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  • Niemi, MEK, et al. (creator_code:aut_t)
  • 2021
  • swepub:Mat__t
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  • Han, YH, et al. (creator_code:aut_t)
  • Multitrait genome-wide analyses identify new susceptibility loci and candidate drugs to primary sclerosing cholangitis
  • 2023
  • record:In_t: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 14:1, s. 1069-
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Primary sclerosing cholangitis (PSC) is a rare autoimmune bile duct disease that is strongly associated with immune-mediated disorders. In this study, we implemented multitrait joint analyses to genome-wide association summary statistics of PSC and numerous clinical and epidemiological traits to estimate the genetic contribution of each trait and genetic correlations between traits and to identify new lead PSC risk-associated loci. We identified seven new loci that have not been previously reported and one new independent lead variant in the previously reported locus. Functional annotation and fine-mapping nominated several potential susceptibility genes such as MANBA and IRF5. Network-based in silico drug efficacy screening provided candidate agents for further study of pharmacological effect in PSC.
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  • Kang, JS, et al. (creator_code:aut_t)
  • Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning
  • 2020
  • record:In_t: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1, s. 20140-
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Most models for predicting malignant pancreatic intraductal papillary mucinous neoplasms were developed based on logistic regression (LR) analysis. Our study aimed to develop risk prediction models using machine learning (ML) and LR techniques and compare their performances. This was a multinational, multi-institutional, retrospective study. Clinical variables including age, sex, main duct diameter, cyst size, mural nodule, and tumour location were factors considered for model development (MD). After the division into a MD set and a test set (2:1), the best ML and LR models were developed by training with the MD set using a tenfold cross validation. The test area under the receiver operating curves (AUCs) of the two models were calculated using an independent test set. A total of 3,708 patients were included. The stacked ensemble algorithm in the ML model and variable combinations containing all variables in the LR model were the most chosen during 200 repetitions. After 200 repetitions, the mean AUCs of the ML and LR models were comparable (0.725 vs. 0.725). The performances of the ML and LR models were comparable. The LR model was more practical than ML counterpart, because of its convenience in clinical use and simple interpretability.
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