Author

Hao‐Bo Guo

University of Tennessee at ChattanoogaORCID 0000-0003-1321-1758Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Systems Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Research Focus:gene regulatory network inference· 2 Approach:statistical model· 2 Organism:yeast· 2 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Organism:human· 1

Frequent Co-Authors

Hong HQ QiniD· 3 shared Mehran GhafariiD· 2 shared Weiwei DangiD· 2 shared Justin Clark· 1 shared Ruofan YuiD· 1 shared Yu SuniD· 1 shared

Activity by Year

2020-2021 · peak 2/yr

Co-Author Collaboration Network

6 top collaborators · 15 mutual co-author links

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3 shared papersBioinformatics

Hong HQ QinORCID

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3 papers

Recent·Most cited

Systems BiologyScientific ReportsPeer-reviewedMar 30, 2021

Protein interaction probability landscapes for yeast replicative aging

Hao‐Bo Guo, Mehran Ghafari, Weiwei Dang, Hong HQ Qin

The paper introduces a probability landscape approach to analyze gene network changes during yeast replicative aging, revealing distinct roles for essential and hub proteins.

4 citations

yeastprotein sequence

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BioinformaticsPLoS ONEPeer-reviewedMar 17, 2021

Complementary Performances of Convolutional and Capsule Neural Networks on Classifying Microfluidic Images of Dividing Yeast Cells

Mehran Ghafari, Justin Clark, Hao‐Bo Guo…·Hong HQ Qin

This study compares convolutional and capsule neural networks for classifying microfluidic images of dividing yeast cells, finding convolutional networks more accurate overall while capsule networks excel at specific categories.

26 citations

yeastimaging

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BioinformaticsScientific ReportsPeer-reviewedJul 1, 2020

Association Study based on Topological Constraints of Protein-Protein Interaction Networks

Hao‐Bo Guo, Hong HQ Qin

The paper introduces NetPAS, a method that uses network topology to assess gene set associations, revealing functional terms missed by existing tools.

3 citations

humanprotein structure

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