| Machine learning for Big Data analytics in plants Chuang Ma, Hao Helen Zhang, Xiangfeng Wang Trends in Plant Science, DOI: http://dx.doi.org/10.1016/j.tplants, 2014 | 245* | 2014 |
| The reference genome of the halophytic plant Eutrema salsugineum R Yang, DJ Jarvis, H Chen, M Beilstein, J Grimwood, J Jenkins, SQ Shu, ... Frontiers in Plant Science 4 (46), 10.3389/fpls.2013.00046, 2013 | 225 | 2013 |
| RNA sequencing of laser-capture microdissected compartments of the maize kernel identifies regulatory modules associated with endosperm cell differentiation J Zhan, D Thakare, C Ma, A Lloyd, NM Nixon, AM Arakaki, WJ Burnett, ... The Plant Cell 27 (3), 513-531, 2015 | 184 | 2015 |
| A deep convolutional neural network approach for predicting phenotypes from genotypes W Ma, Z Qiu, J Song, J Li, Q Cheng, J Zhai, C Ma Planta 248, 1307-1318, 2018 | 101 | 2018 |
| Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis C Ma, M Xin, KA Feldmann, X Wang The Plant Cell 26 (2), 520-537, 2014 | 100 | 2014 |
| Dynamic Expression of Imprinted Genes Associates with Maternally Controlled Nutrient Allocation during Maize Endosperm Development M Xin, R Yang, G Li, H Chen, J Laurie, C Ma, D Wang, Y Yao, BA Larkins, ... The Plant Cell 25 (9), 3212-3227, 2013 | 95 | 2013 |
| Evolution of the RNA N6-methyladenosine methylome mediated by genomic duplication Z Miao, T Zhang, Y Qi, J Song, Z Han, C Ma Plant Physiology 182 (1), 345-360, 2020 | 65 | 2020 |
| Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis. C Ma, XF Wang Plant physiology 160 (1), 10, 2012 | 63 | 2012 |
| A systems approach to a spatio-temporal understanding of the drought stress response in maize Z Miao, Z Han, T Zhang, S Chen, C Ma Scientific reports 7 (1), 6590, 2017 | 62 | 2017 |
| Opaque-2 regulates a complex gene network associated with cell differentiation and storage functions of maize endosperm J Zhan, G Li, CH Ryu, C Ma, S Zhang, A Lloyd, BG Hunter, BA Larkins, ... The Plant Cell 30 (10), 2425-2446, 2018 | 52 | 2018 |
| Transcriptome-Wide Annotation of m5C RNA Modifications Using Machine Learning J Song, J Zhai, E Bian, Y Song, J Yu, C Ma Frontiers in plant science 9, 519, 2018 | 42 | 2018 |
| Hybrid sequencing reveals insight into heat sensing and signaling of bread wheat X Wang, S Chen, X Shi, D Liu, P Zhao, Y Lu, Y Cheng, Z Liu, X Nie, ... The Plant Journal 98 (6), 1015-1032, 2019 | 41 | 2019 |
| The genetic mechanism of heterosis utilization in maize improvement Y Xiao, S Jiang, Q Cheng, X Wang, J Yan, R Zhang, F Qiao, C Ma, J Luo, ... Genome Biology 22 (1), 148, 2021 | 37 | 2021 |
| Evolutionary Origin, Gradual Accumulation and Functional Divergence of Heat Shock Factor Gene Family with Plant Evolution X Wang, X Shi, S Chen, C Ma, S Xu Frontiers in Plant Science 9, 71, 2018 | 33 | 2018 |
| LightGBM: accelerated genomically designed crop breeding through ensemble learning J Yan, Y Xu, Q Cheng, S Jiang, Q Wang, Y Xiao, C Ma, J Yan, X Wang Genome Biology 22, 1-24, 2021 | 30 | 2021 |
| Position‐specific residue preference features around the ends of helices and strands and a novel strategy for the prediction of secondary structures MJ Duan, M Huang, C Ma, L Li, YH Zhou Protein Science 17 (9), 1505-1512, 2008 | 30 | 2008 |
| miRLocator: machine learning-based prediction of mature microRNAs within plant pre-miRNA sequences H Cui, J Zhai, C Ma PLoS One 10 (11), e0142753, 2015 | 28 | 2015 |
| Transcriptome dynamics during maize endosperm development J Qu, C Ma, J Feng, S Xu, L Wang, F Li, Y Li, R Zhang, X Zhang, J Xue, ... PLoS One 11 (10), e0163814, 2016 | 25 | 2016 |
| PEA: an integrated R toolkit for plant epitranscriptome analysis J Zhai, J Song, Q Cheng, Y Tang, C Ma Bioinformatics 34 (21), 3747-3749, 2018 | 20 | 2018 |
| Dynamic parent-of-origin effects on small interfering RNA expression in the developing maize endosperm M Xin, R Yang, Y Yao, C Ma, H Peng, Q Sun, X Wang, Z Ni BMC plant biology 14 (1), 1-13, 2014 | 20 | 2014 |
