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Anjali Koppal

Columbia University

Publishes on RNA and protein synthesis mechanisms, RNA Research and Splicing, MicroRNA in disease regulation. 3 papers and 1.6k citations.

3Publications
1.6kTotal Citations

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Top publicationsby citations

Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites
Doron Betel, Anjali Koppal, Phaedra Agius et al.|Genome biology|2010
Cited by 1.6kOpen Access

mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.

predicted microRNA target sites (miRanda)
Doron Betel, Anjali Koppal, Phaedra Agius et al.|Figshare|2020
Cited by 0Open Access

Target predictions based on the miRanda algorithm. The target sites are scored for likelihood of mRNA downregulation using mirSVR, a regression model that is trained on sequence and contextual features of the predicted miRNA::mRNA duplex. Expression profiles are derived from a comprehensive sequencing project of a large set of mammalian tissues and cell lines of normal and disease origin. This collection contains the following datasets from the August 2010 release of microRNA.org: 16228619 predicted microRNA target sites in 34911 distinct 3'UTR from isoforms of 19898 human genes 7459149 predicted microRNA target sites in 28287 distinct 3'UTR from isoforms of 19231 mouse genes 586068 predicted microRNA target sites in 6865 distinct 3'UTR from isoforms of 6256 rat genes 345671 predicted microRNA target sites in 12285 distinct 3'UTR from isoforms of 10532 fruitfly genes

predicted microRNA target sites (miRanda)
Doron Betel, Anjali Koppal, Phaedra Agius et al.|Zenodo (CERN European Organization for Nuclear Research)|2020
Cited by 0Open Access

Target predictions based on the miRanda algorithm. The target sites are scored for likelihood of mRNA downregulation using mirSVR, a regression model that is trained on sequence and contextual features of the predicted miRNA::mRNA duplex. Expression profiles are derived from a comprehensive sequencing project of a large set of mammalian tissues and cell lines of normal and disease origin. This collection contains the following datasets from the August 2010 release of microRNA.org: 16228619 predicted microRNA target sites in 34911 distinct 3'UTR from isoforms of 19898 human genes 7459149 predicted microRNA target sites in 28287 distinct 3'UTR from isoforms of 19231 mouse genes 586068 predicted microRNA target sites in 6865 distinct 3'UTR from isoforms of 6256 rat genes 345671 predicted microRNA target sites in 12285 distinct 3'UTR from isoforms of 10532 fruitfly genes