Author

Siliangyu Cheng

University of Southern CaliforniaORCID 0009-0004-3465-3832Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Research Focus:database / resource· 1 Approach:deep learning· 1 Assay:GWAS summary statistics· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Assay:metagenomics· 1 Research Focus:sequence-to-function modeling· 1

Frequent Co-Authors

Artem KimiD· 1 shared Dhrithi DeshpandeiD· 1 shared Fengzhu SuniD· 1 shared Jed A. FuhrmaniD· 1 shared Shengwei HouiD· 1 shared Steven GazaliD· 1 shared Tian XiaiD· 1 shared Tianqi TangiD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

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10 top collaborators · 24 mutual co-author links

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1 shared paperGenetic & Genomic Medicine

Artem KimORCID

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2 papers

Recent·Most cited

Genetic & Genomic MedicinemedRxivPreprintOct 23, 2025

A machine-learning framework to characterize functional disease architectures and prioritize disease variants

Siliangyu Cheng, Artem Kim, Dhrithi Deshpande, Steven Gazal

The paper introduces a machine-learning framework to model disease effect sizes from GWAS data, identifying constrained regulatory variants as key functional components and improving variant prioritization.

DOI 10.1101/2025.10.23.25338598

humanGWAS summary statistics

BioinformaticsNAR Genomics and BioinformaticsPeer-reviewedApr 4, 2024

DeepMicroClass sorts metagenomes into prokaryotes, eukaryotes and viruses, with marine applications

Shengwei Hou, Tianqi Tang, Siliangyu Cheng…·Fengzhu Sun

DeepMicroClass classifies metagenomic contigs into five sequence classes with high accuracy, outperforming existing methods in marine metagenomic analysis.

31 citations

metagenomicsdeep learning