Search alternatives:
reading classification » learning classification (Expand Search), crowding classification (Expand Search), rolling classification (Expand Search)
codon optimization » wolf optimization (Expand Search)
based reading » based grading (Expand Search), based reasoning (Expand Search), based learning (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
a codon » _ codon (Expand Search), a common (Expand Search)
reading classification » learning classification (Expand Search), crowding classification (Expand Search), rolling classification (Expand Search)
codon optimization » wolf optimization (Expand Search)
based reading » based grading (Expand Search), based reasoning (Expand Search), based learning (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
a codon » _ codon (Expand Search), a common (Expand Search)
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1
The overview of the proposed method.
Published 2023“…<p>Five main steps, including reading, preprocessing, feature selection, classification, and association rule mining were applied to the mRNA expression data. 1) Required data was collected from the TCGA repository and got organized during the reading step. 2) The pre-processing step includes two sub-steps, nested cross-validation and data normalization. 3) The feature-selection step contains two parts: the filter method based on a t-test and the wrapper method based on binary Non-Dominated Sorting Genetic Algorithm II (NSGAII) for mRNA data, in which candidate mRNAs with more relevance to early-stage and late-stage Papillary Thyroid Cancer (PTC) were selected. 4) Multi-classifier models were utilized to evaluate the discrimination power of the selected mRNAs. 5) The Association Rule Mining method was employed to discover the possible hidden relationship between the selected mRNAs and early and late stages of PTC firstly, and the complex relationship among the selected mRNAs secondly.…”
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2
GSE96058 information.
Published 2024“…</p><p>Results</p><p>In this study, five main steps were followed for the analysis of mRNA expression data: reading, preprocessing, feature selection, classification, and SHAP algorithm. …”
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3
The performance of classifiers.
Published 2024“…</p><p>Results</p><p>In this study, five main steps were followed for the analysis of mRNA expression data: reading, preprocessing, feature selection, classification, and SHAP algorithm. …”
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4
Table_1_Pan-Genomic and Polymorphic Driven Prediction of Antibiotic Resistance in Elizabethkingia.xlsx
Published 2019“…Using core-SNPs and pan-genes in combination with six machine learning (ML) algorithms, binary classification of clindamycin and vancomycin resistance achieved f1 scores of 0.94 and 0.84, respectively. …”
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5
Image_1_Pan-Genomic and Polymorphic Driven Prediction of Antibiotic Resistance in Elizabethkingia.tif
Published 2019“…Using core-SNPs and pan-genes in combination with six machine learning (ML) algorithms, binary classification of clindamycin and vancomycin resistance achieved f1 scores of 0.94 and 0.84, respectively. …”
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6
Image_2_Pan-Genomic and Polymorphic Driven Prediction of Antibiotic Resistance in Elizabethkingia.tif
Published 2019“…Using core-SNPs and pan-genes in combination with six machine learning (ML) algorithms, binary classification of clindamycin and vancomycin resistance achieved f1 scores of 0.94 and 0.84, respectively. …”