Authors:
Marco Calderisi
1
;
Ilaria Ceppa
1
;
Denise Cassandrini
2
;
Rosanna Trovato
2
;
Giulia Bertocci
2
;
Alessandro Tonacci
3
;
Guja Astrea
2
;
Raffaele Conte
3
and
Filippo M. Santorelli
2
Affiliations:
1
Kode Solutions, Pisa and Italy
;
2
IRCCS Fondazione Stella Maris, Pisa and Italy
;
3
IFC-CNR, Pisa and Italy
Keyword(s):
Congenital Myopathies, Muscular Dystrophies, Gene Sequencing, Non Metric Multidimensional Scaling, Clustering, High Throughput Data Analysis.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Knowledge-Based Systems
;
Pattern Recognition and Machine Learning
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
The boundaries between congenital myopathies and muscular dystrophies and other neuromuscular disorders are becoming blurred because of the significant overlap in disease genes, clinical presentations, and histopathological features. Using a MotorPlex7.0 gene panel in massive sequencing, we define disease causative mutations in 76% of our sample. We then analysed the extent of gene information in the data using non metric multidimensional scaling (nMDS), a well-known algorithm for multivariate analysis, and clustering techniques. To perform this analysis, we developed a software that allows for an interactive exploration of the variants dataset and of the results of the nMDS model. Using these techniques, we were able to quickly study a dataset consisting of thousands of variants, identifying groupings of patients based on the presence or absence of specific sets of mutations.