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Sökning: WFRF:(Deng Shun Xin)

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1.
  • 2019
  • Tidskriftsartikel (refereegranskat)
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2.
  • Chen, Jun-Tong, et al. (författare)
  • Corydalis sunhangii (Papaveraceae) : A new species from Xizang, China, based on plastome and morphological data
  • 2024
  • Ingår i: Ecology and Evolution. - : John Wiley & Sons. - 2045-7758. ; 14:4
  • Tidskriftsartikel (refereegranskat)abstract
    • A new species of Papaveraceae, Corydalis sunhangii, in the section Trachycarpae, is described and illustrated from Nyingchi City, Xizang, China. The new species has some resemblance to Corydalis kingdonis, but differs by radical leaves prominent, usually several, blade tripinnate (vs. insignificant, few, blade bi- to triternate); cauline leaf usually one, much smaller than radical leaves, usually situated in lower half of stem (vs. usually two, larger than radical leaves, concentrated in upper third of stem); racemes densely 13-35-flowered (vs. rather lax, 4-11-flowered); claw of lower petal shallowly saccate (vs. very prominently and deeply saccate); capsule oblong, with raised lines of dense papillae (vs. broadly obovoid, smooth). Phylogenetic analysis, based on 68 protein-coding plastid genes of 49 samples, shows that C. sunhangii is not closely related to any hitherto described species, which is consistent with our morphological analysis.
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3.
  • Hsiung, Shih-Yi, et al. (författare)
  • Machine learning-based monosaccharide profiling for tissue-specific classification of Wolfiporia extensa samples
  • 2023
  • Ingår i: Carbohydrate Polymers. - : Elsevier. - 0144-8617 .- 1879-1344. ; 322
  • Tidskriftsartikel (refereegranskat)abstract
    • Machine learning (ML) has been used for many clinical decision-making processes and diagnostic procedures in bioinformatics applications. We examined eight algorithms, including linear discriminant analysis (LDA), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), Naïve Bayes classifier (NB), and artificial neural network (ANN) models, to evaluate their classification and prediction capabilities for four tissue types in Wolfiporia extensa using their monosaccharide composition profiles. All 8 ML-based models were assessed as exemplary models with AUC exceeding 0.8. Five models, namely LDA, KNN, RF, GBM, and ANN, performed excellently in the four-tissue-type classification (AUC > 0.9). Additionally, all eight models were evaluated as good predictive models with AUC value >0.8 in the three-tissue-type classification. Notably, all 8 ML-based methods outperformed the single linear discriminant analysis (LDA) plotting method. For large sample sizes, the ML-based methods perform better than traditional regression techniques and could potentially increase the accuracy in identifying tissue samples of W. extensa.
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  • Resultat 1-3 av 3

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