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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CREST-GV: Cell types Ranking and Enrichment Score for selecTive Genetic Variants
Preprint
Riva SG. et al, (2025)
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REnformer, a single-cell ATAC-seq predicting model to investigate open chromatin sites
Preprint
Riva SG. et al, (2025)
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GENESIS: Generating scRNA-Seq data from Multiome Gene Expression
Preprint
Riva SG. et al, (2025)
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Active regulatory elements recruit cohesin to establish cell specific chromatin domains.
Journal article
Georgiades E. et al, (2025), Sci Rep, 15
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Deciphering cis-regulatory elements using REgulamentary
Preprint
Riva SG. et al, (2024)