AnacletoLab is part of the DI - Dipartimento di Informatica (Computer Science Dept.) of the Università degli Studi di Milano.
AnacletoLab is part of the DI - Dipartimento di Informatica (Computer Science Dept.) of the Università degli Studi di Milano.
This data science laboratory is focused on the development and application of novel Artificial Intelligence (AI) approaches to various types of data, including genomic and exposomic data.
This data science laboratory is focused on the development and application of novel Artificial Intelligence (AI) approaches to various types of data, including genomic and exposomic data.
We are interested in relevant computational biology problems, ranging from the automated functional annotation of proteins, to Systems Biology and Network Medicine problems, and to the detection of non coding genetic variants associated with genetic diseases. These topics raise computational problems that stimulate the development of novel machine learning methods or the adaptation of existing algorithms to relevant problems in Biology and Medicine.
We are interested in relevant computational biology problems, ranging from the automated functional annotation of proteins, to Systems Biology and Network Medicine problems, and to the detection of non coding genetic variants associated with genetic diseases. These topics raise computational problems that stimulate the development of novel machine learning methods or the adaptation of existing algorithms to relevant problems in Biology and Medicine.
We contributed to several international projects in the context of Precision Medicine, collaborating with the Jackson Lab (USA), with the Berlin Institute of Health (Charitè, Berlin), the Computer Science and Bioinformatics center of the Royal Holloway (University of London), the S.Raffaele Hospital of Milan and other leading computational and bio-medical research groups in Europe. We realized the machine learning-based core of Genomiser, the state-of-the-art computational tool for the detection of pathogenic variants causative of genetic Mendelian diseases, and other AI-based computational tools for applications in the diagnosis, prognosis and biomarker discovery of genetic, neurodegenerative diseases and cancer, using integration techniques to combine different types of omics data.
We contributed to several international projects in the context of Precision Medicine, collaborating with the Jackson Lab (USA), with the Berlin Institute of Health (Charitè, Berlin), the Computer Science and Bioinformatics center of the Royal Holloway (University of London), the S.Raffaele Hospital of Milan and other leading computational and bio-medical research groups in Europe. We realized the machine learning-based core of Genomiser, the state-of-the-art computational tool for the detection of pathogenic variants causative of genetic Mendelian diseases, and other AI-based computational tools for applications in the diagnosis, prognosis and biomarker discovery of genetic, neurodegenerative diseases and cancer, using integration techniques to combine different types of omics data.
Members of the laboratory authored more than 150 publications in top ranked journals and conferences in the field of Computational Biology and Bioinformatics.
Members of the laboratory authored more than 150 publications in top ranked journals and conferences in the field of Computational Biology and Bioinformatics.
The laboratory is responsible for UNIMI, as part of the PhD School in Computer Science, of the Collaborative Doctoral Partnership in the field of Genomics and Bioinformatcs in collaboration with the Joint Research Centers of the European Union, and offers bioinformatics courses at Master and PhD level.
The laboratory is responsible for UNIMI, as part of the PhD School in Computer Science, of the Collaborative Doctoral Partnership in the field of Genomics and Bioinformatcs in collaboration with the Joint Research Centers of the European Union, and offers bioinformatics courses at Master and PhD level.
For more details about our research, please look at the Research section.
For more details about our research, please look at the Research section.