| Genome-wide identification of heat shock proteins (Hsps) and Hsp interactors in rice: Hsp70s as a case study Y Wang, S Lin, Q Song, K Li, H Tao, J Huang, X Chen, S Que, H He BMC genomics 15, 1-15, 2014 | 74 | 2014 |
| Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites S Lin, Q Song, H Tao, W Wang, W Wan, J Huang, C Xu, V Chebii, J Kitony, ... Scientific reports 5 (1), 11940, 2015 | 44 | 2015 |
| Genome wide association study and genomic selection of amino acid concentrations in soybean seeds J Qin, A Shi, Q Song, S Li, F Wang, Y Cao, W Ravelombola, Q Song, ... Frontiers in Plant Science 10, 1445, 2019 | 40 | 2019 |
| Prediction of condition-specific regulatory genes using machine learning Q Song, J Lee, S Akter, M Rogers, R Grene, S Li Nucleic Acids Research 48 (11), e62-e62, 2020 | 35 | 2020 |
| Prediction of protein–protein interactions between fungus (Magnaporthe grisea) and rice (Oryza sativa L.) S Ma, Q Song, H Tao, A Harrison, S Wang, W Liu, S Lin, Z Zhang, Y Ai, ... Briefings in bioinformatics 20 (2), 448-456, 2019 | 25 | 2019 |
| Identification of regulatory modules in genome scale transcription regulatory networks Q Song, R Grene, LS Heath, S Li BMC Systems Biology 11, 1-18, 2017 | 19 | 2017 |
| Identification of new marker genes from plant single‐cell RNA‐seq data using interpretable machine learning methods H Yan, J Lee, Q Song, Q Li, J Schiefelbein, B Zhao, S Li New Phytologist 234 (4), 1507-1520, 2022 | 18* | 2022 |
| Computational analysis of alternative splicing in plant genomes QA Song, NS Catlin, WB Barbazuk, S Li Gene 685, 186-195, 2019 | 16 | 2019 |
| Modelling post-implantation human development to yolk sac blood emergence J Hislop, Q Song, K Keshavarz F, A Alavi, R Schoenberger, R LeGraw, ... Nature 626 (7998), 367-376, 2024 | 14 | 2024 |
| Modelling human post-implantation development via extra-embryonic niche engineering J Hislop, A Alavi, Q Song, R Schoenberger, KF Kamyar, R LeGraw, ... bioRxiv, 2023 | 12 | 2023 |
| The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks M Ben Guebila, T Wang, CM Lopes-Ramos, V Fanfani, D Weighill, ... Genome Biology 24 (1), 45, 2023 | 9 | 2023 |
| Using single cell atlas data to reconstruct regulatory networks Q Song, M Ruffalo, Z Bar-Joseph Nucleic Acids Research 51 (7), e38-e38, 2023 | 6 | 2023 |
| scSTEM: clustering pseudotime ordered single-cell data Q Song, J Wang, Z Bar-Joseph Genome Biology 23 (1), 150, 2022 | 5 | 2022 |
| Identification of novel lincRNA and co-expression network analysis using RNA-sequencing data in plants S Qi, S Akter, S Li Plant long non-coding RNAs: methods and protocols, 207-221, 2019 | 4 | 2019 |
| Classification of the fragrant styles and evaluation of the aromatic quality of flue-cured tobacco leaves by machine-learning methods L Gu, L Xue, Q Song, F Wang, H He, Z Zhang Journal of Bioinformatics and Computational Biology 14 (06), 1650033, 2016 | 4 | 2016 |
| Identification of plant co-regulatory modules using coReg Q Song, S Li Transcription Factor Regulatory Networks, 217-223, 2022 | 1 | 2022 |
| Developing machine learning tools to understand transcriptional regulation in plants Q Song Virginia Tech, 2019 | 1 | 2019 |
| Prediction of condition-specific regulatory maps in Arabidopsis using integrated genomic data Q Song, J Lee, S Akter, R Grene, S Li bioRxiv, 565119, 2019 | 1 | 2019 |
| Transcription Factor Regulatory Networks Q Song, Z Tao Humana Press, 2023 | | 2023 |
| Modeling Plant Transcription Factor Networks Using ConSReg Q Song, S Li Transcription Factor Regulatory Networks, 205-215, 2022 | | 2022 |
Song LiVirginia TechVerified email at vt.edu
