Siguo Wang

401 total citations
20 papers, 231 citations indexed

About

Siguo Wang is a scholar working on Molecular Biology, Cancer Research and Artificial Intelligence. According to data from OpenAlex, Siguo Wang has authored 20 papers receiving a total of 231 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 3 papers in Cancer Research and 2 papers in Artificial Intelligence. Recurrent topics in Siguo Wang's work include Machine Learning in Bioinformatics (11 papers), RNA and protein synthesis mechanisms (9 papers) and Genomics and Chromatin Dynamics (9 papers). Siguo Wang is often cited by papers focused on Machine Learning in Bioinformatics (11 papers), RNA and protein synthesis mechanisms (9 papers) and Genomics and Chromatin Dynamics (9 papers). Siguo Wang collaborates with scholars based in China, Macao and South Korea. Siguo Wang's co-authors include Qinhu Zhang, Ying He, Zhan‐Heng Chen, Zhen Shen, De-Shuang Huang, De-Shuang Huang, Qi Liu, Xiujuan Lei, Jianqiang Li and Fang‐Xiang Wu and has published in prestigious journals such as Bioinformatics, Frontiers in Microbiology and BMC Bioinformatics.

In The Last Decade

Siguo Wang

20 papers receiving 224 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Siguo Wang China 7 210 22 17 16 15 20 231
CJ Barberan United States 3 135 0.6× 14 0.6× 13 0.8× 16 1.0× 43 2.9× 4 204
Pooya Zakeri Belgium 8 219 1.0× 31 1.4× 14 0.8× 62 3.9× 23 1.5× 10 277
Johannes Linder United States 9 311 1.5× 25 1.1× 38 2.2× 19 1.2× 20 1.3× 13 382
Zhuoxing Shi China 7 157 0.7× 21 1.0× 11 0.6× 9 0.6× 8 0.5× 22 186
Jia Ren United States 7 175 0.8× 16 0.7× 24 1.4× 11 0.7× 17 1.1× 14 217
Dustin Olley United States 5 209 1.0× 26 1.2× 41 2.4× 22 1.4× 29 1.9× 5 261
Zixiang Pan China 5 166 0.8× 33 1.5× 8 0.5× 16 1.0× 42 2.8× 6 221
Theofanis Karaletsos United States 7 139 0.7× 15 0.7× 17 1.0× 9 0.6× 51 3.4× 16 223
Corrado Pancotti Italy 6 184 0.9× 14 0.6× 24 1.4× 17 1.1× 11 0.7× 13 239
Nensi Ikonomi Germany 7 161 0.8× 16 0.7× 17 1.0× 39 2.4× 20 1.3× 20 234

Countries citing papers authored by Siguo Wang

Since Specialization
Citations

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

Fields of papers citing papers by Siguo Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Siguo Wang

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

All Works

20 of 20 papers shown
1.
Shen, Zhen, et al.. (2025). A brief survey of deep learning-based models for CircRNA-protein binding sites prediction. Neurocomputing. 628. 129637–129637. 1 indexed citations
2.
Wang, Siguo, Qinhu Zhang, Yijie Pan, et al.. (2024). Graph pooling for graph-level representation learning: a survey. Artificial Intelligence Review. 58(2). 3 indexed citations
3.
Zhang, Qinhu, et al.. (2024). Cross‐Species Prediction of Transcription Factor Binding by Adversarial Training of a Novel Nucleotide‐Level Deep Neural Network. Advanced Science. 11(36). e2405685–e2405685. 4 indexed citations
4.
Wang, Yanbin, et al.. (2024). scCorrector: a robust method for integrating multi-study single-cell data. Briefings in Bioinformatics. 25(2). 3 indexed citations
5.
Cui, Zhen, et al.. (2024). NPENN: A Noise Perturbation Ensemble Neural Network for Microbiome Disease Phenotype Prediction. IEEE Journal of Biomedical and Health Informatics. 29(3). 2210–2221. 1 indexed citations
6.
Wang, Siguo, et al.. (2023). scInterpreter: a knowledge-regularized generative model for interpretably integrating scRNA-seq data. BMC Bioinformatics. 24(1). 481–481. 1 indexed citations
7.
Wang, Siguo, et al.. (2023). DeepTPpred: A Deep Learning Approach With Matrix Factorization for Predicting Therapeutic Peptides by Integrating Length Information. IEEE Journal of Biomedical and Health Informatics. 27(9). 4611–4622. 5 indexed citations
8.
Shen, Zhen, et al.. (2023). Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network. Frontiers in Genetics. 14. 1283404–1283404. 2 indexed citations
9.
Cui, Zhen, Yan Wu, Qinhu Zhang, et al.. (2023). MV-CVIB: a microbiome-based multi-view convolutional variational information bottleneck for predicting metastatic colorectal cancer. Frontiers in Microbiology. 14. 1238199–1238199. 1 indexed citations
10.
Zhang, Qinhu, Siguo Wang, Ying He, et al.. (2022). Computational prediction and characterization of cell-type-specific and shared binding sites. Bioinformatics. 39(1). 9 indexed citations
11.
Zhang, Qinhu, Ying He, Siguo Wang, et al.. (2022). Base-resolution prediction of transcription factor binding signals by a deep learning framework. PLoS Computational Biology. 18(3). e1009941–e1009941. 16 indexed citations
12.
Wang, Siguo, Qinhu Zhang, Ying He, et al.. (2022). DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes. PLoS Computational Biology. 18(10). e1010572–e1010572. 5 indexed citations
13.
Chen, Zhan‐Heng, Zhu‐Hong You, Qinhu Zhang, et al.. (2022). In silico prediction methods of self-interacting proteins: an empirical and academic survey. Frontiers of Computer Science. 17(3). 6 indexed citations
14.
Wang, Siguo, Qinhu Zhang, Zhen Shen, et al.. (2021). Predicting transcription factor binding sites using DNA shape features based on shared hybrid deep learning architecture. Molecular Therapy — Nucleic Acids. 24. 154–163. 39 indexed citations
15.
Zhang, Qinhu, Siguo Wang, Zhan‐Heng Chen, et al.. (2021). Predicting In-Vitro DNA-Protein Binding With a Spatially Aligned Fusion of Sequence and Shape. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 19(6). 3144–3153. 4 indexed citations
16.
Wang, Siguo, Ying He, Zhan‐Heng Chen, & Qinhu Zhang. (2021). FCNGRU: Locating Transcription Factor Binding Sites by Combing Fully Convolutional Neural Network With Gated Recurrent Unit. IEEE Journal of Biomedical and Health Informatics. 26(4). 1883–1890. 9 indexed citations
17.
Zhang, Qinhu, Siguo Wang, Zhan‐Heng Chen, et al.. (2020). Locating transcription factor binding sites by fully convolutional neural network. Briefings in Bioinformatics. 22(5). 42 indexed citations
18.
He, Ying, Zhen Shen, Qinhu Zhang, Siguo Wang, & De-Shuang Huang. (2020). A survey on deep learning in DNA/RNA motif mining. Briefings in Bioinformatics. 22(4). 63 indexed citations
19.
Lei, Xiujuan, Siguo Wang, & Fang‐Xiang Wu. (2019). Identification of Essential Proteins Based on Improved HITS Algorithm. Genes. 10(2). 177–177. 15 indexed citations
20.
Lei, Xiujuan, Siguo Wang, & Linqiang Pan. (2018). Identifying Essential Proteins in Dynamic PPI Network with Improved FOA. International Journal of Computers Communications & Control. 13(3). 365–382. 2 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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