ArticlebioRxiv : the preprint server for biology2025
GatorSC: Multi-Scale Cell and Gene Graphs with Mixture-of-Experts Fusion for Single-Cell Transcriptomics.
Yuxi Liu, Zhenhao Zhang, Mufan Qiu, Song Wang, Flora D Salim, Jun Shen, Tianlong Chen, Imran Razzak, Jiang Bian
Abstract readPreprint
In one paragraphArticle in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from itWhat it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
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4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
9 authors.
Yuxi LiuBiostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, 46202, IN, USA.ORCID 0000-0003-1265-7926 Zhenhao ZhangCollege of Life Sciences, Northwest A&F University, Yangling, 712100, Shaanxi, China.
Mufan QiuDepartment of Computer Science, The University of North Carolina at Chapel Hill, Chapel Hill, 27599, NC, USA.
Song WangDepartment of Computer Science, University of Central Florida, Orlando, 32816, FL, USA.
Flora D SalimSchool of Computer Science and Engineering, University of New South Wales, Sydney, 2052, NSW, Australia.
Jun ShenSchool of Computing and Information Technology, University of Wollongong, Wollongong, 2522, NSW, Australia.
Tianlong ChenDepartment of Computer Science, The University of North Carolina at Chapel Hill, Chapel Hill, 27599, NC, USA.
Imran RazzakDepartment of Computational Biology, Muhammad bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE.
Jiang BianBiostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, 46202, IN, USA.ORCID 0000-0002-2238-5429 Funding
Computational Drug Repurposing for AD/ADRD with Integrative Analysis of Real World Data and Biomedical KnowledgeR01AG076234 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI Jiang Bian, Fei Wang · 2022 to 2026
$3.8MAn end-to-end informatics framework to study Multiple Chronic Conditions (MCC)'s impact on Alzheimer's disease using harmonized electronic health recordsR01AG083039 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI BIAN, JIANG, JIANG, XIAOQIAN · 2023 to 2025
$3.4MEligibility criteria design for Alzheimer's trials with real-world data and explainable AIR01AG080991 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI Jiang Bian, Fei Wang · 2023 to 2026
$3.1MDisparities of Alzheimer's disease progression in Sexual Minority IndividualsR01AG080624 · NIA · UNIVERSITY OF FLORIDA · PI Jiang Bian, Yi Guo · 2023 to 2026
$3.1MPost-Acute Sequelae of SARS-CoV-2 Infection and Subsequent Disease Progression in Individuals with AD/ADRD: Influence of the Social and Environmental Determinants of HealthRF1AG084178 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI BIAN, JIANG, HU, HUI · 2023 to 2023
$2.6MAI-ADRD: Accelerating interventions of AD/ADRD via Machine learning methodsRF1AG077820 · NIA · UNIVERSITY OF PENNSYLVANIA · PI BIAN, JIANG, CHEN, YONG · 2023 to 2023
$2.3MNIA NIH HHS R01 AG076234NIA NIH HHS R01 AG080624NIA NIH HHS R01 AG080991NIA NIH HHS R01 AG083039NIA NIH HHS RF1 AG077820NIA NIH HHS RF1 AG084178
6 · The paper itselfAbstract
Single-cell RNA sequencing (scRNA-seq) enables high-resolution characterization of cellular heterogeneity, but its rich, complementary structure across cells and genes remains underexploited, especially in the presence of technical noise and sparsity. Effectively leveraging this multi-scale structure is essentially an information fusion problem that requires integrating heterogeneous graph-based views of cells and genes into robust low-dimensional representations. In this paper, we introduce
Indexed as
Cell clusteringCell type annotationContrastive learningGene expression imputationMixture-of-ExpertsscRNA-seq data
Identifiers
PMID41409156
PMCPMC12707292
What OpenQuestion holds
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LicenceCC BY-NC-ND
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