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Machine learning-ba...
Machine learning-based monosaccharide profiling for tissue-specific classification of Wolfiporia extensa samples
- Article/chapterEnglish2023
Publisher, publication year, extent ...
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Elsevier,2023
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printrdacarrier
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LIBRIS-ID:oai:DiVA.org:kth-335226
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https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-335226URI
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https://doi.org/10.1016/j.carbpol.2023.121338DOI
Supplementary language notes
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Language:English
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Summary in:English
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Subject category:ref swepub-contenttype
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Subject category:art swepub-publicationtype
Notes
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QC 20230904
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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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Deng, Shun-Xin
(author)
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Li, Jing
(author)
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Huang, Sheng-Yao
(author)
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Liaw, Chen-Kun
(author)
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Huang, Su-Yun
(author)
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Wang, Ching-Chiung
(author)
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Hsieh, Yves S. Y.KTH,Glykovetenskap(Swepub:kth)u1a596wi
(author)
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KTHGlykovetenskap
(creator_code:org_t)
Related titles
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In:Carbohydrate Polymers: Elsevier3220144-86171879-1344
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