Mining clinical and laboratory data of neurodegenerative diseases by Machine Learning: Transcriptomic biomarkers

Ivan Arisi, Mara Dronofrio, Rossella Brandi, Michele Sonnessa, Alessandra Campanelli, Rita Florio, Valentina Sposato, Francesca Malerba, Antonino Cattaneo, Patrizia Mecocci, Giuseppe Bruno, Marco Canevelli, Magda Tsolaki, Natalia Pelteki, Fabrizio Stocchi, Laura Vacca, Giulia Fiscon, Paola Bertolazzi

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Low sensitivity and specificity of current diagnostic methodologies lead to frequent misdiagnosis of Alzheimer's and other dementia, causing an extra economic and social burden. We aim to compare real word data with the largest public databases, to extract new diagnostic models for an earlier and more accurate diagnosis of cognitive impairment. We analyzed both neuropsychological, neurological, physical assessments and transcriptomic data from biosamples. We used Machine Learning approaches and biostatistical methods to analyze the transcriptome from the large-scale ADNI and AddNeuroMed international projects: we selected some genes as potential transcriptomic biomarkers and highlighted affected cellular processes. Furthermore the analysis, by machine learning, of real-world data provided by European clinical dementia centres, resulted in a small subset of comorbidities able to discriminate diagnostic classes with a good classifier performance.
Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2735-2737
ISBN (Electronic)9781538654880
DOIs
Publication statusPublished - 21 Jan 2019
Externally publishedYes
Event2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
Duration: 3 Dec 20186 Dec 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
CountrySpain
CityMadrid
Period03/12/201806/12/2018

Cite this

Arisi, I., Dronofrio, M., Brandi, R., Sonnessa, M., Campanelli, A., Florio, R., ... Bertolazzi, P. (2019). Mining clinical and laboratory data of neurodegenerative diseases by Machine Learning: Transcriptomic biomarkers. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, ... L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 2735-2737). (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621072