Author

Liang Fang

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

Same name, identified by ORCID

Biology Disciplines

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Bioinformatics· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:drug response / drug discovery· 1 Organism:human· 1 Assay:metagenomics· 1 Approach:statistical model· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Alena L. PribyliD· 1 shared Andrea Rabellino· 1 shared Annika KruegeriD· 1 shared Areej S. Alsheikh-HussainiD· 1 shared Ben LiaoiD· 1 shared Blake WillsiD· 1 shared Bozica Nyeverecz· 1 shared Chang‐Yu HsiehiD· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperMicrobiology

Alena L. PribylORCID

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Author profile →

2 papers

Recent·Most cited

A rational approach for the targeted discovery and characterisation of microbiome-derived therapeutics

Lutz Krause, Joel A. Boyd, Charlotte Vivian, Annika Krueger, Jeimy Johana Jimenez Loayza, Johanna K. Ljungberg, Joyce Zhou, Andrea Rabellino, Bozica Nyeverecz, Michael D. Nissen, Kaylyn D. Tousignant, Mareike Bongers, Martha M. Cooper, Donovan H. Parks, Areej S. Alsheikh-Hussain, , Páraic Ó Cuív

This paper identifies microbiome-derived therapeutics for IBD using a metagenome-guided approach, showing improved disease markers and safety in preclinical models.

bioRxiv · Oct 22, 2024 · PreprintMicrobiology

Deep-BGCpred: A unified deep learning genome-mining framework for biosynthetic gene cluster prediction

Ziyi Yang, Ben Liao, Chang‐Yu Hsieh, Chao Han, Liang Fang, Shengyu Zhang

Deep-BGCpred improves BGC prediction by reducing false positives using deep learning and customized strategies, achieving state-of-the-art results on benchmarks.

bioRxiv · Nov 15, 2021 · PreprintBioinformatics