Simone Riva
Senior Computational and Machine Learning Scientist in Genomics
With foundational knowledge and training rooted in computer science, I have seamlessly amalgamated myself within the domains of biology and genomics. My primary area of expertise revolves around designing, developing, and implementing cutting-edge and high-throughput bioinformatic tools that harness the potential of computational intelligence. I am particularly oriented towards the latest advancements in machine learning technologies.
Furthermore, I develop pipelines for projects that guarantee both efficiency and reproducibility, encompassing adept data management and the generation of data tailored for utilization in machine learning applications.
My main project revolves around deciphering non-coding regions of the genome, encompassing coding, splicing, regulatory elements, and structural aspects.
Recent publications
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Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases.
Journal article
Pagnamenta AT. et al, (2023), Genome Med, 15
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MAGNETO: Cell type marker panel generator from single-cell transcriptomic data.
Journal article
Tangherloni A. et al, (2023), J Biomed Inform, 147
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CATCH-UP: A High-Throughput Upstream-Pipeline for Bulk ATAC-Seq and ChIP-Seq Data.
Journal article
Riva SG. et al, (2023), J Vis Exp
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MLL-AF4 cooperates with PAF1 and FACT to drive high-density enhancer interactions in leukemia.
Journal article
Crump NT. et al, (2023), Nat Commun, 14
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GTAC enables parallel genotyping of multiple genomic loci with chromatin accessibility profiling in single cells.
Journal article
Turkalj S. et al, (2023), Cell Stem Cell, 30, 722 - 740.e11