Qin Ma
Qin Ma
Professor, Department of Biomedical Informatics, The Ohio State University
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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), 1-11, 2021
QUBIC: a qualitative biclustering algorithm for analyses of gene expression data
G Li, Q Ma, H Tang, AH Paterson, Y Xu
Nucleic acids research 37 (15), e101-e101, 2009
Interpretation of differential gene expression results of RNA-seq data: review and integration
A McDermaid, B Monier, J Zhao, B Liu, Q Ma
Briefings in bioinformatics 20 (6), 2044-2054, 2019
Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier
C Chen, Q Zhang, B Yu, Z Yu, PJ Lawrence, Q Ma, Y Zhang
Computers in biology and medicine 123, 103899, 2020
LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion
C Chen, Q Zhang, Q Ma, B Yu
Chemometrics and Intelligent Laboratory Systems 191, 54-64, 2019
DOOR 2.0: presenting operons and their functions through dynamic and integrated views
X Mao, Q Ma, C Zhou, X Chen, H Zhang, J Yang, F Mao, W Lai, Y Xu
Nucleic acids research 42 (D1), D654-D659, 2014
Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure
H Shi, S Liu, J Chen, X Li, Q Ma, B Yu
Genomics 111 (6), 1839-1852, 2019
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
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, 2019
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
Caldicellulosiruptor core and pan genomes reveal determinants for non-cellulosomal thermophilic deconstruction of plant biomass
SE Blumer-Schuette, RJ Giannone, JV Zurawski, I Ozdemir, Q Ma, Y Yin, ...
Journal of Bacteriology, 2012
LncFinder: an integrated platform for long non-coding RNA identification utilizing sequence intrinsic composition, structural information and physicochemical property
S Han, Y Liang, Q Ma, Y Xu, Y Zhang, W Du, C Wang, Y Li
Briefings in bioinformatics 20 (6), 2009-2027, 2019
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
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
A shared disease-associated oligodendrocyte signature among multiple CNS pathologies
M Kenigsbuch, P Bost, S Halevi, Y Chang, S Chen, Q Ma, R Hajbi, ...
Nature neuroscience 25 (7), 876-886, 2022
Metabolomics and multi-omics integration: a survey of computational methods and resources
T Eicher, G Kinnebrew, A Patt, K Spencer, K Ying, Q Ma, R Machiraju, ...
Metabolites 10 (5), 202, 2020
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
scREAD: a single-cell RNA-Seq database for Alzheimer's disease
J Jiang, C Wang, R Qi, H Fu, Q Ma
Iscience 23 (11), 2020
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