Predicting genes associated with RNA methylation pathways using machine learning
Machine learning of multi-modal data, including transcriptomic, proteomic, structural and physical interaction, predicts genes and molecular pathways involved in RNA methylation in humans.
Main Authors: | Georgia Tsagkogeorga, Helena Santos-Rosa, Andrej Alendar, Dan Leggate, Oliver Rausch, Tony Kouzarides, Hendrik Weisser, Namshik Han |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2022-08-01
|
Series: | Communications Biology |
Online Access: | https://doi.org/10.1038/s42003-022-03821-y |
Similar Items
-
A computational platform for high-throughput analysis of RNA sequences and modifications by mass spectrometry
by: Samuel Wein, et al.
Published: (2020-02-01) -
Downregulation of Dystrophin Expression Occurs across Diverse Tumors, Correlates with the Age of Onset, Staging and Reduced Survival of Patients
by: Nancy Alnassar, et al.
Published: (2023-02-01) -
Histone H3 lysine 4 methylation is associated with the transcriptional reprogramming efficiency of somatic nuclei by oocytes
by: Murata Kazutaka, et al.
Published: (2010-02-01) -
Approaches to the synthesis of the higher terpenes
by: Leggate, P
Published: (1959) -
Active genes are tri-methylated at K4 of histone H3.
by: Santos-Rosa, H, et al.
Published: (2002)