Boya Ji

597 total citations
29 papers, 372 citations indexed

About

Boya Ji is a scholar working on Molecular Biology, Cancer Research and Computational Theory and Mathematics. According to data from OpenAlex, Boya Ji has authored 29 papers receiving a total of 372 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Molecular Biology, 9 papers in Cancer Research and 5 papers in Computational Theory and Mathematics. Recurrent topics in Boya Ji's work include MicroRNA in disease regulation (8 papers), Cancer-related molecular mechanisms research (7 papers) and Machine Learning in Bioinformatics (5 papers). Boya Ji is often cited by papers focused on MicroRNA in disease regulation (8 papers), Cancer-related molecular mechanisms research (7 papers) and Machine Learning in Bioinformatics (5 papers). Boya Ji collaborates with scholars based in China, Thailand and Saudi Arabia. Boya Ji's co-authors include Zhu‐Hong You, Leon Wong, Lei Wang, Li Cheng, Shaoliang Peng, Xiaorui Su, Bo-Wei Zhao, De-Shuang Huang, Meijie Zhang and Yuming Liu and has published in prestigious journals such as Scientific Reports, Expert Systems with Applications and BMC Bioinformatics.

In The Last Decade

Boya Ji

25 papers receiving 363 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Boya Ji China	11	254	112	98	61	31	29	372
Hua Shi China	10	271 1.1×	115 1.0×	34 0.3×	45 0.7×	24 0.8×	45	465
Aanchal Mongia India	9	235 0.9×	65 0.6×	60 0.6×	49 0.8×	6 0.2×	17	339
Jihwan Ha South Korea	9	264 1.0×	196 1.8×	21 0.2×	79 1.3×	10 0.3×	16	410
Huaicheng Sun China	7	233 0.9×	93 0.8×	82 0.8×	31 0.5×	18 0.6×	10	366
Junlin Xu China	17	516 2.0×	214 1.9×	187 1.9×	77 1.3×	26 0.8×	53	743
Leon Wong China	16	574 2.3×	208 1.9×	232 2.4×	54 0.9×	8 0.3×	34	676
Zhen-Hao Guo China	13	342 1.3×	186 1.7×	124 1.3×	27 0.4×	11 0.4×	26	460
Changcheng Lu China	8	336 1.3×	49 0.4×	281 2.9×	51 0.8×	21 0.7×	11	463
Peiran Jiang China	9	369 1.5×	50 0.4×	124 1.3×	76 1.2×	114 3.7×	12	583

Countries citing papers authored by Boya Ji

Since Specialization

Citations

This map shows the geographic impact of Boya Ji'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 Boya Ji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Boya Ji more than expected).

Fields of papers citing papers by Boya Ji

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Boya Ji. 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 Boya Ji. The network helps show where Boya Ji may publish in the future.

Co-authorship network of co-authors of Boya Ji

This figure shows the co-authorship network connecting the top 25 collaborators of Boya Ji. A scholar is included among the top collaborators of Boya Ji 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 Boya Ji. Boya Ji is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Liu, Zhengyu, et al.. (2025). TransEHR: Alignment-free electronic health records continual learning across feature spaces. Expert Systems with Applications. 296. 129020–129020. 1 indexed citations

Ji, Boya, et al.. (2025). Accurate Nucleic Acid-Binding Residue Identification Based Domain-Adaptive Protein Language Model and Explainable Geometric Deep Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 39(1). 1004–1012.

Ji, Boya, et al.. (2024). A hierarchical GNN across semantic and topological domains for predicting circRNA-microRNA interactions. Knowledge-Based Systems. 304. 112549–112549. 1 indexed citations

Ji, Boya, Xiaoqi Wang, Xiang Wang, Liwen Xu, & Shaoliang Peng. (2024). scDCA: deciphering the dominant cell communication assembly of downstream functional events from single-cell RNA-seq data. Briefings in Bioinformatics. 26(1). 2 indexed citations

Bai, Liang, et al.. (2024). SAE-Impute: imputation for single-cell data via subspace regression and auto-encoders. BMC Bioinformatics. 25(1). 317–317. 2 indexed citations

Ji, Boya, et al.. (2024). SeqCCC: A Sequence-Informed Ligand-Receptor Interaction Prediction Method for Cell-Cell Communication Inference. 534–539. 1 indexed citations

Ji, Boya, et al.. (2024). A multi-source molecular network representation model for protein–protein interactions prediction. Scientific Reports. 14(1). 6184–6184. 7 indexed citations

Hong, Xia, et al.. (2024). CellMsg: graph convolutional networks for ligand–receptor-mediated cell-cell communication analysis. Briefings in Bioinformatics. 26(1). 1 indexed citations

Ji, Boya, et al.. (2024). MUSCLE: multi-view and multi-scale attentional feature fusion for microRNA–disease associations prediction. Briefings in Bioinformatics. 25(3). 8 indexed citations

10.

Ji, Boya, et al.. (2024). MHGTMDA: Molecular heterogeneous graph transformer based on biological entity graph for miRNA-disease associations prediction. Molecular Therapy — Nucleic Acids. 35(1). 102139–102139. 9 indexed citations

11.

Bai, Liang, Liwen Xu, Boya Ji, Shulin Wang, & Shaoliang Peng. (2024). MRF-XGBLC: Large-scale gene regulatory network inference based on multi-model fusion. 485–490.

12.

Ji, Boya, Wenjuan Liu, Yannan Liu, et al.. (2023). HyperVR: a hybrid deep ensemble learning approach for simultaneously predicting virulence factors and antibiotic resistance genes. NAR Genomics and Bioinformatics. 5(1). lqad012–lqad012. 12 indexed citations

13.

Zheng, Kai, Xinlu Zhang, Lei Wang, et al.. (2022). SPRDA: a link prediction approach based on the structural perturbation to infer disease-associated Piwi-interacting RNAs. Briefings in Bioinformatics. 24(1). 33 indexed citations

14.

Lian, Wang, et al.. (2022). Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma. Biomedical Signal Processing and Control. 77. 103824–103824. 29 indexed citations

15.

Ji, Boya, et al.. (2022). MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images. Health Information Science and Systems. 10(1). 4–4. 8 indexed citations

16.

Ji, Boya, Zhu‐Hong You, Yi Wang, Zhengwei Li, & Leon Wong. (2021). DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding. iScience. 24(6). 102455–102455. 17 indexed citations

17.

Su, Xiaorui, Zhu‐Hong You, Lei Wang, et al.. (2021). SANE: A sequence combined attentive network embedding model for COVID-19 drug repositioning. Applied Soft Computing. 111. 107831–107831. 23 indexed citations

18.

Zhang, Peng, Fei Xia, Boya Ji, et al.. (2021). FEDI: Few-shot learning based on Earth Mover's Distance algorithm combined with deep residual network to identify diabetic retinopathy. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 1032–1036. 3 indexed citations

19.

You, Zhu‐Hong, et al.. (2020). Prediction of lncRNA-disease associations via an embedding learning HOPE in heterogeneous information networks. Molecular Therapy — Nucleic Acids. 23. 277–285. 23 indexed citations

20.

Ji, Boya, Zhu‐Hong You, Zhan‐Heng Chen, Leon Wong, & Hai-Cheng Yi. (2020). NEMPD: a network embedding-based method for predicting miRNA-disease associations by preserving behavior and attribute information. BMC Bioinformatics. 21(1). 401–401. 5 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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