| scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses J Wang, A Ma, Y Chang, J Gong, Y Jiang, R Qi, C Wang, H Fu, Q Ma, ... Nature communications 12 (1), 1882, 2021 | 546 | 2021 |
| Integrative methods and practical challenges for single-cell multi-omics A Ma, A McDermaid, J Xu, Y Chang, Q Ma Trends in biotechnology 38 (9), 1007-1022, 2020 | 198 | 2020 |
| Clustering and classification methods for single-cell RNA-sequencing data R Qi, A Ma, Q Ma, Q Zou Briefings in bioinformatics 21 (4), 1196-1208, 2020 | 176 | 2020 |
| SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting B Yu, W Qiu, C Chen, A Ma, J Jiang, H Zhou, Q Ma Bioinformatics 36 (4), 1074-1081, 2020 | 164 | 2020 |
| Protein–protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique X Wang, B Yu, A Ma, C Chen, B Liu, Q Ma Bioinformatics 35 (14), 2395-2402, 2019 | 152 | 2019 |
| Microglia coordinate cellular interactions during spinal cord repair in mice FH Brennan, Y Li, C Wang, A Ma, Q Guo, Y Li, N Pukos, WA Campbell, ... Nature Communications 13 (1), 4096, 2022 | 131 | 2022 |
| Androgen conspires with the CD8+ T cell exhaustion program and contributes to sex bias in cancer H Kwon, JM Schafer, NJ Song, S Kaneko, A Li, T Xiao, A Ma, C Allen, ... Science immunology 7 (73), eabq2630, 2022 | 124 | 2022 |
| Single-cell biological network inference using a heterogeneous graph transformer A Ma, X Wang, J Li, C Wang, T Xiao, Y Liu, H Cheng, J Wang, Y Li, ... Nature Communications 14 (1), 964, 2023 | 101* | 2023 |
| It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data J Xie, A Ma, A Fennell, Q Ma, J Zhao Briefings in bioinformatics 20 (4), 1450-1465, 2019 | 92 | 2019 |
| Network analyses in microbiome based on high-throughput multi-omics data Z Liu, A Ma, E Mathé, M Merling, Q Ma, B Liu Briefings in bioinformatics 22 (2), 1639-1655, 2021 | 86 | 2021 |
| Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data J Chen, X Wang, A Ma, QE Wang, B Liu, L Li, D Xu, Q Ma Nature Communications 13 (1), 1-13, 2022 | 82 | 2022 |
| NIH SenNet Consortium to map senescent cells throughout the human lifespan to understand physiological health Brown University TDA Wang Siyuan (Steven) 34, ... Nature aging 2 (12), 1090-1100, 2022 | 80 | 2022 |
| Prediction of protein–protein interactions based on elastic net and deep forest B Yu, C Chen, X Wang, Z Yu, A Ma, B Liu Expert Systems with Applications 176, 114876, 2021 | 79 | 2021 |
| QUBIC2: A novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data J Xie, A Ma, Y Zhang, B Liu, S Cao, C Wang, J Xu, C Zhang, Q Ma Bioinformatics, 2019 | 74 | 2019 |
| A review of matched-pairs feature selection methods for gene expression data analysis S Liang, A Ma, S Yang, Y Wang, Q Ma Computational and structural biotechnology journal 16, 88-97, 2018 | 72 | 2018 |
| Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework J Yang, A Ma, AD Hoppe, C Wang, Y Li, C Zhang, Y Wang, B Liu, Q Ma Nucleic acids research 47 (15), 7809-7824, 2019 | 69 | 2019 |
| Inductive inference of gene regulatory network using supervised and semi-supervised graph neural networks J Wang, A Ma, Q Ma, D Xu, T Joshi Computational and Structural Biotechnology Journal 18, 3335-3343, 2020 | 59 | 2020 |
| scGMAI: a Gaussian mixture model for clustering single-cell RNA-Seq data based on deep autoencoder B Yu, C Chen, R Qi, R Zheng, PJ Skillman-Lawrence, X Wang, A Ma, ... Briefings in Bioinformatics, 2020 | 51 | 2020 |
| Explainable deep hypergraph learning modeling the peptide secondary structure prediction Y Jiang, R Wang, J Feng, J Jin, S Liang, Z Li, Y Yu, A Ma, R Su, Q Zou, ... Advanced Science 10 (11), 2206151, 2023 | 50 | 2023 |
| DNNAce: prediction of prokaryote lysine acetylation sites through deep neural networks with multi-information fusion B Yu, Z Yu, C Chen, A Ma, B Liu, B Tian, Q Ma Chemometrics and Intelligent Laboratory Systems 200, 103999, 2020 | 49 | 2020 |
Qin MaProfessor, Department of Biomedical Informatics, The Ohio State UniversityVerified email at osumc.edu