Simone Marini

Research Assistant Professor




I design prediction models for medicine and molecular biology with machine learning. I am particularly interested in data integration, i.e. in developing models by harvesting heterogeneous data from genomics, proteomics, medical literature, ontologies, and knowledgebases. I coordinated multi-disciplinary, international research teams comprising medical doctors, biologists, and engineers, across 7 institutions in Europe, Asia, and America.

Preferred Pronouns


Personal Academic Website

Research/Topics of Interest

Antibiotic resistance
Between-host modeling
Intra-host modeling
Machine learning models
Network models
New methods development
Phylogenetic models
data fusion
single cell RNA sequencing
protein-protein interaction
data integration
baysian networks

Pathogens/Diseases of Main Interest/Expertise

Human Immunodeficiency Virus (HIV)

Countries of Work/Collaboration

United States



Marini S, Mora RA, Boucher C, Robertson Noyes N, Prosperi M. (2022). Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing. Briefings in bioinformatics

Nicora G, Marini S, Salemi M, Bellazzi R. (2022). Dynamic Prediction of Non-Neutral SARS-Cov-2 Variants Using Incremental Machine Learning. Studies in health technology and informatics, (294)

Jun I, Rich SN, Marini S, Feng Z, Bian J, Morris JG, Prosperi M. (2022). Moving from predicting hospital deaths by antibiotic-resistant bloodstream bacteremia toward actionable risk reduction using machine learning on electronic health records. AMIA ... Annual Symposium proceedings. AMIA Symposium, (2022)

Magalis BR, Mavian C, Tagliamonte M, Rich SN, Cash M, Riva A, Loeb JC, Norris M, Amador DM, Zhang Y, Shapiro J, Starostik P, Marini S, Myers P, Ostrov DA, Lednicky JA, Glenn Morris J Jr., Lauzardo M, Salemi M. (2022). Low-frequency variants in mildly symptomatic vaccine breakthrough infections presents a doubled-edged sword. Journal of medical virology

Marini S, Oliva M, Slizovskiy IB, Das RA, Noyes NR, Kahveci T, Boucher C, Prosperi M. (2022). AMR-meta: a k-mer and metafeature approach to classify antimicrobial resistance from high-throughput short-read metagenomics data. GigaScience, (11)

Chowdary AR, Maertz T, Henn D, Hankenson KD, Pagani CA, Marini S, Gallagher K, Aguilar CA, Tower RJ, Levi B. (2022). Macrophage-mediated PDGF Activation Correlates with Regenerative Outcomes Following Musculoskeletal Trauma. Annals of surgery

Patel NK, Nunez JH, Sorkin M, Marini S, Pagani CA, Strong AL, Hwang CD, Li S, Padmanabhan KR, Kumar R, Bancroft AC, Greenstein JA, Nelson R, Rasheed HA, Livingston N, Vasquez K, Huber AK, Levi B. (2022). Macrophage TGFβ signaling is critical for wound healing with heterotopic ossification after trauma. JCI insight

Rife Magalis B, Rich S, Tagliamonte MS, Mavian C, Cash MN, Riva A, Marini S, Moraga Amador D, Zhang Y, Shapiro J, Horine A, Starostik P, Pieretti M, Vega S, Lacombe AP, Salinas J, Stevenson M, Myers P, Morris JG, Lauzardo M, Prosperi M, Salemi M. (2022). SARS-CoV-2 Delta vaccine breakthrough transmissibility in Alachua County, Florida. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America

Prosperi M, Boucher C, Bian J, Marini S. (2022). Assessing putative bias in prediction of anti-microbial resistance from real-world genotyping data under explicit causal assumptions. Artificial intelligence in medicine, (130)

Cella E, Ali S, Schmedes SE, Rife Magalis B, Marini S, Salemi M, Blanton J, Azarian T. (2022). Early Emergence Phase of SARS-CoV-2 Delta Variant in Florida, US. Viruses, 14(4)

Tarchi L, Damiani S, La Torraca Vittori P, Marini S, Nazzicari N, Castellini G, Pisano T, Politi P, Ricca V. (2021). The colors of our brain: an integrated approach for dimensionality reduction and explainability in fMRI through color coding (i-ECO). Brain imaging and behavior

Marini S, Oliva M, Slizovskiy IB, Noyes NR, Boucher C, Prosperi M. (2021). Exploring Prediction of Antimicrobial Resistance Based on Protein Solvent Accessibility Variation. Frontiers in genetics, (12)

Marini S, Mavian C, Riva A, Salemi M, Magalis BR. (2021). Optimizing viral genome subsampling by genetic diversity and temporal distribution (TARDiS) for phylogenetics. Bioinformatics (Oxford, England)

Prosperi M, Marini S, Boucher C. (2021). Fast and exact quantification of motif occurrences in biological sequences. BMC bioinformatics, 22(1)

Lee S, Hwang C, Marini S, Tower RJ, Qin Q, Negri S, Pagani CA, Sun Y, Stepien DM, Sorkin M, Kubiak CA, Visser ND, Meyers CA, Wang Y, Rasheed HA, Xu J, Miller S, Huber AK, Minichiello L, Cederna PS, Kemp SWP, Clemens TL, James AW, Levi B. (2021). NGF-TrkA signaling dictates neural ingrowth and aberrant osteochondral differentiation after soft tissue trauma. Nature communications, 12(1)

Lednicky J, Salemi M, Subramaniam K, Waltzek TB, Sabo-Attwood T, Loeb JC, Hentschel S, Tagliamonte MS, Marini S, Alam MM, Stephenson CJ, Elbadry M, Morris JG Jr.. (2021). Earliest detection to date of SARS-CoV-2 in Florida: Identification together with influenza virus on the main entry door of a university building, February 2020. PloS one, 16(1)

Mavian C, Ramirez-Mata AS, Dollar JJ, Nolan DJ, Cash M, White K, Rich SN, Magalis BR, Marini S, Prosperi MCF, Amador DM, Riva A, Williams KC, Salemi M. (2021). Brain tissue transcriptomic analysis of SIV-infected macaques identifies several altered metabolic pathways linked to neuropathogenesis and poly (ADP-ribose) polymerases (PARPs) as potential therapeutic targets. Journal of neurovirology

Prosperi M, Marini S. (2021). -mer-based Antibiotic Resistance Gene Analysis of High-throughput Sequencing Data. ... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics, (2021)

Mavian C, Marini S, Prosperi M, Salemi M. (2020). A Snapshot of SARS-CoV-2 Genome Availability up to April 2020 and its Implications: Data Analysis. JMIR public health and surveillance, 6(2)

Mavian C, Marini S, Prosperi M, Salemi M. (2020). Authors' Reply to: Errors in Tracing Coronavirus SARS-CoV-2 Transmission Using a Maximum Likelihood Tree. Comment on "A Snapshot of SARS-CoV-2 Genome Availability up to April 2020 and its Implications: Data Analysis". JMIR public health and surveillance, 6(4)

Mavian C, Marini S, Prosperi M, Salemi M. (2020). Correction: A Snapshot of SARS-CoV-2 Genome Availability up to April 2020 and its Implications: Data Analysis. JMIR public health and surveillance, 6(3)

Damiani S, Tarchi L, Scalabrini A, Marini S, Provenzani U, Rocchetti M, Oliva F, Politi P. (2020). Beneath the surface: hyper-connectivity between caudate and salience regions in ADHD fMRI at rest. European child & adolescent psychiatry

Mavian C, Pond SK, Marini S, Magalis BR, Vandamme AM, Dellicour S, Scarpino SV, Houldcroft C, Villabona-Arenas J, Paisie TK, Trovão NS, Boucher C, Zhang Y, Scheuermann RH, Gascuel O, Lam TT, Suchard MA, Abecasis A, Wilkinson E, de Oliveira T, Bento AI, Schmidt HA, Martin D, Hadfield J, Faria N, Grubaugh ND, Neher RA, Baele G, Lemey P, Stadler T, Albert J, Crandall KA, Leitner T, Stamatakis A, Prosperi M, Salemi M. (2020). Sampling bias and incorrect rooting make phylogenetic network tracing of SARS-COV-2 infections unreliable. Proceedings of the National Academy of Sciences of the United States of America

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