Author

Wanli Wang

Baylor College of MedicineORCID 0000-0002-1024-5015Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Wanli Wang1 paper · Biogen (United States)

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Organism:human· 2 Assay:metabolomics· 2 Approach:deep learning· 1 Organism:Drosophila· 1 Research Focus:drug response / drug discovery· 1 Organism:mouse· 1 Approach:statistical model· 1

Frequent Co-Authors

Lihua MaiD· 2 shared Mirjana Maletić‐SavatićiD· 2 shared Zhandong LiuiD· 2 shared Damian W. YoungiD· 1 shared Genevera I. AlleniD· 1 shared Ismael Al‐RamahiiD· 1 shared Juan BotasiD· 1 shared Kevin R. MacKenzieiD· 1 shared

Activity by Year

2023-2023 · peak 2/yr

Co-Author Collaboration Network

9 top collaborators · 36 mutual co-author links

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2 shared papersBioinformatics

Lihua MaORCID

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2 papers

Recent·Most cited

NMRQNet: a deep learning approach for automatic identification and quantification of metabolites using Nuclear Magnetic Resonance (NMR) in human plasma samples

Wanli Wang, Lihua Ma, Mirjana Maletić‐Savatić, Zhandong Liu

NMRQNet automatically identifies and quantifies metabolites in human plasma NMR spectra, outperforming existing methods and identifying potential biomarkers for neurological disorders.

bioRxiv · Mar 1, 2023 · PreprintBioinformatics

SPA-STOCSY: An Automated Tool for Identification of Annotated and Non-Annotated Metabolites in High-Throughput NMR Spectra

Xu Han, Wanli Wang, Lihua Ma, Ismael Al‐Ramahi, Juan Botas, Kevin R. MacKenzie, Genevera I. Allen, Damian W. Young, Zhandong Liu, Mirjana Maletić‐Savatić

SPA-STOCSY is an automated tool for identifying annotated and non-annotated metabolites in NMR spectra, showing high accuracy and efficiency in both synthetic and real data.

bioRxiv · Feb 22, 2023 · PreprintBioinformatics