English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Long non-coding RNAs (lncRNAs) have important functions in the regulation of life activities at multiple levels, such as chromatin remodeling, transcriptional and posttranscriptional regulation. They have different functions according to the different locations in the cell. Therefore, it is important to obtain the sub-cellular location information to understand the functions of lncRNAs. However, the biochemical experimental way of sub-cellular localization requires a long period and high cost. Currently, many methods of predicting sub-cellular location based on the machine learning and deep learning have been developed to ease the difficulty in detecting the sub-cellular location. We fetched the latest version RNA database to evaluate the performance of existing predictors, lncLocator and iLoc-lncRNA.

Performance of Existing Predictor of SubCellular Localization of Long Non-Coding RNA on the New Database