TY - JOUR
KW - Computational models
KW - Diseases
AU - Francesca Pistollato
AU - Fabia Furtmann
AU - Lindsay J. Marshall
AU - Surat Parvatam
AU - Jan Turner
AU - Flora Tshinanu Musuamba
AU - Giulia Russo
AU - Francesco Pappalardo
AB - Rare diseases affect over 300 million people worldwide and pose unique research challenges. In silico approaches, such as mechanistic models, machine learning, and simulations, offer scalable tools for disease characterisation, drug discovery, and virtual trials. This review categorises these methods by context of use, critically appraises their strengths and limitations, and identifies barriers to translation, highlighting key opportunities and ongoing challenges in advancing computational strategies for rare disease research.
BT - npj Digital Medicine
DA - 2025-11-17
DO - 10.1038/s41746-025-02068-1
IS - 1
LA - en
N2 - Rare diseases affect over 300 million people worldwide and pose unique research challenges. In silico approaches, such as mechanistic models, machine learning, and simulations, offer scalable tools for disease characterisation, drug discovery, and virtual trials. This review categorises these methods by context of use, critically appraises their strengths and limitations, and identifies barriers to translation, highlighting key opportunities and ongoing challenges in advancing computational strategies for rare disease research.
PY - 2025
EP - 676
ST - Advancing the frontier of rare disease modeling
T2 - npj Digital Medicine
TI - Advancing the frontier of rare disease modeling: a critical appraisal of in silico technologies
UR - https://www.nature.com/articles/s41746-025-02068-1
VL - 8
Y2 - 2026-01-09
SN - 2398-6352
ER -