Additional file 1: Figure S1. of DectICO: an alignment-free supervised metagenomic classification method based on feature extraction and dynamic selection

Abstract

Comparisons of classification performance between DectICO and the unsupervised alignment-free metagenomic clustering methods. Table S2. The information of the three collections of metagenomes. Table S3. The sizes of the training and testing sets for three collections of metagenomes used in our stability test and generality test. Figure S2. Comparisons of classification performances between DectICO and the non-dynamic feature-selection-based method on the T2D dataset. Figure S3. Comparisons of classification performances between DectICO and RSVM that based on the ICO on the asthma and T2D datasets. Table S4. Comparisons of the stability (from stability test) and generality (from generality test) between DectICO and the RSVM that with the ICO on asthma dataset. Table S5. Comparisons of the stability (from stability test) and generality (from generality test) between DectICO and the RSVM that with the ICO on T2D dataset. Table S6. The runtime of calculation for the feature vectors of ICO based on three kinds of metagenomic samples and 1 Mbp contig. Table S7. The consumed RAM of calculation for the feature vectors of ICO based on three kinds of metagenomic samples. Table S8. The runtime of classification process with varying rounds of feature selection and different numbers of samples in training set on the T2D metagenomes. Table S9. The consumed RAM of classification process with varying rounds of feature selection and different numbers of samples in training set on the T2D metagenomes. Table S10. The ICO vector dimension for different length oligonucleotides. (DOCX 76 kb

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