CV

Experience

Postdoctoral Researcher. Harvard School of Public Health, Department of Biostatistics. Boston, MA.

Topics and interests:

• Inference of individual-specific Gene Regulatory Networks for personalized medicine
• Analysis of multi-omics assays to infer tumor evolution trajectories.
• Software and workflow development
• Teaching and mentoring

Research Technician. The University of Edinburgh. Edinburgh UK

• Analysis of sequencing data for synthetic biology
• Graph Neural Networks for interpretable cancer network modeling and driver gene identification

Education

PhD student in Computational Biology. Institute for Quantitative Biology, Biochemistry and Biotechnology, The University of Edinburgh. Edinburgh, UK. Supervised by Dr. G. Stracquadanio and Prof. G. Sanguinetti

Research Interests: cancer heritability estimation from GWAS data; deep neural networks for network inference; gene expression analysis.

2017, MSc. in Biomedical Engineering. Università degli Studi di Pisa, Italy.

Dissertation: “Classification of resting state fMRI datasets: machine learning methods for the identification of patients with anxiety disorders”. Supervisors: N. Vanello, L. Citi, C. Gentili.

Relevant modules : stochastic processes, signal processing, bioinformatics, statistical analysis, exploratory data analysis, database development and programming, digital and analogue electronics.

2017, BSc. in Biomedical Engineering. Università degli Studi di Pisa, Italy.

Recent publications

Full list is on Google Scholar

• Saha, E. & Fanfani, V. et al. ‘Bayesian inference of sample-specific coexpression networks’. Genome Research, 2024. https://doi.org/10.1101/gr.279117.124
• Hossein, I. et al. ‘Biologically informed NeuralODEs for genome-wide regulatory dynamics’. Genome Biology, 2024. https://doi.org/10.1186/s13059-024-03264-0
• Ben Guebila, M. et al. ‘The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks’. Genome Biology 2023. https://doi.org/10.1186/s13059-023-02877-1
• Zhao et al. ‘Debugging and consolidating multiple synthetic chromosomes reveals combinatorial genetic interactions’. Cell, 2023
• Fanfani et al. ‘Discovering cancer driver genes and pathways using stochastic block model graph neural networks’. Biorxiv 2021
• Fanfani, Viola, et al. ‘The landscape of the heritable cancer genome‘. Cancer Research, 2021. https://cancerres.aacrjournals.org/content/early/2021/03/12/0008-5472.CAN-20-3348
• Fanfani, Viola, et al. ‘PyGNA: a unified framework for geneset network analysis‘. BMC Bioinformatics, 2020. https://doi.org/10.1186/s12859-020-03801-1
• Fanfani, Viola, et al. ‘Dissecting the heritable risk of breast cancer: from statistical methods to susceptibility genes.’ Seminars in Cancer Biology. Academic Press, 2020. https://doi.org/10.1016/j.semcancer.2020.06.001
• Draberova, Helena, et al. ‘Systematic analysis of the IL‐17 receptor signalosome reveals a robust regulatory feedback loop.’ The EMBO Journal (2020): e104202. doi: https://doi.org/10.15252/embj.2019104202

Conferences

• 2024, RECOMB, Cambridge, US. Presentation: “BONOBO: Bayesian Optimized sample-specific Networks Obtained By Omics data”.
• 2022, Reproducibility, Replicability and Trust in Science (virtual), Wellcome Genome Campus, UK. “Research practices, reproducibility, and overhead in biostatistics and bioinformatics”
• 2021, Network Biology virtual meeting, CSHL, US. Poster Presentation: “Interpretable graph neural networks unveil system‑level reprogramming in cancer”.
• 2020, The Biology of Genomes virtual meeting, CSHL, US. Poster Presentation: “Decoding cancer risk in the broader population with gene-level heritability”.
• 2019, Genes and Cancer Meeting, Cambridge, UK. Selected flash talk: “Dissecting cancer heritability in European populations”.
• 2018, From functional genomics to system biology, EMBL, Heidelberg, DL. Poster presentation: “Geneset Network Analysis: understanding high-throughput genomic data using the interactome”.
• 2018, BACR Students Conference. Poster presentation: “A new geneset analysis approach to identify and characterise cancer pathways”

Other

• 2019 – Invited talk “Cancer heritability, decoding the contribution of high frequency variants to cancer risk”. SynthSys, University of Edinburgh
• I have served as reviewer for BMC Bioinformatics, BMC Genomics, and Frontiers in Molecular Biosciences.

Skills

• Core Competencies: Algebra, number theory, and calculus. Univariate and multivariate statistics. Bayesian inference. Graphs. Stochastic signal processing and image processing. Machine learning. Chemistry, biochemistry, and physiology fundamentals.
• Programming
  • Languages: python, R, MATLAB & Simulink, C/C++, SQL, android/Java
  • Scientific packages: numpy, scipy, pymc, pytorch, networkx
  • Workflow systems : nextflow, snakemake
  • Data visualisation, Python and R, Photoshop, Illustrator
• Other: Git, Latex, docker, conda, AWS, bash, HPC

Maintained Software

• netZooPy. Python package for gene regulatory network methods https://github.com/netZoo/netZooPy
• BAGHERA. Bayesian Gene Heritability Analysis from GWAS summary statistics. https://github.com/stracquadaniolab/baghera
• PyGNA. A python package for Geneset Network Analysis. https://github.com/stracquadaniolab/pygna

Teaching

• 2023 & 2024 - Instructor. “BST270, Reproducible Data Science”, Harvard School of Public Health, Boston, US
• 2021 - Invited Instructor “Primer on Bayesian modelling”, SynBio Society, Edinburgh, UK.
• 2019 & 2020 – Tutor on Next Generation Sequencing for “Tools for Synthetic Biology”. The University of Edinburgh.
• 2019 & 2021 – Invited instructor. “Analytics & Data Science Summer School 2019”.
  IADS, University of Essex, UK. Course title: “Learning from Small Data”
• 2018 – Teaching and Lab Assistant at the University of Essex for the PGR introductory course “CSEE Bootcamp”.
  Fundamentals of data analysis and research methods.
• 2017 - 2018 – Graduate Laboratory Assistant at the University of Essex, for the following courses:
  • “Data Science and Decision Making”
  • “Foundations of Electronics II”
  • “Introduction to Databases”
  • “Introduction to programming”

Selected awards

• 2019 – James Rennie Bequest. Travel award. The Biology of Genomes, Cold Spring Harbor Laboratory, US.
• 2019 - NVidia Data science GPU program. Nominated research software engineering developer.
• 2016 – Erasmus+ scholarship. Six months traineeship at the University of Essex.

Memberships of professional bodies

Italian State Exam for Qualified Information Engineer, 2018

Languages

• English, Proficient.
• German, Intermediate
• French, Spanish Basics
• Italian, native speaker