DTreePred: an online viewer based on machine learning for pathogenicity prediction of genomic variants
DTreePred offers a robust solution for reducing uncertainty in clinical decision-making regarding pathogenic variants. Improving the accuracy of pathogenicity assessments has the potential to significantly increase the precision of medical diagnoses and treatments, particularly for underrepresented populations.
Authors
- Tirzah Braz Petta Lajus (ORCID: https://orcid.org/0000-0003-0890-9430)
- Beatriz Stransky (ORCID: https://orcid.org/0000-0003-4506-393X)
- Inácio Gomes Medeiros (ORCID: https://orcid.org/0000-0002-3407-218X)
- Daniel H. F. Gomes (ORCID: https://orcid.org/0009-0005-3021-5277)
- Jorge Estefano Santana de Souza (ORCID: https://orcid.org/0000-0003-2347-042X)
Institutions
- University of Southern California (US)
- Instituto Federal do Rio Grande do Norte (BR)
- Universidade Federal do Rio Grande do Norte (BR)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-04-09
- DOI
- https://doi.org/10.1186/s12859-025-06113-4
- Citations
- 2
- Primary Topic
- Genomics and Rare Diseases
- Type
- article
- Field-Weighted Citation Impact
- 3.63
Funders
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Universidade Federal do Rio Grande do Norte