IGH-IGMM-IPSIM-LIRMM External Seminar
Thursday May 30, 2024 – 11:00 am
Amphithéâtre – CNRS DR13, 1919 route de Mende, Montpellier
« A novel General Genome Interpretation Paradigm for Clinical Genetics & Plant Biology »
Dr. Daniele RAIMONDI
Bioinformatics & Computational Biology, STADIUS – ESAT, KU Leuven, Belgium
Inscriptions : https://evento.renater.fr/survey/seminar-a-novel-general-genome-interpretation-paradigm-for-clinical-genetics-plant-biology-3h5d3cmv
Daniele Raimondi obtained his Ph.D. in Brussels in 2017, where he developed Machine Learning-based Bioinformatics methods for predicting the deleterious effects of genomic variants in humans. He then moved to the University of Leuven, where he is a senior post-doc. Building on his past expertise and on the latest advancements in Deep Learning, he devised a new paradigm of Bioinformatics methods for Genome Interpretation that aim at directly modeling the relationship between genotype and phenotype, with the end goal of enabling Precision and Stratified Medicine.
Genome Interpretation (GI) refers to the scientific endeavors towards understanding the genotype-phenotype relationship. Being able to precisely model how the information encoded in our genome leads to the observed phenotypes would indeed constitute a crucial advancement for genetics and molecular biology, and could open a new era for precision medicine and agricultural technology. Current GI Bioinformatics methods present several limitations, but the unprecedented availability of sequencing and phenotypic data, coupled with the recent fast-paced advancements in Deep Learning are making the development of a new breed of end-to-end GI methods finally possible.
Relevant publications:
[1] N. Verplaetse, A. Passemiers, A. Arany, Y Moreau, D. Raimondi. Large sample size and nonlinear sparse models outline epistatic effects in inflammatory bowel disease. Genome Biology 24 (1), 224 (2023)
[2] D. Raimondi, M. Corso, P. Fariselli, Y. Moreau. From genotype to phenotype in Arabidopsis thaliana: in-silico genome interpretation predicts 288 phenotypes from sequencing data. Nucleic Acids Research, 2021; gkab1099, https://doi.org/10.1093/nar/gkab1099 2022
[3] D. Raimondi, J. Simm, A. Arany, P. Fariselli, I. Cleynen, Y. Moreau. An interpretable low-complexity machine learning framework for robust exome-based in-silico diagnosis of Crohn’s disease patients. NAR Genomics and Bioinformatics, Volume 2, Issue 1, March 2020, lqaa011, https://doi.org/10.1093/nargab/lqaa011
[4] D. Raimondi, J. Simm, A. Arany, Y. Moreau. A novel method for data fusion over entity-relation graphs and its application to protein–protein interaction prediction. Bioinformatics, Volume 37, Issue 16, 15 August 2021, Pages 2275–2281, https://doi.org/10.1093/bioinformatics/btab092
[5] D. Raimondi, I. Tanyalcin, J.S.D. Fertè, A. Gazzo, G. Orlando, T. Lenaerts, M. Rooman, and W. Vranken. Deogen2: Prediction and interactive visualization of single amino acid variant deleteriousness in human proteins. Nucleic Acids Research, 45(W1):W201–W206, 2017.
Contact: etienne.schwob@igmm.cnrs.fr