CV

Education

present, 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

• 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
• Fanfani, V. Citi, L., Harris, AL, Pezzella, F. and Stracquadanio, G. ‘Gene-level heritability analysis explains the polygenic architecture of cancer’. bioRxiv, 2019, doi: 10.1101/599753

Conferences

• 2021, Network Biology virtual meeting, CSHL, US. Poster Presentation: “Interpretable graph neural networks unveil system‑levelreprogramming 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”

Recent Talks

• 2019 – “Cancer heritability, decoding the contribution of high frequency variants to cancer risk”. SynthSys, University of Edinburgh

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
  • Scientific packages: numpy, scipy, pymc, pytorch, networkx
  • Workflow systems : snakemake, nextflow
  • Data visualisation, Python and R
• Other: Git, Latex, Photoshop, Illustrator

Teaching

• 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