Author

Yanfu Zhang

University of PittsburghORCID 0000-0002-0183-925XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Signature Assays, Organisms & Methods

Click to filter · or open tag

Assay:CITE-seq· 2 Research Focus:clustering· 2 Assay:scRNA-seq· 2 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Approach:contrastive learning· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Avinash D SahuiD· 2 shared David ArredondoiD· 2 shared Heng HuangiD· 2 shared Kong CheniD· 2 shared Kushal Virupakshappa· 2 shared Luis E. Tafoya· 2 shared Oladimeji Macaulay· 2 shared Richard H. DuerriD· 2 shared

Activity by Year

2020-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioinformatics

Avinash D SahuORCID

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

MedGraphNet: Leveraging Multi-Relational Graph Neural Networks and Text Knowledge for Biomedical Predictions

Oladimeji Macaulay, Michael Servilla, David Arredondo, Kushal Virupakshappa, Yue Hu, Luis E. Tafoya, Yanfu Zhang, Avinash D Sahu

MedGraphNet integrates multi-relational biological data with text knowledge to improve biomedical predictions, outperforming single-relation methods and identifying drug side effects without direct training.

bioRxiv · Sep 24, 2024 · PreprintBioinformatics

LitGene: a transformer-based model that uses contrastive learning to integrate textual information into gene representations

Ala Jararweh, Oladimeji Macaulay, David Arredondo, Olufunmilola Oyebamiji, Yue Hu, Luis E. Tafoya, Yanfu Zhang, Kushal Virupakshappa, Avinash D Sahu

LitGene integrates textual information into gene representations using a transformer-based model with contrastive learning, improving disease risk gene prediction and interpretability.

bioRxiv · Aug 7, 2024 · PreprintBioinformatics

SECANT: a biology-guided semi-supervised method for clustering, classification, and annotation of single-cell multi-omics

Xinjun Wang, Zhongli Xu, Haoran Hu, Xueping Zhou, Yanfu Zhang, Robert Lafyatis, Kong Chen, Heng Huang, Ying Ding, Richard H. Duerr, Wei Chen

SECANT improves clustering and annotation of single-cell multi-omics data by integrating surface protein and RNA information with biology-guided semi-supervised learning.

PNAS Nexus · Aug 19, 2022Peer-reviewedBioinformatics

BREM-SC: A Bayesian Random Effects Mixture Model for Joint Clustering Single Cell Multi-omics Data

Xinjun Wang, Zhe Sun, Yanfu Zhang, Zhongli Xu, Hongyi Xin, Heng Huang, Richard H. Duerr, Kong Chen, Ying Ding, Wei Chen

BREM-SC is a Bayesian model that jointly clusters single-cell transcriptomic and proteomic data, improving accuracy and quantifying clustering uncertainty.

Nucleic Acids Research · Apr 20, 2020Peer-reviewedGenomics