“… Wheeler N. E, et al | 2018 | Classification | TD, RF | Q1, Q3, Q4 | Genomics, Transcriptomics | 100% out-of-bag classification accuracy | out-of-bag classification accuracy | atypical mutations in protein coding genes | 29,760,095 [22] | Predictive model based on machine learning algorithms to reliably determine malaria infection status in humans based on volatile biomarkers | De Moraes CM, et al | 2018 | Prediction, Classification | RF, RRF, AdaBoost | Q1, Q2, Q3, Q5, Q6 | Proteomics, Metabolomics | 0.95, 80, 92 | 10 Fold cross-validation | 17 (4-hydroxy-4-methylpentan-2-one), multiple compounds (compound 49 , 31, 61, 5, 9, 14, 20, 38) |
30,416,498 [34] | Development of an in silico method to predict whether a protein is an effector of type IV secretion system or not based on its sequence information. | Xiong Y, et al | 2018 | Prediction, Classification | NB, KNN, LR, ERT, GBM, XGB, SVM, RF, MC-SGE | Q1, Q3 | Transcriptomics | 73.2, 85.5, 87.9, 89.4, 90.5, 90.1, 90.2, 88.5, metric of F1 | 5-fold cross-validation, independent test for testing the generalization ability | PSSM-composition features |
30,682,021 [49] | This study focuses on the best way to use validated effector protein features for effector prediction using three machine learning classifiers, and compares results with those of others to obtain de novo results | Esna Ashari Z, et al | 2019 | Classification, Prediction, Clustering | SVM, E-SVM | Q2, Q3, Q4, Q5 | Transcriptomics, Proteomics | 94.05%, 93.64%, and 92.44%, for Models 1, 2, and 3, respectively. | 10 fold cross-validation | Optimal feature set includes 15 features (i.e, coiled coil domains, hydropath, PSSM composites) |
31,146,762 [23] | Enabling rapid assessment of mosquito blood-feeding histories and vectorial capacities using Mid-infrared spectroscopy and supervised machine learning . |
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