FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4366998421> ?p ?o ?g. }

• W4366998421 abstract "ABSTRACT Background Amyotrophic lateral sclerosis (ALS) displays considerable clinical, genetic and molecular heterogeneity. Machine learning approaches have shown potential to disentangle complex disease landscapes and they have been utilised for patient stratification in ALS. However, lack of independent validation in different populations and in pre-mortem tissue samples have greatly limited their use in clinical and research settings. We overcame such issues by performing a large-scale study of over 600 post-mortem brain and blood samples of people with ALS from four independent datasets from the UK, Italy, the Netherlands and the US. Methods Hierarchical clustering was performed on the 5000 most variably expressed autosomal genes identified from post-mortem motor cortex expression data of people with sporadic ALS from the KCL BrainBank (N=112). The molecular architectures of each cluster were investigated with gene enrichment, network and cell composition analysis. Methylation and genetic data were also used to assess if other omics measures differed between individuals. Validation of these clusters was achieved by applying linear discriminant analysis models based on the KCL BrainBank to the TargetALS US motor cortex (N=93), as well as Italian (N=15) and Dutch (N=397) blood expression datasets. Phenotype analysis was also performed to assess cluster-specific differences in clinical outcomes. Results We identified three molecular phenotypes, which reflect the proposed major mechanisms of ALS pathogenesis: synaptic and neuropeptide signalling, excitotoxicity and oxidative stress, and neuroinflammation. Known ALS risk genes were identified among the informative genes of each cluster, suggesting potential for genetic profiling of the molecular phenotypes. Cell types which are known to be associated with specific molecular phenotypes were found in higher proportions in those clusters. These molecular phenotypes were validated in independent motor cortex and blood datasets. Phenotype analysis identified distinct cluster-related outcomes associated with progression, survival and age of death. We developed a public webserver ( https://alsgeclustering.er.kcl.ac.uk ) that allows users to stratify samples with our model by uploading their expression data. Conclusions We have identified three molecular phenotypes, driven by different cell types, which reflect the proposed major mechanisms of ALS pathogenesis. Our results support the hypothesis of biological heterogeneity in ALS where different mechanisms underly ALS pathogenesis in a subgroup of patients that can be identified by a specific expression signature. These molecular phenotypes show potential for stratification of clinical trials, the development of biomarkers and personalised treatment approaches." @default.
• W4366998421 created "2023-04-27" @default.
• W4366998421 creator A5009252445 @default.
• W4366998421 creator A5013893534 @default.
• W4366998421 creator A5017913957 @default.
• W4366998421 creator A5020621452 @default.
• W4366998421 creator A5026567185 @default.
• W4366998421 creator A5028349382 @default.
• W4366998421 creator A5029095625 @default.
• W4366998421 creator A5033154792 @default.
• W4366998421 creator A5035602637 @default.
• W4366998421 creator A5037656140 @default.
• W4366998421 creator A5037963150 @default.
• W4366998421 creator A5061510230 @default.
• W4366998421 creator A5061853919 @default.
• W4366998421 date "2023-04-25" @default.
• W4366998421 modified "2023-09-29" @default.
• W4366998421 title "Unsupervised machine learning identifies distinct molecular and phenotypic ALS subtypes in post-mortem motor cortex and blood expression data" @default.
• W4366998421 cites W1978688182 @default.
• W4366998421 cites W1981492923 @default.
• W4366998421 cites W1999393328 @default.
• W4366998421 cites W2008213051 @default.
• W4366998421 cites W2028790868 @default.
• W4366998421 cites W2038606974 @default.
• W4366998421 cites W2049958331 @default.
• W4366998421 cites W2060230550 @default.
• W4366998421 cites W2060799374 @default.
• W4366998421 cites W2081863795 @default.
• W4366998421 cites W2108637424 @default.
• W4366998421 cites W2128297440 @default.
• W4366998421 cites W2136102567 @default.
• W4366998421 cites W2169456326 @default.
• W4366998421 cites W2179438025 @default.
• W4366998421 cites W2478380847 @default.
• W4366998421 cites W2503929286 @default.
• W4366998421 cites W2586670522 @default.
• W4366998421 cites W2596238655 @default.
• W4366998421 cites W2608631762 @default.
• W4366998421 cites W2799345117 @default.
• W4366998421 cites W2806989085 @default.
• W4366998421 cites W2809921225 @default.
• W4366998421 cites W2885507494 @default.
• W4366998421 cites W2893037011 @default.
• W4366998421 cites W2896958428 @default.
• W4366998421 cites W2901355602 @default.
• W4366998421 cites W2914760194 @default.
• W4366998421 cites W2945263195 @default.
• W4366998421 cites W2953216035 @default.
• W4366998421 cites W2960606948 @default.
• W4366998421 cites W2972830687 @default.
• W4366998421 cites W2982641702 @default.
• W4366998421 cites W2991360048 @default.
• W4366998421 cites W2999628275 @default.
• W4366998421 cites W3000347769 @default.
• W4366998421 cites W3010358256 @default.
• W4366998421 cites W3023397682 @default.
• W4366998421 cites W3042631053 @default.
• W4366998421 cites W3043193619 @default.
• W4366998421 cites W3043607727 @default.
• W4366998421 cites W3047170019 @default.
• W4366998421 cites W3047765583 @default.
• W4366998421 cites W3085262448 @default.
• W4366998421 cites W3111314674 @default.
• W4366998421 cites W3112635102 @default.
• W4366998421 cites W3157631221 @default.
• W4366998421 cites W3177218861 @default.
• W4366998421 cites W3185058907 @default.
• W4366998421 cites W3197268625 @default.
• W4366998421 cites W3207362495 @default.
• W4366998421 cites W4210521274 @default.
• W4366998421 cites W4220712320 @default.
• W4366998421 cites W4226286160 @default.
• W4366998421 cites W4243198840 @default.
• W4366998421 cites W4252979096 @default.
• W4366998421 cites W4293522954 @default.
• W4366998421 cites W4297264803 @default.
• W4366998421 cites W4309076667 @default.
• W4366998421 cites W4311617932 @default.
• W4366998421 cites W4313640271 @default.
• W4366998421 cites W4323533778 @default.
• W4366998421 doi "https://doi.org/10.1101/2023.04.21.23288942" @default.
• W4366998421 hasPublicationYear "2023" @default.
• W4366998421 type Work @default.
• W4366998421 citedByCount "1" @default.
• W4366998421 countsByYear W43669984212023 @default.
• W4366998421 crossrefType "posted-content" @default.
• W4366998421 hasAuthorship W4366998421A5009252445 @default.
• W4366998421 hasAuthorship W4366998421A5013893534 @default.
• W4366998421 hasAuthorship W4366998421A5017913957 @default.
• W4366998421 hasAuthorship W4366998421A5020621452 @default.
• W4366998421 hasAuthorship W4366998421A5026567185 @default.
• W4366998421 hasAuthorship W4366998421A5028349382 @default.
• W4366998421 hasAuthorship W4366998421A5029095625 @default.
• W4366998421 hasAuthorship W4366998421A5033154792 @default.
• W4366998421 hasAuthorship W4366998421A5035602637 @default.
• W4366998421 hasAuthorship W4366998421A5037656140 @default.
• W4366998421 hasAuthorship W4366998421A5037963150 @default.
• W4366998421 hasAuthorship W4366998421A5061510230 @default.
• W4366998421 hasAuthorship W4366998421A5061853919 @default.
• W4366998421 hasBestOaLocation W43669984211 @default.

• next