Anjun Ma
Zitiert von
Zitiert von
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Single-Cell techniques and deep learning in predicting drug response
Z Wu, PJ Lawrence, A Ma, J Zhu, D Xu, Q Ma
Trends in Pharmacological Sciences 41 (12), 1050-1065, 2020
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