| 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 | 211 | 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 | 59 | 2018 |
| 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 | 35 | 2015 |
| 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 | 33 | 2018 |
| CAFU: a galaxy framework for exploring unmapped RNA-Seq data S Chen, C Ren, J Zhai, J Yu, X Zhao, Z Li, T Zhang, W Ma, Z Han, C Ma Briefings in Bioinformatics 21 (2), 676-686, 2020 | 13 | 2020 |
| deepEA: a containerized web server for interactive analysis of epitranscriptome sequencing data J Zhai, J Song, T Zhang, S Xie, C Ma Plant Physiology 185 (1), 29-33, 2021 | 12 | 2021 |
| A meta-analysis based method for prioritizing candidate genes involved in a pre-specific function J Zhai, Y Tang, H Yuan, L Wang, H Shang, C Ma Frontiers in Plant Science 7, 1914, 2016 | 12 | 2016 |
| Interactive web-based annotation of plant MicroRNAs with iwa-miRNA T Zhang, J Zhai, X Zhang, L Ling, M Li, S Xie, M Song, C Ma Genomics, Proteomics and Bioinformatics 20 (3), 557-567, 2022 | 11 | 2022 |
| easyMF: a web platform for matrix factorization-based gene discovery from large-scale transcriptome data W Ma, S Chen, Y Qi, M Song, J Zhai, T Zhang, S Xie, G Wang, C Ma Interdisciplinary Sciences: Computational Life Sciences 14 (3), 746-758, 2022 | 8 | 2022 |
| Cross-species modeling of plant genomes at single nucleotide resolution using a pre-trained DNA language model J Zhai, A Gokaslan, Y Schiff, A Berthel, ZY Liu, WY Lai, ZR Miller, ... bioRxiv, 2024 | 7 | 2024 |
| Global hypermethylation of the N6-methyladenosine RNA modification associated with apple heterografting J Xu, J He, B Hu, N Hou, J Guo, C Wang, X Li, Z Li, J Zhai, T Zhang, C Ma, ... Plant Physiology 193 (4), 2513-2537, 2023 | 4 | 2023 |
| Exploring transcriptional switches from pairwise, temporal and population RNA-Seq data using deepTS Z Qiu, S Chen, Y Qi, C Liu, J Zhai, S Xie, C Ma Briefings in Bioinformatics 22 (3), bbaa137, 2021 | 4 | 2021 |
| miRLocator: a Python implementation and web server for predicting miRNAs from pre-miRNA sequences T Zhang, L Ju, J Zhai, Y Song, J Song, C Ma Plant MicroRNAs: Methods and Protocols, 89-97, 2019 | 4 | 2019 |
| Extensive genome evolution distinguishes maize within a stable tribe of grasses MC Stitzer, AS Seetharam, A Scheben, SK Hsu, AJ Schulz, ... bioRxiv, 2025.01. 22.633974, 2025 | 3 | 2025 |
| Fishing for a reelGene: evaluating gene models with evolution and machine learning AJ Schulz, J Zhai, T AuBuchon-Elder, M El-Walid, TH Ferebee, ... BioRxiv, 2023.09. 19.558246, 2023 | 3 | 2023 |
| PEA-m6A: an ensemble learning framework for accurately predicting N6-methyladenosine modifications in plants M Song, J Zhao, C Zhang, C Jia, J Yang, H Zhao, J Zhai, B Lei, S Tao, ... Plant Physiology 195 (2), 1200-1213, 2024 | 2 | 2024 |
| The maize recombination landscape evolved during domestication R Epstein, JJ Wheeler, M Hubisz, Q Sun, R Bukowski, J Zhai, WY Lai, ... bioRxiv, 2024.11. 04.621928, 2024 | | 2024 |
| Effects of dietary supplementation of soybean protein hydrolysate on production performance and nutrients utilization rate in weaned piglets. WD Liu, P Cheng, ZC Wang, LG Huang, QY Wang, HX Cheng, MD Zhang, ... | | 2020 |
Chuang MaCenter of Bioinformatics, College of Life Sciences, Northwest A&F University, ChinaПотвърден имейл адрес: nwafu.edu.cn
