Prediction of potent shRNAs with a sequential classification algorithm

The most effective shRNAs to silence a gene are calculated by a machine learning algorithm. We present SplashRNA, a sequential classifier to predict potent microRNA-based short hairpin RNAs (shRNAs). Trained on published and novel data sets, SplashRNA outperforms previous algorithms and reliably pre...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Nature biotechnology Ročník 35; číslo 4; s. 350 - 353
Hlavní autoři:	Pelossof, Raphael, Fairchild, Lauren, Huang, Chun-Hao, Widmer, Christian, Sreedharan, Vipin T, Sinha, Nishi, Lai, Dan-Yu, Guan, Yuanzhe, Premsrirut, Prem K, Tschaharganeh, Darjus F, Hoffmann, Thomas, Thapar, Vishal, Xiang, Qing, Garippa, Ralph J, Rätsch, Gunnar, Zuber, Johannes, Lowe, Scott W, Leslie, Christina S, Fellmann, Christof
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Nature Publishing Group US 01.04.2017 Nature Publishing Group
Témata:	13/44 13/89 631/114/1305 631/114/2401 631/337/505 631/61/191/505 631/67/68 Agriculture Algorithms Analysis Bioinformatics Biology Biomedical Engineering/Biotechnology Biomedicine Biotechnology brief-communication Cancer Chromosome Mapping - methods Classification Clustered Regularly Interspaced Short Palindromic Repeats - genetics Computer science CRISPR-Cas Systems - genetics Datasets Gene Silencing Genetics Life Sciences Machine Learning Methods MicroRNA MicroRNAs Physiological aspects RNA sequencing RNA, Small Interfering - genetics Sequence Analysis, RNA - methods Software Support vector machines New York United States > US California
ISSN:	1087-0156, 1546-1696, 1546-1696
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!