Staff

Arianna Consiglio

Ricercatore
E-Mail

arianna.consiglio@cnr.it

PHONE

+390805929667

LOCATION

Bari

ROOM (floor/number)

512

I graduated in Computer Science in 2006 at the University of Bari Aldo Moro with a thesis on Computational Intelligence. From 2007 to 2009, I worked on the development of predictive computational models for credit risk and operational risk analysis, and in May 2009 I obtained a Master’s degree in Information Systems Governance at Roma Tre University. Since November 2009, I have been working at the Institute for Biomedical Technologies of the CNR in Bari within the Bioinformatics group, and in April 2016 I earned a PhD in Computer Science at the University of Bari with a thesis applying Computational Intelligence methods to Bioinformatics. My expertise includes the design and development of bioinformatics algorithms, workflows, applications, and databases; statistical analysis of bioinformatics data; functional analysis of differential expression from RNA-Seq data generated by Next-Generation Sequencing (NGS) technologies; analysis of structural variations in the genome; analysis and design of CRISPR screens; and the application of Machine Learning and Computational Intelligence methods to experimental data, with a particular interest in Explainable Artificial Intelligence (XAI).

Development of high-throughput CRISPR screening platforms, integrating machine learning-guided library design with large-scale genetic perturbation experiments in different biological contexts

Application of advanced statistical methods, machine learning, deep learning, explainable AI (XAI), and data mining for predicting pathological mechanisms, identifying disease biomarkers and supporting patient stratification.

Development, application and optimization of automated pipelines for the analysis of genomics, epigenomics and transcriptomics data for large-scale studies.

Deployment and application of automated analysis pipelines for the analysis of bulk, single-cell and spatial transcriptomics data.

Characterization of the functional landscape of single nucleotide variants (SNV) in disease models by using large-scale CRISPR screens and quantifying variant effects via phenotypic and transcriptional readouts