Fang‐Xiang Wu

17.2k total citations · 2 hit papers
443 papers, 12.2k citations indexed

About

Fang‐Xiang Wu is a scholar working on Molecular Biology, Computational Theory and Mathematics and Cancer Research. According to data from OpenAlex, Fang‐Xiang Wu has authored 443 papers receiving a total of 12.2k indexed citations (citations by other indexed papers that have themselves been cited), including 355 papers in Molecular Biology, 80 papers in Computational Theory and Mathematics and 53 papers in Cancer Research. Recurrent topics in Fang‐Xiang Wu's work include Bioinformatics and Genomic Networks (171 papers), Machine Learning in Bioinformatics (98 papers) and Gene expression and cancer classification (86 papers). Fang‐Xiang Wu is often cited by papers focused on Bioinformatics and Genomic Networks (171 papers), Machine Learning in Bioinformatics (98 papers) and Gene expression and cancer classification (86 papers). Fang‐Xiang Wu collaborates with scholars based in Canada, China and United States. Fang‐Xiang Wu's co-authors include Jianxin Wang, Yi Pan, Min Li, Xiujuan Lei, Yaohang Li, Yu Tang, Jin Liu, Wenjun Zhang, Min Zeng and Wei Lan and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Fang‐Xiang Wu

426 papers receiving 11.9k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks

2014 870 citations Min Li, Jianxin Wang et al. profile →
Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm

2016 329 citations Jianxin Wang, Fang‐Xiang Wu et al. Bioinformatics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Fang‐Xiang Wu Canada	59	8.0k	2.6k	2.2k	1.4k	878	443	12.2k
Jianxin Wang China	69	10.3k 1.3×	4.0k 1.6×	2.2k 1.0×	2.1k 1.5×	1.4k 1.6×	1.1k	22.1k
Yi Pan United States	67	6.6k 0.8×	3.2k 1.2×	1.4k 0.6×	2.6k 1.8×	1.6k 1.9×	615	16.9k
Min Li China	53	7.3k 0.9×	2.8k 1.1×	1.4k 0.6×	927 0.6×	354 0.4×	489	10.8k
Yvan Saeys Belgium	49	8.5k 1.1×	553 0.2×	1.1k 0.5×	2.1k 1.4×	1.0k 1.2×	196	16.4k
Quan Zou China	76	15.1k 1.9×	2.4k 0.9×	3.7k 1.7×	2.2k 1.5×	592 0.7×	554	20.1k
Luonan Chen China	66	9.6k 1.2×	1.5k 0.6×	1.3k 0.6×	1.5k 1.0×	265 0.3×	507	15.3k
Xin Gao Saudi Arabia	56	5.9k 0.7×	930 0.4×	995 0.5×	1.5k 1.0×	782 0.9×	550	12.0k
Hiroaki Kitano Japan	52	8.7k 1.1×	1.5k 0.6×	531 0.2×	1.6k 1.1×	955 1.1×	285	15.6k
Zhongming Zhao United States	63	9.1k 1.1×	1.1k 0.4×	3.1k 1.4×	503 0.3×	244 0.3×	535	15.1k
Olga G. Troyanskaya United States	54	11.3k 1.4×	593 0.2×	1.3k 0.6×	1.3k 0.9×	419 0.5×	153	15.8k

Countries citing papers authored by Fang‐Xiang Wu

Since Specialization

Citations

This map shows the geographic impact of Fang‐Xiang Wu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fang‐Xiang Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fang‐Xiang Wu more than expected).

Fields of papers citing papers by Fang‐Xiang Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fang‐Xiang Wu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fang‐Xiang Wu. The network helps show where Fang‐Xiang Wu may publish in the future.

Co-authorship network of co-authors of Fang‐Xiang Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Fang‐Xiang Wu. A scholar is included among the top collaborators of Fang‐Xiang Wu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fang‐Xiang Wu. Fang‐Xiang Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Fu, Minghan, et al.. (2024). SG-Fusion: A swin-transformer and graph convolution-based multi-modal deep neural network for glioma prognosis. Artificial Intelligence in Medicine. 157. 102972–102972. 7 indexed citations

Liu, Yajun, Fan Zhang, Yulian Ding, et al.. (2024). MRDPDA: A multi‐Laplacian regularized deepFM model for predicting piRNA‐disease associations. Journal of Cellular and Molecular Medicine. 28(17). e70046–e70046. 2 indexed citations

Fu, Minghan & Fang‐Xiang Wu. (2024). QLABGrad: A Hyperparameter-Free and Convergence-Guaranteed Scheme for Deep Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 38(11). 12072–12081. 5 indexed citations

Zhu, Wen, et al.. (2024). Identifying Associations Between Small Nucleolar RNAs and Diseases via Graph Convolutional Network and Attention Mechanism. IEEE Journal of Biomedical and Health Informatics. 28(12). 7647–7658. 2 indexed citations

Kakodkar, Pramath, Khalid Hamid Musa, Ahmed Shoker, et al.. (2024). The Interplay Between Human Leukocyte Antigen Antibody Profile and COVID-19 Vaccination in Waitlisted Renal Transplant Patients. Archives of Pathology & Laboratory Medicine. 149(1). 20–29. 1 indexed citations

Kakodkar, Pramath, Pouneh Dokouhaki, Fang‐Xiang Wu, et al.. (2023). The role of the HLA allelic repertoire on the clinical severity of COVID-19 in Canadians, living in the Saskatchewan province. Human Immunology. 84(3). 163–171. 7 indexed citations

Wu, Fang‐Xiang, et al.. (2023). Predicting enhancer-promoter interaction based on epigenomic signals. Frontiers in Genetics. 14. 1133775–1133775. 5 indexed citations

Zhang, Yuchen, Xiujuan Lei, Cai Dai, Yi Pan, & Fang‐Xiang Wu. (2023). Identify potential circRNA-disease associations through a multi-objective evolutionary algorithm. Information Sciences. 647. 119437–119437. 10 indexed citations

Zheng, Ruiqing, et al.. (2023). CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity. Bioinformatics. 39(3). 3 indexed citations

10.

Chen, Siqi, Ruiqing Zheng, Luyi Tian, Fang‐Xiang Wu, & Min Li. (2023). A posterior probability based Bayesian method for single-cell RNA-seq data imputation. Methods. 216. 21–38. 2 indexed citations

11.

Xiang, Ju, et al.. (2022). HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure. Briefings in Bioinformatics. 23(3). 10 indexed citations

12.

Li, Wen-Kai, et al.. (2022). Temporal-Spatial Analysis of the Essentiality of Hub Proteins in Protein-Protein Interaction Networks. IEEE Transactions on Network Science and Engineering. 9(5). 3504–3514. 11 indexed citations

13.

Zeng, Min, et al.. (2021). DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding. Briefings in Bioinformatics. 23(1). 65 indexed citations

14.

Xiang, Ju, et al.. (2021). DPCMNE: Detecting Protein Complexes From Protein-Protein Interaction Networks Via Multi-Level Network Embedding. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 19(3). 1592–1602. 32 indexed citations

15.

Zhu, Wen, et al.. (2020). ALSBMF: Predicting lncRNA-Disease Associations by Alternating Least Squares Based on Matrix Factorization. IEEE Access. 8. 26190–26198. 4 indexed citations

16.

Li, Hong‐Dong, Zhimin Zhang, Mengyun Yang, et al.. (2020). IsoResolve: predicting splice isoform functions by integrating gene and isoform-level features with domain adaptation. Bioinformatics. 37(4). 522–530. 9 indexed citations

17.

Liao, Xingyu, et al.. (2019). Computational Approaches for Transcriptome Assembly Based on Sequencing Technologies. Current Bioinformatics. 15(1). 2–16. 9 indexed citations

18.

Lei, Xiujuan, et al.. (2017). Predicting Protein Complexes in Weighted Dynamic PPI Networks Based on ICSC. Complexity. 2017. 1–11. 13 indexed citations

19.

Lei, Xiujuan, et al.. (2014). ABC and IFC: Modules Detection Method for PPI Network. BioMed Research International. 2014. 1–11. 7 indexed citations

20.

Wu, Fang‐Xiang, Wenjun Zhang, & Anthony Kusalik. (2004). State-space model for gene regulatory networks with time delays. 454–455. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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