Author

Chenguang Liang

University of California San Diego

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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Systems Biology· 3 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Research Focus:gene regulatory network inference· 3 Assay:metabolomics· 3 Approach:statistical model· 3 Research Focus:benchmarking· 2 Research Focus:differential expression· 2 Research Focus:drug response / drug discovery· 2 Organism:human· 1 Organism:mouse· 1

Frequent Co-Authors

Austin W.T. ChiangiD· 4 shared Nathan E. LewisiD· 4 shared Benjamin P. KellmaniD· 2 shared Bokan BaoiD· 2 shared Wen-Jen LiniD· 2 shared Anders Bech Bruntse· 1 shared Annalee FürstiD· 1 shared Anne RichelleiD· 1 shared

Activity by Year

2020-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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4 shared papersSystems Biology

Austin W.T. ChiangORCID

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4 papers

Recent·Most cited

iLipidome: enhancing statistical power and interpretability using hidden biosynthetic interdependencies in the lipidome

Wen-Jen Lin, Austin W.T. Chiang, Evanston H. Zhou, Chenguang Liang, Chia-Hsin Liu, Wen‐Lung Ma, Wei‐Chung Cheng, Nathan E. Lewis

iLipidome analyzes lipidomics data using biosynthetic networks to improve statistical power and interpretability, identifying genetic causes of lipid changes and potential therapeutic targets.

bioRxiv · May 16, 2024 · PreprintSystems Biology

LipidSIM: inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, systematic Markov Modeling framework

Chenguang Liang, Sue Murray, Yang Li, Richard G. Lee, Audrey Low, Shruti Sasaki, Austin W.T. Chiang, Wen-Jen Lin, Joel Andrew Mathews, Will Barnes, Nathan E. Lewis

LipidSIM infers lipid biosynthesis perturbations from lipidomics data using a low-parameter Markov model, demonstrating its utility in studying disease-related lipidome changes.

Metabolic Engineering · Mar 1, 2024Peer-reviewedBioinformatics

Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multi-omics integration

Benjamin P. Kellman, Anne Richelle, Jeong‐Yeh Yang, Digantkumar Gopaldas Chapla, Austin W.T. Chiang, Julia A. Najera, Chenguang Liang, Annalee Fürst, Bokan Bao, Natalia Koga, Mahmoud Ali Mohammad, Anders Bech Bruntse, Morey W. Haymond, Kelley W. Moremen, Lars Bode, Nathan E. Lewis

The paper constructs an HMO biosynthesis network using multi-omics integration to predict glycosyltransferases and propose candidate genes for HMO synthesis pathways.

Nature Communications · May 4, 2022Peer-reviewedSystems Biology

A consensus-based and readable extension of Linear Code for Reaction Rules (LiCoRR)

Benjamin P. Kellman, Yujie Zhang, Emma Logomasini, Eric Meinhardt, Karla P. Godinez‐Macias, Austin W.T. Chiang, James T. Sorrentino, Chenguang Liang, Bokan Bao, Yusen Zhou, Sachiko Akase, Isami Sogabe, Thukaa Kouka, Elizabeth A Winzeler, Iain B. H. Wilson, , Nathan E. Lewis

This paper extends Linear Code to represent reaction rules in glycobiology, aiming to improve consistency and interoperability in glycan synthesis models.

Beilstein Journal of Organic Chemistry · Oct 27, 2020Peer-reviewedSystems Biology