ClustAll: An R package for patient stratification in complex diseases
Year: 2024
Type: article
Abstract: In the era of precision medicine, it is necessary to understand heterogeneity among patients with complex diseases to improve personalized prevention and management strategies. Here, we introduce ClustAll, a Bioconductor package designed for unsupervised patient stratification using clinical data. ClustAll is based on the previously validated methodology ClustAll, a clustering framework that effectively handles intricacies in clinical data, including mixed data types, missing values, and collinearity. Additionally, ClustAll stands out in its ability to identify multiple patient stratifications within the same population while ensuring their robustness. The updated implementation of ClustAll features S4 classes, parallel computing for enhanced computational efficiency, and user-friendly tools for exploring and comparing stratifications against clinical phenotypes. The performance of ClustAll has been validated using two public clinical datasets, confirming its effectiveness in patient stratification and highlighting its potential impact on clinical management. In summary, ClustAll is a powerful tool for patient stratification in personalized medicine.
Source: PLoS Computational Biology
Institutions: Universidad Pública de Navarra (UPNA), Navarrabiomed, Ilia State University, King Abdullah University of Science and Technology, Karolinska Institutet, Inserm, Université Paris Cité, Hôpital Beaujon, Assistance Publique – Hôpitaux de Paris, Centre de Recherche sur l'Inflammation, Navarre Institute of Health Research, Centro de Investigación Médica Aplicada, Universidad de Navarra, Karolinska University Hospital
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,926
Awards: DECOMPENSATED CIRRHOSIS: IDENTIFICATION OF NEW COMBINATORIAL THERAPIES BASED ON SYSTEMS APPROACHES, TimE frame ControL of Inflammation by canonical and non-canonical autophagy to Prevent fatty liver diSease progrEssion, G434337789, Deciphering HEArt-liVEr commuNication in nonalcohoLic fattY liver disease, Université de Paris
OpenAlex ID: W4405363159