Author

Liangqun Lu

University of Hawaiʻi at Mānoa

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 4

Signature Assays, Organisms & Methods

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Organism:human· 4 Assay:bulk RNA-seq· 3 Research Focus:differential expression· 3 Approach:statistical model· 3 Research Focus:benchmarking· 2 Approach:deep learning· 2 Research Focus:protein structure prediction· 2 Assay:DNA methylation· 1

Frequent Co-Authors

Bernie J. DaigleiD· 2 shared Kumardeep ChaudharyiD· 2 shared Olivier PoirioniD· 2 shared Kevin A. Townsend· 1 shared Lana X. GarmireiD· 1 shared Lana Xia GarmireiD· 1 shared Sijia HuangiD· 1 shared Travers H. ChingiD· 1 shared

Activity by Year

2017-2021 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 13 mutual co-author links

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2 shared papersBioinformatics

Bernie J. DaigleORCID

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Author profile →

4 papers

Recent·Most cited

GEOlimma: Differential Expression Analysis and Feature Selection Using Pre-Existing Microarray Data

Liangqun Lu, Kevin A. Townsend, Bernie J. Daigle

GEOlimma improves differential expression analysis by integrating pre-existing microarray data, enhancing power and classification performance compared to Limma.

BMC Bioinformatics · Feb 3, 2021Peer-reviewedBioinformatics

Prognostic Analysis of Histopathological Images Using Pre-Trained Convolutional Neural Networks

Liangqun Lu, Bernie J. Daigle

This study uses pre-trained CNNs to extract features from HCC histopathological images, showing their ability to predict survival outcomes and correlate with biological pathways.

PeerJ · Mar 12, 2020Peer-reviewedBioinformatics

Multi-modal meta-analysis of 1494 hepatocellular carcinoma samples reveals vast impacts of consensus driver genes on phenotypes

Kumardeep Chaudhary, Olivier Poirion, Liangqun Lu, Sijia Huang, Travers H. Ching, Lana Xia Garmire

This study identifies 10 consensus driver genes in HCC across six cohorts and shows their associations with mRNA transcriptomes, patient survival, and demographic factors.

bioRxiv · May 29, 2018 · PreprintBioinformatics

Deep Learning based multi-omics integration robustly predicts survival in liver cancer

Kumardeep Chaudhary, Olivier Poirion, Liangqun Lu, Lana X. Garmire

This paper develops a deep learning model integrating multi-omics data to predict survival in liver cancer, identifying two patient subgroups with significant survival differences.

Clinical Cancer Research · Oct 5, 2017Peer-reviewedBioinformatics