Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloIntegration of Machine Learning Methods to Dissect Genetically Imputed Transcriptomic Profiles in Alzheimer's Disease
Anno di pubblicazione2019
Formato-
Autore/iCarlo Maj1*+, Tiago Azevedo2+, Valentina Giansanti3+, Oleg Borisov1, Giovanna Maria Dimitri2, Simeon Spasov2, Alzheimer's Disease Neuroimaging Initiative, Pietro Lió2* and Ivan Merelli3*
Affiliazioni autori1Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany 2Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom 3National Research Council, Institute for Biomedical Technologies, Milan, Italy
Autori CNR e affiliazioni
  • VALENTINA GIANSANTI
  • IVAN MERELLI
Lingua/e
  • inglese
AbstractThe genetic component of many common traits is associated with the gene expression and several variants act as expression quantitative loci, regulating the gene expression in a tissue specific manner. In this work, we applied tissue-specific cis-eQTL gene expression prediction models on the genotype of 808 samples including controls, subjects with mild cognitive impairment, and patients with Alzheimer's Disease. We then dissected the imputed transcriptomic profiles by means of different unsupervised and supervised machine learning approaches to identify potential biological associations. Our analysis suggests that unsupervised and supervised methods can provide complementary information, which can be integrated for a better characterization of the underlying biological system. In particular, a variational autoencoder representation of the transcriptomic profiles, followed by a support vector machine classification, has been used for tissue-specific gene prioritizations. Interestingly, the achieved gene prioritizations can be efficiently integrated as a feature selection step for improving the accuracy of deep learning classifier networks. The identified gene-tissue information suggests a potential role for inflammatory and regulatory processes in gut-brain axis related tissues. In line with the expected low heritability that can be apportioned to eQTL variants, we were able to achieve only relatively low prediction capability with deep learning classification models. However, our analysis revealed that the classification power strongly depends on the network structure, with recurrent neural networks being the best performing network class. Interestingly, cross-tissue analysis suggests a potentially greater role of models trained in brain tissues also by considering dementia-related endophenotypes. Overall, the present analysis suggests that the combination of supervised and unsupervised machine learning techniques can be used for the evaluation of high dimensional omics data.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da-
Pagine a-
Pagine totali16
RivistaFrontiers in genetics
Attiva dal 2010
Editore: Frontiers Research Foundation, - Lausanne
Paese di pubblicazione: Svizzera
Lingua: inglese
ISSN: 1664-8021
Titolo chiave: Frontiers in genetics
Titolo proprio: Frontiers in genetics
Titolo abbreviato: Front. genet.
Numero volume della rivista10
Fascicolo della rivista-
DOI10.3389/fgene.2019.00726
Verificato da referee-
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000483568600001)
Parole chiaveeQTL, gene expression imputation, GTEx, variational autoencoder, support vector machine, deep learning, recurrent neural networks, Alzheimer's
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Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • ITB — Istituto di tecnologie biomediche
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
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