Anjun Ma

Orcid: 0000-0001-6269-398X

According to our database1, Anjun Ma authored at least 15 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
A weighted two-stage sequence alignment framework to identify motifs from ChIP-exo data.
Patterns, March, 2024

2023
Inference of disease-associated microbial gene modules based on metagenomic and metatranscriptomic data.
Comput. Biol. Medicine, October, 2023

2022
scGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data.
Bioinform., November, 2022

MMGraph: a multiple motif predictor based on graph neural network and coexisting probability for ATAC-seq data.
Bioinform., 2022

Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data.
Briefings Bioinform., 2022

Machine learning development environment for single-cell sequencing data analyses.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022

2021
Prediction of protein-protein interactions based on elastic net and deep forest.
Expert Syst. Appl., 2021

Network analyses in microbiome based on high-throughput multi-omics data.
Briefings Bioinform., 2021

2020
IRIS3: integrated cell-type-specific regulon inference server from single-cell RNA-Seq.
Nucleic Acids Res., 2020

SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting.
Bioinform., 2020

QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data.
Bioinform., 2020

Clustering and classification methods for single-cell RNA-sequencing data.
Briefings Bioinform., 2020

2019
Protein-protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique.
Bioinform., 2019

MetaQUBIC: a computational pipeline for gene-level functional profiling of metagenome and metatranscriptome.
Bioinform., 2019

It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data.
Briefings Bioinform., 2019


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