About the Bio-Informatics Laboratory (BIL)

The Bio-Informatics Laboratory (BIL) was established in January 2011 as the evolution of the Bioinformatics activity of the former “Biomedical Informatics Laboratory” in an effort to strengthen and support bioinformatics and related research at ICS-FORTH. BIL’s mission is to progress biological and translational research via the application of computational methods, advance computational methods for performing research in biology, disseminate the research results to the community, and promote education in the field of bioinformatics. More specifically, BIL is engaged in the following activities:

• Research in Computational Bioinformatics Methods:

Theoretical and algorithmic research in areas of bioinformatics to improve the state- of-the-art and invent solutions to new problems.

Applied Research:

• Applications of the state-of-the-art and best-practices in computational methods on specific biological problems with the intent to discover new knowledge.

Education:

• Dissemination of knowledge and technical know-how by educational activities, such as teaching university courses, tutorials, summers schools, as well as supervising undergraduate dissertations, Masters’ projects, and Ph.D. theses.

Systems and Software:

• Dissemination of knowledge, technical know-how, research results and facilitate research by the implementation of tools, systems, and code libraries.

BIL is quite active in collaborating with several biological, medical, or computational groups and laboratory nationally, internationally, and locally, particularly with the Institute of Molecular Biology and Biotechnology (IMBB-FORTH). In addition, BIL’s researchers routinely participate in national and European funded research projects. Recognition of BIL’s research is reflected in the number of citations to publications, invited talks, participation in Editorial Boards and Program Committees of prestigious journals and conferences in the field, as well as several research awards. BIL’s computational expertise is focused on Machine Learning, Data Mining, Statistical, and Artificial Intelligence methods. In more detail, current activities are focused on the following:

• Methods: The lab is interested in developing causal discovery methods that can induce knowledge about the possible causal relations of biological quantities. In addition, it is interested in biomarker and molecular signature identification methodology; this is also known as the feature selection problem in Machine Learning. Our research with collaborators has revealed deep theoretical connections with the causal structure of the data distribution and has developed scalable, efficient, and accurate learning algorithms that identify the minimal-size, most-predictive set of biomarkers (e.g., genes), that multi-variately predict or diagnose a disease, condition, gene expression level, or other quantity. BIL researchers have recently introduced a new paradigm based on causal modeling of the data, named Integrative Causal Analysis, with the potential of developing methods for co-analyzing datasets on different samples that are heterogeneous in terms of measured quantities, experimental conditions, or sampling methodologies, in the context of prior causal knowledge.
• Bioinformatics Applications: The computational expertise is applied on several biological problems in collaboration with biologists and clinicians in other labs. Examples include the understanding of the miRNA biogenesis and targeting, the chemosensitivity prediction of tumours based on genomic information, the understanding of the role of DNA damaging mechanisms in aging, the understanding of causal mechanisms in human mesothelioma cancer and others.
• Systems and Software: BIL's researchers have a long tradition disseminating novel computational methods in the form of software libraries and award-winning, easy-to-use, automated tools for research. Novel algorithms for feature selection methods and learning graphical models such as Bayesian Networks are provided in open-source format at the laboratory’s web-site or related links.