A Combined Manual Annotation and Deep-Learning Natural Language Processing Study on Accurate Entity Extraction in Hereditary Disease Related Biomedical Literature.

We report a combined manual annotation and deep-learning natural language processing study to make accurate entity extraction in hereditary disease related biomedical literature. A total of 400 full articles were manually annotated based on published guidelines by experienced genetic interpreters at Beijing Genomics Institute (BGI). The performance of our manual annotations was assessed by comparing our re-annotated results with those publicly available.

Correction to: Identification of tRNA nucleoside modification genes critical for stress response and development in rice and Arabidopsis.

Following publication of the original article [1], the authors reported that there was a mistake in the presentation of their funding information. The sentence "This study was supported by the National Natural Science Foundation of China (31,100,268 to Peng Chen, 31,270,658 to Bo Zheng);" should instead read "This study was supported by the National Natural Science Foundation of China (31100268 to Peng Chen, 31370604 to Bo Zheng);".

Identification of tRNA nucleoside modification genes critical for stress response and development in rice and Arabidopsis.

Background: Modification of nucleosides on transfer RNA (tRNA) is important either for correct mRNA decoding process or for tRNA structural stabilization. Nucleoside methylations catalyzed by MTase (methyltransferase) are the most common type among all tRNA nucleoside modifications. Although tRNA modified nucleosides and modification enzymes have been extensively studied in prokaryotic systems, similar research remains preliminary in higher plants, especially in crop species, such as rice (Oryza sativa).