Prediction and validation of microRNAs and their targets

By Isaac Bentwich. FEBS Letters, 2005

398 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/68e421d37c2c20b2208fbed18959a2ed78144abb

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Citations in the corpus

Thermodynamics of RNA-RNA Binding (2006, methodology): “It can, however, be combined easily with faster methods for assessing RNA-RNA interactions, such as RNAhybrid and RNAduplex, or with other microRNA target prediction programs (see (Bentwich, 2005; Brennecke et al., 2005) for reviews) that are fast enough for genome-wide screens.”
Genome-wide identification and analysis of drought-responsive microRNAs in Oryza sativa. (2010, background): “Micro RNAs (miRNAs) are a class of non-protein-coding, single-strand molecules of 20–27 nucleotides in length, that have been reported to regulate gene expression at the posttranscriptional level in plants, animals, nematodes, and fungi (Bartel, 2004; Denli et al., 2004; Dugas and Bartel, 2004; Meis”
Identifying miRNAs, targets and functions (2012, background): “used to train the learning processes to discriminate between true predictions and false positives [16, 24].”
Transcripts Targeted by the MicroRNA-16 Family Cooperatively Regulate Cell Cycle Progression (2007, methodology): “Available methods for validation are laborious and not easily amenable to high-throughput methodologies (4).”
A combined computational and microarray-based approach identifies novel microRNAs encoded by human gamma-herpesviruses. (2006, background): “The BART locus can give rise to a multitude of differentially spliced transcripts originating from several promoters (Hitt et al. 1989; Gilligan et al. 1990, 1991; Chen et al. 1992, 2005; Brooks et al. 1993; Smith et al. 1993, 2000; Sadler and Raab-Traub 1995; Sugiura et al. 1996).”
microRNA target predictions in animals (2006, methodology): “Although the general nature of all methods were related (for a more detailed comparison see ref”
Discovering microRNAs from deep sequencing data using miRDeep (2008, background): “A complementary approach, involving miRNA discovery by computational predictions that analyze genomic DNA for structures that resemble known miRNA precursor”