Research Centre of Bioinformatics
The Centre for Bioinformatics focuses on the theoretical and computational aspects of modelling the molecular biology processes, genome evolution and adaptive change, as well as biomedical data representation and integration. The goal is to bring basic research and new bioinformatics methods to real-world applications, ranging, for example, from biotechnology and forensics to biomedical research and environmental applications. The research area is represented by the several research groups, each focusing on certain methods or application domains.
The research group develops computational methods for comparative and evolutionary genomics, including modelling of stochastic processes in molecular evolution. Many research projects focus on the analysis of protein-coding genes and gene families, selection, adaptation, phylodynamics and evolution, including host-pathogen interactions; applications in medical genomics, epidemiology, metagenomics and forensics. Our research includes studies of genomic repeat sequences and indel evolution with applications in cancer genomics and biotechnology, as well as studies of dynamics and evolution of viruses and other pathogens.
The research group is specialized in the analysis of strings (i.e. finite sequences of symbols). The research projects and applications focus on genomic data and biomedical natural language. The group develops new computational science methods and applies existing methods. This includes: mathematical modeling, computational statistics, algorithm design, discrete mathematics, machine and deep learning, natural language processing, semantic web technologies.
The group develops and applies mathematical models and methods to address open research questions in biology. Many methods use standard calculus, differential equations, machine learning and dynamical systems theory to describe and predict biological phenomena, such as for example, the relationship between codon bias and gene expression via the concept of translational efficiency, applied to codon optimization problems. Further interests lie in the exploration of cancer-immune system interactions and their predictive power for cancer immunotherapies as well as the population genetics of the early infection-phase of partially-recombining viruses.
AI for colorectal cancer: towards the improved CMS classification and interpretability
Access to large complex biomedical data today allows scientists to take full advantage of AI-driven approaches in a variety of applications with high societal impact. One such application is precision medicine, which is gradually becoming reality for some cancers. Unfortunately, for colorectal cancer (CRC) ...
Digital Tools for Codon Optimization
This project proposes to develop, study and apply mathematical models to optimize protein production of a gene that stems from one organism in a different organism. The focus will lie specifically on genes involved in the biosynthesis of suberin. Suberin is a carbon-rich decay-resistant biopolyester, found majorly ...
Dynamik Knowledge Platform
Trans-omic approach to colorectal cancer: an integrative computational and clinical perspective
Colorectal Cancer (CRC) is an important cause of cancer-related mortality world-wide. The Consensus Molecular Subtypes represent the first comprehensive molecular classification with clinical implications, but many aspects are still missing. We use a transomic approach to improve the stratification, prognosis, and ...
Data mining in neurological medicine
Restless legs syndrome (RLS, Willis-Ekbom disease) is a neurological movement disorder characterised by motor and sensory symptoms, such as the uncontrollable need to move the legs (and sometimes also the arms). Such need is associated with an unpleasant and disturbing sensation in the lower limbs that typically ...
Apsley, Abner T.; Domico, Emma R.; Verbiest, Max A.; Brogan, Carly A.; Buck, Evan R.; Burich, Andrew J.; Cardone, Kathleen M.; Stone, Wesley J.; Anisimova, Maria; Vandenbergh, David J.,
Life Science Alliance.
6(4), pp. e202201677.
Available from: https://doi.org/10.26508/lsa.202201677
Verbiest, Max; Maksimov, Mikhail; Jin, Ye; Anisimova, Maria; Gymrek, Melissa; Bilgin Sonay, Tugce,
Journal of Evolutionary Biology.
36(2), pp. 321-336.
Available from: https://doi.org/10.1111/jeb.14106
Jowkar, Gholamhossein; Pecerska, Julija; Maiolo, Massimo; Gil, Manuel; Anisimova, Maria,
72(2), pp. 307-318.
Available from: https://doi.org/10.1093/sysbio/syac050
Sima, Ana Claudia; Mendes de Farias, Tarcisio; Anisimova, Maria; Dessimoz, Christophe; Robinson-Rechavi, Marc; Zbinden, Erich; Stockinger, Kurt,
Distributed and Parallel Databases.
40(2), pp. 409-440.
Available from: https://doi.org/10.1007/s10619-022-07414-w
Lardos, Andreas; Aghaebrahimian, Ahmad; Koroleva, Anna; Sidorova, Julia; Wolfram, Evelyn; Anisimova, Maria; Gil, Manuel,
Frontiers in Bioinformatics.
Available from: https://doi.org/10.3389/fbinf.2022.827207