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Federica De Paoli

dblp:409/6403 · DBLP profile ↗
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3ranked-venue papers
2as first author
3since 2021 · last 2025
0000-0002-8027-5666ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 3 · 2 first-author · 3 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 81% Medical and health informatics · 19%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › clinical bioinformatics
genetic variant interpretation
0.812024
VarChat: the generative AI assistant for the interpretation of human genomic variations · Bioinform. 2024
Medical and health informatics
clinical genomics
0.212024
VarChat: the generative AI assistant for the interpretation of human genomic variations · Bioinform. 2024
Bioinformatics and computational biology
genetic variation
0.212024
VarChat: the generative AI assistant for the interpretation of human genomic variations · Bioinform. 2024

Methods — techniques the papers use, named apart from their topics

large language model · 0.8generative AI · 0.8
YearPublicationVenuePosition
2025 AI Models Predicting Methylation Status from DNA Sequence: What is Missing?
Andrei Stefan Blindu, Silvia Berardelli, Federica De Paoli, Rossella Tricarico, Susanna Zucca, Paolo Magni
AIME (2)3
2025 Generative AI Meets Genomics: VarChat, a RAG-Based Approach for Literature-Driven Variant Summarization
Federica De Paoli, Silvia Berardelli, Alessia Tudisco, Andrei Blindu, Enea Parimbelli, Susanna Zucca
AIME (2)1
2024 VarChat: the generative AI assistant for the interpretation of human genomic variations
abstract
MOTIVATION: In the modern era of genomic research, the scientific community is witnessing an explosive growth in the volume of published findings. While this abundance of data offers invaluable insights, it also places a pressing responsibility on genetic professionals and researchers to stay informed about the latest findings and their clinical significance. Genomic variant interpretation is currently facing a challenge in identifying the most up-to-date and relevant scientific papers, while also extracting meaningful information to accelerate the process from clinical assessment to reporting. Computer-aided literature search and summarization can play a pivotal role in this context. By synthesizing complex genomic findings into concise, interpretable summaries, this approach facilitates the translation of extensive genomic datasets into clinically relevant insights. RESULTS: To bridge this gap, we present VarChat (varchat.engenome.com), an innovative tool based on generative AI, developed to find and summarize the fragmented scientific literature associated with genomic variants into brief yet informative texts. VarChat provides users with a concise description of specific genetic variants, detailing their impact on related proteins and possible effects on human health. In addition, VarChat offers direct links to related scientific trustable sources, and encourages deeper research. AVAILABILITY AND IMPLEMENTATION: varchat.engenome.com.
Federica De Paoli, Silvia Berardelli, Ivan Limongelli, Ettore Rizzo, Susanna Zucca
Bioinform.1