loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

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.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 44.222.196.236

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Calderisi, M.; Ceppa, I.; Cassandrini, D.; Trovato, R.; Bertocci, G.; Tonacci, A.; Astrea, G.; Conte, R. and Santorelli, F. (2019). A Novel Approach to Gene Analysis: Gene Panels and Cluster Definition to Assist Genotyping Patients with Congenital Myopathies. In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - HEALTHINF; ISBN 978-989-758-353-7; ISSN 2184-4305, SciTePress, pages 345-352. DOI: 10.5220/0007396003450352

@conference{healthinf19,
author={Marco Calderisi. and Ilaria Ceppa. and Denise Cassandrini. and Rosanna Trovato. and Giulia Bertocci. and Alessandro Tonacci. and Guja Astrea. and Raffaele Conte. and Filippo M. Santorelli.},
title={A Novel Approach to Gene Analysis: Gene Panels and Cluster Definition to Assist Genotyping Patients with Congenital Myopathies},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - HEALTHINF},
year={2019},
pages={345-352},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007396003450352},
isbn={978-989-758-353-7},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - HEALTHINF
TI - A Novel Approach to Gene Analysis: Gene Panels and Cluster Definition to Assist Genotyping Patients with Congenital Myopathies
SN - 978-989-758-353-7
IS - 2184-4305
AU - Calderisi, M.
AU - Ceppa, I.
AU - Cassandrini, D.
AU - Trovato, R.
AU - Bertocci, G.
AU - Tonacci, A.
AU - Astrea, G.
AU - Conte, R.
AU - Santorelli, F.
PY - 2019
SP - 345
EP - 352
DO - 10.5220/0007396003450352
PB - SciTePress