High-throughput bioinformatics, statistical machine learning, applications in molecular biology, personalised medicine, health, regulation of gene transcription.
Description of Research
Bioinformatics is an interdisciplinary research field combining computer science methods and statistical/mathematical modelling to analyse biological measurement data. New ways of conducting research in biology are now available with direct applications in cancer research and other biomedical. We develop and apply these methods for the integrative analysis of large-scale biological and clinical data. We work on a diverse set of biological problems, including genomics and medical genomics, network inference, and molecular oncology. The ultimate goal is the understanding of the mechanistic basis of human diseases, using a combination of computational and experimental data.
We collaborate with national and international research groups in molecular biology and personalized medicine applications.
a) High-throughput bioinformatics. High-throughput sequencing data analysis for bulk and single cell data using probabilistic / machine learning methods. The projects based on High-throughput bioinformatics used the capability of monitoring the genes activity in order to describe a better picture of the underlying biology.
b) Statistical machine learning. Gaussian processes, non-parametric methods. Deep generative models, deep learning. The projects based on Statistical machine learning want to overcome the difficulties of interpretation of big data by the use innovative deep learning methods.
c) Applications in molecular biology / personalized medicine / health. Cancer research, cancer-immunology. Biobank and health data analysis. Clinical data is a special kind of data. Our interests are in the integration of clinical information with High-throughput molecular profiles.
d) Regulation of gene transcription. In the projects related to the regulation of gene transcription, we want to explore the still hidden code that is present in DNA sequence responsible for cell-specific gene activation.
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