Author

Rui Fan

Nankai University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

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Biology Disciplines

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Bioinformatics· 3 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Approach:deep learning· 2 Approach:experimental assay· 2 Research Focus:benchmarking· 1 Research Focus:cancer & tumor microenvironment· 1 Research Focus:cell signaling & trafficking· 1 Research Focus:drug response / drug discovery· 1 Approach:graph neural network· 1

Frequent Co-Authors

Boming KangiD· 1 shared Cai JiangiD· 1 shared Chunmei CuiiD· 1 shared Chunmei Cui· 1 shared Cuizhe Wang· 1 shared Donald N. FreediD· 1 shared Hanying Feng· 1 shared Haodong CheniD· 1 shared

Activity by Year

2022-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 14 mutual co-author links

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1 shared paperBioinformatics

Boming KangORCID

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

A large-scale foundation model for bulk transcriptomes

Boming Kang, Rui Fan, Meizheng Yi, Chunmei Cui, Qinghua Cui

This paper introduces BulkFormer, a foundation model for bulk transcriptome analysis that outperforms existing models in six downstream tasks.

bioRxiv · Jun 11, 2025 · PreprintBioinformatics

Ovarian cancer cell cluster inhibits pancreatic cancer progression

Maodi Liang, Rui Fan, Cuizhe Wang, Kejian Gong, Xiao Lin, Mengyuan Zhao, Huizi Zhang, Lili Xu, Qin Liu, Yurui Su, Chunmei Cui, Xingkai Liu, Jun Zhang, Qinghua Cui

This study shows that ovarian cancer cell clusters inhibit pancreatic cancer progression more effectively than single cells through inflammation promotion and macrophage polarization.

bioRxiv · Jan 26, 2025 · PreprintCancer Biology

DeepDrug: A general graph-based deep learning framework for drug-drug interactions and drug-target interactions prediction

Qijin Yin, Xusheng Cao, Rui Fan, Qiao Liu, Rui Jiang, Wanwen Zeng

DeepDrug uses graph-based deep learning to predict drug-drug and drug-target interactions, outperforming existing methods and identifying potential SARS-CoV-2 treatments.

Quantitative Biology · Mar 22, 2023Peer-reviewedBioinformatics

Processing UMI Datasets at High Accuracy and Efficiency with the Sentieon ctDNA Analysis Pipeline

Jinnan Hu, Cai Jiang, Yu S. Huang, Haodong Chen, Hanying Feng, Donald N. Freed, Yan Dong Qu, Rui Fan, Zhencheng Su, Weizhi Chen

The paper introduces the Sentieon ctDNA pipeline for accurate and efficient small somatic variant calling from ctDNA sequencing data, showing improved accuracy over existing methods.

bioRxiv · Jun 3, 2022 · PreprintBioinformatics