Xuejun Liu

发表

Gene expression Probe-level measurement error improves accuracy in detecting differential gene expression

Neil D. Lawrence, M. Rattray, M. Milo, 2006 .

DAE-TPGM: A deep autoencoder network based on a two-part-gamma model for analyzing single-cell RNA-seq data

Xuejun Liu, Shuchang Zhao, Li Zhang, 2022, Comput. Biol. Medicine.

Modeling Exon-Specific Bias Distribution Improves the Analysis of RNA-Seq Data

Songcan Chen, Xuejun Liu, Li Zhang, 2015, PloS one.

AE-TPGG: a novel autoencoder-based approach for single-cell RNA-seq data imputation and dimensionality reduction

Xuejun Liu, Shuchang Zhao, Li Zhang, 2022, Frontiers of Computer Science.

Detecting differential expression from RNA-seq data with expression measurement uncertainty

Songcan Chen, Xuejun Liu, Li Zhang, 2014, Frontiers of Computer Science.

A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips

Neil D. Lawrence, Magnus Rattray, Xuejun Liu, 2005, Bioinform..

Mapping RNA-seq reads to transcriptomes efficiently based on learning to hash method

Xuejun Liu, Xueting Yu, Xuejun Liu, 2019, Comput. Biol. Medicine.

puma: a Bioconductor package for propagating uncertainty in microarray analysis

Neil D. Lawrence, Magnus Rattray, Xuejun Liu, 2009, BMC Bioinformatics.

Propagating uncertainty in microarray data analysis

Neil D. Lawrence, Magnus Rattray, Xuejun Liu, 2006, Briefings Bioinform..

Probe-level measurement error improves accuracy in detecting differential gene expression

Neil D. Lawrence, Magnus Rattray, Xuejun Liu, 2006, Bioinform..

Improving RNA-Seq expression estimation by modeling isoform- and exon-specific read sequencing rate

Xuejun Liu, Li Zhang, Xinxin Shi, 2015, BMC Bioinformatics.

Microarray Data Analysis using Probabilistic Methods

Xuejun Liu, Xuejun Liu, 2006 .

puma 3.0: improved uncertainty propagation methods for gene and transcript expression analysis

Magnus Rattray, Li Zhang, Xuejun Liu, 2013, BMC Bioinformatics.

PBSeq: Modeling base-level bias to estimate gene and isoform expression for RNA-seq data

Li Zhang, Xuejun Liu, Xuejun Liu, 2016, International Journal of Machine Learning and Cybernetics.

PBSeq: Modeling base-level bias to estimate gene and isoform expression for RNA-seq data

Xuejun Liu, Li Zhang, 2016, International Journal of Machine Learning and Cybernetics.

Improving RNA-Seq expression estimation by modeling isoform- and exon-specific read sequencing rate

Li Zhang, Chunlin Chen, Xuejun Liu, 2015, BMC Bioinformatics.

Detecting differential transcript usage across multiple conditions for RNA-seq data based on the smoothed LDA model

Daoqiang Zhang, Xuejun Liu, Jing Li, 2020, Frontiers of Computer Science.