Prediction of both conserved and nonconserved microRNA targets in animals
By Xiaowei Wang, I. E. Naqa. Bioinform., 2008
565 citations recorded by the source corpus.
Subfields: Cancer Research, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/5c637d1a6877574fd50263d9040104a49210f06c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cancer Research
- Xiaowei Wang
- Jin-Wu Nam
- David P. Bartel
- Murray J. Cairns
- Jiuyong Li
- Bing Liu
- Vikram Agarwal
- George W. Bell
In Molecular Biology
- Xiaowei Wang
- Jin-Wu Nam
- David P. Bartel
- Murray J. Cairns
- Jiuyong Li
- Bing Liu
- Vikram Agarwal
- George W. Bell
Citations in the corpus
- Predicting effective microRNA target sites in mammalian mRNAs (2015, background): “0, January 2012) ( Wang, 2008 ; Wang and El Naqa, 2008 ), MIRZA-G (sets predicted either with or without conservation features and either with or without more stringent seed-match requirements, March 2015) ( Gumienny and Zavolan, 2015 ), PACCMIT-CDS (sets predicted either with or without…”
- miRDB: an online resource for microRNA target prediction and functional annotations (2014, methodology): “Target prediction was then performed with the MirTarget algorithm, which was developed by analyzing high-throughput expression profiling data in a support vector machine framework (9).”
- LNCipedia: a database for annotated human lncRNA transcript sequences and structures (2012, methodology): “miRNA seed predictions were performed using the MirTarget2 algoritm (21).”
- miRecords: an integrated resource for microRNA–target interactions (2008, background): “Yet, recently developed machine-learning-based target prediction tools (miTarget, MirTarget2 and NBmiRTar) have not been through rigorous independent assessments.”
- Characterization of human plasma-derived exosomal RNAs by deep sequencing (2013, methodology): “All the targets were predicted using MirTarget2 [68,69].”
- Identifying miRNAs, targets and functions (2012, methodology): “Statistical enrichment test of target genes in each term is carried out for Gene Ontology (GO), pathway and disease annotations. miRDB [64] uses MirTarget2 [68] for miRNAtarget prediction using a machine learning method (support vector machines), with public microarray data sets.”
- miRWalk - Database: Prediction of possible miRNA binding sites by "walking" the genes of three genomes (2011, background): “In the past years, a large number of target prediction programs and databases on experimentally validated information have been developed for animal miRNAs [5,16–26].”