus, the session aims to foster and present new ideas to develop more effective approaches that tackle large and complex biological datasets by considering computational, algorithmic, and methodological solutions to open problems.
This special session welcomes any contribution describing new approaches and tools that enable designers to effectively harness large amounts of data to analyse complex real-world situations for supporting the decisions of professional medical experts. Examples of possible contributions include, but are not limited to, computational algorithm, unsupervised and supervised AI methods, and statistical methodologies for spatiotemporal dataset, such as:
- Mass spectrometry data
- Spatial omics
- Microbiome data
- Multi-omics Integration
Some examples of methodologies of interest are:
- Fuzzy inference system
- Statistical methods for inference on imaging data
- Uncertainty quantification methods
- MCMC and Variational Bayes methods for massive data
- Design AI-empowered tools
- Cooperative learning
- Giulia Capitoli (University of Milan – Bicocca, Italy)
- Francesco Denti (Catholic University of the Sacred Heart – Milan, Italy)
- Andrea Sottosanti (University of Padova, Italy)
Instructions for authors
Prospective authors are invited to submit papers of no more than eight (8) pages in IEEE conference format including results, figures, and references. Papers must be in pdf form and written in English. Detailed instructions and templates for preparing your manuscripts can be found on the IEEE website. Each paper will be peer-reviewed. Submission implies the willingness of at least one author to register and present the paper at the conference. Each registration is able to cover a maximum of two papers. Extra papers submitted by one registered author beyond the maximum of two papers per registration will incur a charge of €150 per paper. Papers that are more than 8 pages in length will incur an extra length page charge of €100/page. No papers greater than 10 pages in length will be accepted.
Short papers (will be included on the conference website but not IEEExplore)
Short papers may present preliminary findings from work in progress, and industry perspectives on issues relevant to CIBCB 2023. Short papers should present and discuss a clear and focused central idea, incorporate discussion of relevant research or context, and provide references, as necessary. Short papers must adhere to the IEEE conference template and have a length of no more than two (2) pages. Papers must be in PDF and written in English. Short papers will be desk reviewed by the CIBCB 2023 organizational committee. Submission implies the willingness of at least one of the authors to register and present the paper at the conference. Short papers will be presented as a poster, or as part of a workshop or competition session
Download the sample short paper.
|Early bird (EURO)
By June 30
|Late and on-site registration (EURO)
After July 15
|IEEE student member||€250||€350|
|Student non member||€350||€450|