👩🏻💻 MSc Student in Bioinformatics at University of Bologna
🎓 BSc in Biological Sciences at University of Ferrara
I focus on developing computational approaches for biological data analysis, combining algorithmic methods with molecular insight.
Currently exploring how machine learning and genomic technologies can be integrated into reproducible pipelines for data-driven research.
- Machine Learning & Predictive Modeling in Biology
- Genomics and Epigenomics
- Biomedical Data Pipelines and Workflow Automation
- In Silico Biology and Systems Thinking
- Molecular Network Analysis
- Data-Driven Discovery and Model Interpretation
- Programming & Tools: Python, R, Bash/Linux, Git/GitHub, Conda
- Libraries: NumPy, Pandas, Matplotlib, scikit-learn, Biopython
- Bioinformatics: HMMER, BLAST+, CD-HIT, InterProScan
- Epigenomics: minfi, methylKit
- Machine Learning: SVM, Random Forest, Logistic Regression, PCA, MCC, ROC/AUC
- Systems Biology: Cytoscape, STRING, KEGG, Reactome
- HPC & Pipelines: Workflow automation, parallel computing, job scheduling
- Wet Lab: Molecular biology foundation; experience with Western blot, immunofluorescence, microscopy
Design of a structure-guided Hidden Markov Model for the Kunitz-type protease inhibitor domain, integrating PDBeFold alignments, redundancy reduction (CD-HIT), and binary cross-validation with HMMER.
A comprehensive multi-omics study on the Alpine marmot (Marmota marmota), integrating genomic, transcriptomic, and epigenomic layers to explore environmental adaptation mechanisms.
A methylation-analysis pipeline in R for Illumina 450K data: preprocessing, QC, normalization, PCA, and DMP detection between control and disease.
