SwePub
Sök i SwePub databas

  form:Ext_t

Träfflista för sökning "WFRF:(Farmer AJ) "

form:Search_simp_t: WFRF:(Farmer AJ)

  • navigation:Result_t 1-9 navigation:of_t 9
hitlist:Modify_result_t
   
hitlist:Enumeration_thitlist:Reference_thitlist:Reference_picture_thitlist:Find_Mark_t
1.
  • Romagnoni, A, et al. (creator_code:aut_t)
  • Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
  • 2019
  • record:In_t: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
  •  
2.
  •  
3.
  •  
4.
  • de Jong, S, et al. (creator_code:aut_t)
  • Applying polygenic risk scoring for psychiatric disorders to a large family with bipolar disorder and major depressive disorder
  • 2018
  • record:In_t: Communications biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 1, s. 163-
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Psychiatric disorders are thought to have a complex genetic pathology consisting of interplay of common and rare variation. Traditionally, pedigrees are used to shed light on the latter only, while here we discuss the application of polygenic risk scores to also highlight patterns of common genetic risk. We analyze polygenic risk scores for psychiatric disorders in a large pedigree (n ~ 260) in which 30% of family members suffer from major depressive disorder or bipolar disorder. Studying patterns of assortative mating and anticipation, it appears increased polygenic risk is contributed by affected individuals who married into the family, resulting in an increasing genetic risk over generations. This may explain the observation of anticipation in mood disorders, whereby onset is earlier and the severity increases over the generations of a family. Joint analyses of rare and common variation may be a powerful way to understand the familial genetics of psychiatric disorders.
  •  
5.
  •  
6.
  •  
7.
  •  
8.
  • Morgan, AR, et al. (creator_code:aut_t)
  • Inflammatory biomarkers in Alzheimer's disease plasma
  • 2019
  • record:In_t: Alzheimer's & dementia : the journal of the Alzheimer's Association. - : Wiley. - 1552-5279. ; 15:6, s. 776-787
  • swepub:Mat_article_t (swepub:level_refereed_t)
  •  
9.
  •  
Skapa referenser, mejla, bekava och länka
  • navigation:Result_t 1-9 navigation:of_t 9

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt tools:Close_t

tools:Permalink_label_t