Title of the research project
Development of a tool for the prediction of phage’s bacterial host by machine learning
Description

Mariame Gnéré Coulibaly's project will focus on the development of a tool for the prediction of bacterial host of phages by machine learning (ML). The first objective will be to develop a ML tool to identify bacteria-phage pairs from CRISPR spacer sequences (clustered regularly interspaced short palindromic repeats) found in bacterial genomes, which are fragments of phage genomes that have infected the bacteria. The second objective is to develop a ML tool to identify bacteria-phage pairs based on sequence and methylation information (i.e.

  • Mariame Gnéré Coulibaly

    M.Sc. candidate
    Faculté des sciences et de génie
    Université Laval

    Directeur.e(s) de recherche
    Elsa Rousseau
    Start date
    Title of the research project
    Development of a tool for the prediction of phage’s bacterial host by machine learning
    Description

    Mariame Gnéré Coulibaly's project will focus on the development of a tool for the prediction of bacterial host of phages by machine learning (ML). The first objective will be to develop a ML tool to identify bacteria-phage pairs from CRISPR spacer sequences (clustered regularly interspaced short palindromic repeats) found in bacterial genomes, which are fragments of phage genomes that have infected the bacteria. The second objective is to develop a ML tool to identify bacteria-phage pairs based on sequence and methylation information (i.e. the addition of methyl groups to nucleotides). For the first two objectives, algorithms such as neural networks with attention mechanisms, and similarity predictors based on string kernels, will be developed and tested.

    The third objective is to develop a multi-view algorithm combining the two previous objectives, with one view for CRISPR information and a second view for methylation patterns.

    The tools developed will have a strong impact on the microbiology and virology research community, by being able to identify new bacteria-phage pairs from microbiota samples.

  • Fadwa Mehdaoui

    M.Sc. candidate
    Faculté des sciences et de génie
    Université Laval

    Directeur.e(s) de recherche
    Elsa Rousseau
    Start date
    Title of the research project
    Study of bacterial-phage interactions in the gut microbiota of Nunavik Inuit
    Description

    Fadwa Mehdaoui's project focuses on the analysis of interactions between bacteria and their viruses, called phages, in the microbiota using metagenomic data coupled with bioinformatics and machine learning methods.

    The metagenomic data come from the North Sentinel project 3.6 (axis Environment-health interactions in the North) that sampled the gut microbiota of young Inuit from Nunavik. Following the identification of bacteria and phages in the sequencing data, her work will consist of an exploratory statistical analysis of the interactions between bacteria and phages, followed by a machine learning analysis based on interpretable algorithms such as the set covering machine or random forests. It is also envisaged to develop a new machine learning model for phage host prediction based on these data.

    This project could have important implications for the understanding of the interactions between bacteria and phages, which are very poorly known, but also for the knowledge of the gut microbiota of the Inuit in relation to their unique diet.

  • Title of the research project
    Multi-view supervised machine learning for solving multi-omics problems
    Description

    Supervised classification allows to build predictive models based on complex data to help human decision making processes. It has undergone an impressive development in recent years, particularly thanks to neural networks and the use of big data. However, these methods are not relevant to use on databases in which only a few instances are available to build the model, and even less when these instances are described by a large number of features.

    Title of the research project
    Systematic evaluation of robustness and exploitation potential of radiomic features in magnetic resonance imaging.
    Description

    In medical imaging, radiomic features make it possible to characterize heterogeneity of a region of interest at the anatomical level. This way of quantifying the heterogeneity of a region of interest can be useful, for example, in order to identify the more aggressive tumors in oncology. To do this, we hypothesize here that variation in magnetic resonance imaging (MRI) acquisition sequences and its resulting different levels of contrast would make it possible to optimize the subsequent radiomic analysis.

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