Integration of Machine Learning Methods to Dissect Genetically Imputed Transcriptomic Profiles in Alzheimer’s Disease

Maj, Carlo; Azevedo, Tiago; Giansanti, Valentina; Borisov, Oleg; Dimitri, Giovanna Maria; Spasov, S; Initiative, Alzheimer’s Disease Neuroimaging; Lió, Píetro; Merelli, Ivan

doi:10.3389/fgene.2019.00726

Cited by 21 publications

(24 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A different approach was taken in another study, in which gene expression profiles were predicted from ADNI GWAS data and a range of different machine learning algorithms used to identify associations between AD diagnosis and gene expression profiles across different tissues 141 . In this study, the RNN was the most accurate algorithm for distinguishing individuals with AD from healthy individuals.…”

Section: Language Featuresmentioning

confidence: 99%

Applications of machine learning to diagnosis and treatment of neurodegenerative diseases

Myszczynska

Ojamies

Lacoste³

et al. 2020

Nat Rev Neurol

346

182

View full text Add to dashboard Cite

show abstract

Section: Language Featuresmentioning

confidence: 99%

Applications of machine learning to diagnosis and treatment of neurodegenerative diseases

Myszczynska

Ojamies

Lacoste³

et al. 2020

Nat Rev Neurol

346

182

View full text Add to dashboard Cite

show abstract

“…Using a VAE model of the communities, we tested the extent to which they could help to identify expression changes associated with disease and discover biologically-meaningful features 8 , 45 . We built on Tybalt , which uses a Keras implementation.…”

Section: Methodsmentioning

confidence: 99%

Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits

et al. 2021

Self Cite

View full text Add to dashboard Cite

Here, we performed a comprehensive intra-tissue and inter-tissue multilayer network analysis of the human transcriptome. We generated an atlas of communities in gene co-expression networks in 49 tissues (GTEx v8), evaluated their tissue specificity, and investigated their methodological implications. UMAP embeddings of gene expression from the communities (representing nearly 18% of all genes) robustly identified biologically-meaningful clusters. Notably, new gene expression data can be embedded into our algorithmically derived models to accelerate discoveries in high-dimensional molecular datasets and downstream diagnostic or prognostic applications. We demonstrate the generalisability of our approach through systematic testing in external genomic and transcriptomic datasets. Methodologically, prioritisation of the communities in a transcriptome-wide association study of the biomarker C-reactive protein (CRP) in 361,194 individuals in the UK Biobank identified genetically-determined expression changes associated with CRP and led to considerably improved performance. Furthermore, a deep learning framework applied to the communities in nearly 11,000 tumors profiled by The Cancer Genome Atlas across 33 different cancer types learned biologically-meaningful latent spaces, representing metastasis (p < 2.2 × 10−16) and stemness (p < 2.2 × 10−16). Our study provides a rich genomic resource to catalyse research into inter-tissue regulatory mechanisms, and their downstream consequences on human disease.

show abstract

“…Maj et al [ 40 ] proposed an integrated deep learning and machine learning approach to analyze gene expression data in connection with AD cognitive decline. Their integrated approach leverages the concept of variational auto-encoders (see Section 3.2.4 .…”

Section: Research Studies In Genomics On the Prediction Of Ad Using Deep Learningmentioning

confidence: 99%

Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

Lin

Lane

2021

IJMS

View full text Add to dashboard Cite

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

show abstract

Integration of Machine Learning Methods to Dissect Genetically Imputed Transcriptomic Profiles in Alzheimer’s Disease

Cited by 21 publications

References 78 publications

Applications of machine learning to diagnosis and treatment of neurodegenerative diseases

Applications of machine learning to diagnosis and treatment of neurodegenerative diseases

Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits

Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

Contact Info

Product

Resources

About