Joshua W. K. Ho

发表

Ultrafast clustering of single-cell flow cytometry data using FlowGrid

Xiaoxin Ye, Joshua W. K. Ho, J. Ho, 2019, BMC Systems Biology.

PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells

Hayden Kwok-Hay So, Joshua W. K. Ho, shobana Venkat stassen, 2019, bioRxiv.

Verification and validation of bioinformatics software without a gold standard: a case study of BWA and Bowtie

Eleni Giannoulatou, Joshua W. K. Ho, Shin-Ho Park, 2014, BMC Bioinformatics.

An innovative approach for testing bioinformatics programs using metamorphic testing

Huai Liu, Tsong Yueh Chen, Xiaoyuan Xie, 2009, BMC Bioinformatics.

Application of Metamorphic Testing to Supervised Classifiers

Baowen Xu, Gail E. Kaiser, Tsong Yueh Chen, 2009, 2009 Ninth International Conference on Quality Software.

starmapVR: immersive visualisation of single cell spatial omic data

Yu Yao, Jianfu Li, Andrian Yang, 2020 .

XGSA: A statistical method for cross-species gene set analysis

Djordje Djordjevic, Joshua W. K. Ho, Kenro Kusumi, 2016, Bioinform..

Falco: A quick and flexible single-cell RNA-seq processing framework on the cloud

Peijie Lin, Andrian Yang, Joshua W. K. Ho, 2016, bioRxiv.

hiHMM: Bayesian non-parametric joint inference of chromatin state maps

Peter J. Park, Kyung-Ah Sohn, Djordje Djordjevic, 2015, Bioinform..

Verification of phylogenetic Inference Programs Using Metamorphic Testing

Fei-Ching Kuo, Tsong Yueh Chen, Michael A. Charleston, 2011, J. Bioinform. Comput. Biol..

Gene-gene interaction filtering with ensemble of filters

Jean Yee Hwa Yang, Bing Bing Zhou, Pengyi Yang, 2011, BMC Bioinformatics.

A genetic ensemble approach for gene-gene interaction identification

Albert Y. Zomaya, Bing Bing Zhou, Pengyi Yang, 2010, BMC Bioinformatics.

Integrative analysis identifies co‐dependent gene expression regulation of BRG1 and CHD7 at distal regulatory sites in embryonic stem cells

Jean Yee Hwa Yang, Taiyun Kim, Pengyi Yang, 2017, Bioinform..

A cloud-based framework for applying metamorphic testing to a bioinformatics pipeline

Tsong Yueh Chen, Eleni Giannoulatou, Amir Hossein Kamali, 2016, MET@ICSE.

Harnessing Multiple Source Test Cases in Metamorphic Testing: A Case Study in Bioinformatics

Tsong Yueh Chen, Andrian Yang, Joshua Y. S. Tang, 2017, 2017 IEEE/ACM 2nd International Workshop on Metamorphic Testing (MET).

PBrowse: A web-based platform for real-time collaborative exploration of genomic data

Uwe Röhm, Andrian Yang, Joshua W. K. Ho, 2016 .

Differential variability analysis of gene expression and its application to human diseases

Michael A. Charleston, Joshua W. K. Ho, Maurizio Stefani, 2008, ISMB.

Expression Clustering

Joshua W. K. Ho, Xiaoxin Ye, J. Ho, 2019, Encyclopedia of Bioinformatics and Computational Biology.

iSyTE 2.0: a database for expression-based gene discovery in the eye

Djordje Djordjevic, Hongzhan Huang, Abhyudai Singh, 2017, Nucleic Acids Res..

Falco: A quick and flexible single-cell RNA-seq processing framework on the cloud

Peijie Lin, Andrian Yang, Joshua W. K. Ho, 2016 .

Discovery of perturbation gene targets via free text metadata mining in Gene Expression Omnibus

Joshua Y. S. Tang, Joshua W. K. Ho, Yun Xin Chen, 2018 .

Co-expression based regulatory network for lens development

Cathy H. Wu, Djordje Djordjevic, Abhyudai Singh, 2015 .

Testing and validating machine learning classifiers by metamorphic testing

Baowen Xu, Gail E. Kaiser, Tsong Yueh Chen, 2011, J. Syst. Softw..

FlowGrid enables fast clustering of very large single-cell RNA-seq data

Joshua W. K. Ho, Xiunan Fang, J. Ho, 2021, Bioinform..

How Difficult Is Inference of Mammalian Causal Gene Regulatory Networks?

Djordje Djordjevic, Andrian Yang, Joshua W. K. Ho, 2014, PloS one.

CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-Seq data

Joshua W. K. Ho, Michael Troup, Peijie Lin, 2016 .

CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data

Joshua W. K. Ho, Michael Troup, J. Ho, 2016, Genome Biology.

Impact of sequencing depth and read length on single cell RNA sequencing data: lessons from T cells

Joshua W. K. Ho, Rowena A. Bull, Fabio Luciani, 2017, bioRxiv.