Identification of differential RNA modifications from nanopore direct RNA sequencing with xPore PN Pratanwanich, F Yao, Y Chen, CWQ Koh, YK Wan, C Hendra, P Poon, ... Nature biotechnology 39 (11), 1394-1402, 2021 | 176 | 2021 |
Detection of m6A from direct RNA sequencing using a multiple instance learning framework C Hendra, PN Pratanwanich, YK Wan, WSS Goh, A Thiery, J Göke Nature methods 19 (12), 1590-1598, 2022 | 85 | 2022 |
A systematic benchmark of Nanopore long read RNA sequencing for transcript level analysis in human cell lines Y Chen, NM Davidson, YK Wan, H Patel, F Yao, HM Low, C Hendra, ... BioRxiv, 2021.04. 21.440736, 2021 | 59 | 2021 |
Beyond sequencing: machine learning algorithms extract biology hidden in Nanopore signal data YK Wan, C Hendra, PN Pratanwanich, J Göke Trends in Genetics 38 (3), 246-257, 2022 | 58 | 2022 |
N-glycan profiles of acute myocardial infarction patients reveal potential biomarkers for diagnosis, severity assessment and treatment monitoring SY Lim, C Hendra, XH Yeo, XY Tan, BH Ng, AKC Laserna, SH Tan, ... Glycobiology 32 (6), 469-482, 2022 | 7 | 2022 |
N6-methyl adenosine from individual direct RNA C Hendra, J Göke nature methods 19, 1530-1531, 2022 | 1 | 2022 |
Signal to Sequence Attention-Based Multiple Instance Network for Segmentation Free Inference of RNA Modifications C Hendra, AH Thiery, J Goeke | | 2022 |
DEEP MULTIPLE INSTANCE LEARNING ON BIOLOGICAL DATA C HENDRA | | 2022 |
Extracting Part of Signal Representation from Direct RNA Squiggle for Modification Detection C Hendra, J Göke, A Thiery | | |