Additional file 1 of Imbalance learning for the prediction of N6-Methylation sites in mRNAs

Data set of human mature mRNA N6-Methylation. Training and testing data used in this paper is accessible in this file. For each sample, the transcript id, site position, transcript length and flanking sequence with a size of 26 nts are given. (XLSX 15155 kb

Extra spuisluis in de Afsluitdijk: Effect op onderhoud havens

Datasets for constructing the miRNA-disease association prediction models. Four datasets such as three positive sample sets “positive_miR”, “positive_HMDD” and “positive_miRcancer” and the negative sample set “negative_expression” are stored. The three positive sample sets are retrieved from the three existing databases such as miR2Disease, HMDD v2 and miRCancer, while the negative sample set was obtained via analyzing the expression of the miRNAs.This file can also be downloaded from: https://drive.google.com/open?id=0B6lH3mKdA9CSTkg2OVBPS0ZfVnM . (XLS 947 kb

Additional file 2 of Imbalance learning for the prediction of N6-Methylation sites in mRNAs

Supplementary Tables, Algorithm and Figure. Table S1: The result of Fisher’s exact test on training data. The SNP variant states of positive and negative samples are counted respectively at all positions in window sequence. The P-value is computed with Fisher’s exact function from Python scipy package. Table S2: Complete SNP specificity ranking for all positions. Table S3: The feature distribution in HMpre feature space. Algorithm S1: SNP Specificity Identification Algorithm. Figure S1: Distribution of feature importance scores in XGBoost Classifier learning stage. (PDF 323 kb