Columbia University: Computational Human High-grade Glioblastoma Multiforme Interactome - miRNA (Post-transcriptional) Layer

The Human High-Grade Glioma Interactome (HGi) contains a genome-wide complement of molecular interactions that are Glioblastoma Multiforme (GBM)-specific. HGi v3 contains the post-transcriptional layer of the HGi, which includes the miRNA-target (RNA-RNA) layer of the interactome.

Read the Abstract

Experimental Approaches

microRNA target predictions were obtained using a two-step machine learning approach. First, sites predicted using miRanda, PITA and TargetScan were scored by classifying sites against a gold standard of validated interactions using a Support Vector Machine (SVM). The SVM is trained on features including the normalized score from the predicting algorithm, conservation across mammalian genomes, and site location relative to the start and end positions of the 3’ UTR. Then co-expression, site scores, and modular site grammar were used to predict interactions with SVM. Features and parameters were selected using cross validation and produced high confidence predictions after retraining the SVM on the complete dataset.

Read the detailed Experimental Approaches

If you cannot access the manuscript, or if you have additional questions, please email Kenneth Smith

Data

Access the CTD2 Data Portal.

Last updated: July 01, 2017