Cargando…
Simulation of undiagnosed patients with novel genetic conditions
Rare Mendelian disorders pose a major diagnostic challenge and collectively affect 300–400 million patients worldwide. Many automated tools aim to uncover causal genes in patients with suspected genetic disorders, but evaluation of these tools is limited due to the lack of comprehensive benchmark da...
Autores principales: | Alsentzer, Emily, Finlayson, Samuel G., Li, Michelle M., Kobren, Shilpa N., Kohane, Isaac S. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570269/ https://www.ncbi.nlm.nih.gov/pubmed/37828001 http://dx.doi.org/10.1038/s41467-023-41980-6 |
Ejemplares similares
-
Machine Learning of Patient Characteristics to Predict Admission Outcomes in the Undiagnosed Diseases Network
por: Amiri, Hadi, et al.
Publicado: (2021) -
Commonalities across computational workflows for uncovering explanatory variants in undiagnosed cases
por: Kobren, Shilpa Nadimpalli, et al.
Publicado: (2021) -
Systematic domain-based aggregation of protein structures highlights DNA-, RNA- and other ligand-binding positions
por: Kobren, Shilpa Nadimpalli, et al.
Publicado: (2019) -
Clinical application of exome sequencing in undiagnosed genetic conditions
por: Need, Anna C, et al.
Publicado: (2012) -
PertInInt: An Integrative, Analytical Approach to Rapidly Uncover Cancer Driver Genes with Perturbed Interactions and Functionalities
por: Kobren, Shilpa Nadimpalli, et al.
Publicado: (2020)