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Read our publications and check eVai citations

An AI‑based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases

- Zucca, S., Nicora, G., De Paoli, F., Carta, M.G., Bellazi, R., Magni, P, Rizzo, E., Limongelli, I.

Phenotypic Variation in Two Siblings Affected with Shwachman-Diamond Syndrome: The Use of Expert Variant Interpreter (eVai) Suggests Clinical Relevance of a Variant in the KMT2A Gene

- Taha, I.; De Paoli, F.; Foroni, S.; Zucca, S.; Limongelli, I.; Cipolli, M.; Danesino, C.; Ramenghi, U.; Minelli, A.

A machine learning approach based on ACMG/AMP guidelines for genomic variant classification and prioritization

- Nicora G, Zucca S, Limongelli I, Bellazzi R & Magni P

An automatic implementation of ACMG/ClinGen guidelines for constitutional Copy Number Variants annotation and interpretation

- De Paoli F, Limongelli I, Rizzo E, Nicora G, Magni P.

A comparison of eVai, CADD and VVP variant prediction results on the ICR639 hereditary cancer dataset

- Nicora G, Limongelli I, Zucca S, Santolisier R, Magni P, Bellazzi R.

CardioVAI: An automatic implementation of ACMG‐AMP variant interpretation guidelines in the diagnosis of cardiovascular diseases

- Nicora G, Limongelli I, Gambelli P, Memmi M, Malovini A, Mazzanti A, Napolitano C, Priori S, Bellazzi R.

Variant interpretation supporting genetic diagnosis in exome sequencing NGS data: eVai clinical validation

- Zucca, S.; Limongelli, I.; Valente, M.; Asaro, A.; Garau, J.;Palmieri, I.; Bellazzi, R.; Cereda, C.