Zhenran Jiang

429 total citations
34 papers, 287 citations indexed

About

Zhenran Jiang is a scholar working on Molecular Biology, Computational Theory and Mathematics and Pharmacology. According to data from OpenAlex, Zhenran Jiang has authored 34 papers receiving a total of 287 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Molecular Biology, 17 papers in Computational Theory and Mathematics and 4 papers in Pharmacology. Recurrent topics in Zhenran Jiang's work include Computational Drug Discovery Methods (17 papers), Bioinformatics and Genomic Networks (15 papers) and Machine Learning in Bioinformatics (7 papers). Zhenran Jiang is often cited by papers focused on Computational Drug Discovery Methods (17 papers), Bioinformatics and Genomic Networks (15 papers) and Machine Learning in Bioinformatics (7 papers). Zhenran Jiang collaborates with scholars based in China and United States. Zhenran Jiang's co-authors include Yanhong Zhou, Yan Yan, Meiyue Song, Jun Fan, Liming Xiao, Mingyao Liu, Weiming Yu, Jing Yang, Mingyuan Xin and Xuejun Chen and has published in prestigious journals such as Environmental Science & Technology, PLoS ONE and Scientific Reports.

In The Last Decade

Zhenran Jiang

34 papers receiving 279 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Zhenran Jiang China 13 221 98 30 19 17 34 287
Bulat Zagidullin Finland 4 155 0.7× 161 1.6× 22 0.7× 18 0.9× 16 0.9× 7 235
Hansaim Lim United States 9 186 0.8× 136 1.4× 23 0.8× 18 0.9× 8 0.5× 15 239
Joseph Saad Finland 3 158 0.7× 109 1.1× 15 0.5× 17 0.9× 19 1.1× 8 213
Xin‐Yi Chu China 12 245 1.1× 73 0.7× 20 0.7× 14 0.7× 24 1.4× 23 337
Irina Persikova United States 3 160 0.7× 73 0.7× 26 0.9× 31 1.6× 7 0.4× 5 259
Jinxian Wang China 5 227 1.0× 220 2.2× 23 0.8× 29 1.5× 18 1.1× 7 304
Vigneshwari Subramanian United Kingdom 8 243 1.1× 210 2.1× 12 0.4× 27 1.4× 17 1.0× 15 328
Valeria Scardino Argentina 3 121 0.5× 125 1.3× 11 0.4× 15 0.8× 8 0.5× 4 232
Lara Kuhnke Germany 5 154 0.7× 177 1.8× 27 0.9× 15 0.8× 11 0.6× 6 291
Balaguru Ravikumar Finland 7 185 0.8× 175 1.8× 18 0.6× 33 1.7× 8 0.5× 9 264

Countries citing papers authored by Zhenran Jiang

Since Specialization
Citations

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

Fields of papers citing papers by Zhenran Jiang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhenran Jiang

This figure shows the co-authorship network connecting the top 25 collaborators of Zhenran Jiang. A scholar is included among the top collaborators of Zhenran Jiang 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 Zhenran Jiang. Zhenran Jiang 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.
Jiang, Zhenran, et al.. (2024). A systematic method for solving data imbalance in CRISPR off-target prediction tasks. Computers in Biology and Medicine. 178. 108781–108781. 1 indexed citations
2.
Chen, Xi, Zhenran Jiang, Qiancai Liu, et al.. (2024). Global Atlas of Methane Metabolism Marker Genes in Soil. Environmental Science & Technology. 58(21). 9261–9271. 5 indexed citations
3.
Jiang, Zhenran, et al.. (2023). Transformer-based anti-noise models for CRISPR-Cas9 off-target activities prediction. Briefings in Bioinformatics. 24(3). 10 indexed citations
4.
Jiang, Zhenran, et al.. (2022). Effective use of sequence information to predict CRISPR-Cas9 off-target. Computational and Structural Biotechnology Journal. 20. 650–661. 27 indexed citations
5.
Jiang, Zhenran, et al.. (2022). GraphDPA: Predicting drug-pathway associations by graph convolutional networks. Computational Biology and Chemistry. 99. 107719–107719. 2 indexed citations
6.
Chen, Xuejun & Zhenran Jiang. (2021). ISFMDA: Learning Interactions of Selected Features-Based Method for Predicting Potential MicroRNA-Disease Associations. Journal of Computational Biology. 28(12). 1219–1227. 1 indexed citations
7.
Mao, Xiaodan, Yichen Xu, & Zhenran Jiang. (2019). HColonDB: A Database for Human Colon Cancer Research. Journal of Computational Biology. 26(3). 218–224. 1 indexed citations
8.
Jiang, Huiyan, et al.. (2017). Prediction of drug–pathway interaction pairs with a disease-combined LSA-PU-KNN method. Molecular BioSystems. 13(12). 2583–2591. 5 indexed citations
9.
Lu, Jian, Xiaojing Zhou, Ang Chen, et al.. (2017). Plant natural product plumbagin presents potent inhibitory effect on human cytochrome P450 2J2 enzyme. Phytomedicine. 39. 137–145. 14 indexed citations
10.
Zhou, Wenbo, Wenshu Tang, Zhenliang Sun, et al.. (2016). Discovery and Optimization of N-Substituted 2-(4-pyridinyl)thiazole carboxamides against Tumor Growth through Regulating Angiogenesis Signaling Pathways. Scientific Reports. 6(1). 33434–33434. 15 indexed citations
11.
Tian, Xin, Mingyuan Xin, Jian Luo, Mingyao Liu, & Zhenran Jiang. (2016). Identification of Genes Involved in Breast Cancer Metastasis by Integrating Protein–Protein Interaction Information with Expression Data. Journal of Computational Biology. 24(2). 172–182. 2 indexed citations
12.
Xin, Mingyuan, et al.. (2015). DSEP: A Tool Implementing Novel Method to Predict Side Effects of Drugs. Journal of Computational Biology. 22(12). 1108–1117. 13 indexed citations
13.
Song, Meiyue & Zhenran Jiang. (2015). Inferring Association between Compound and Pathway with an Improved Ensemble Learning Method. Molecular Informatics. 34(11-12). 753–760. 6 indexed citations
14.
Song, Meiyue, Yan Yan, & Zhenran Jiang. (2014). Drug–pathway interaction prediction via multiple feature fusion. Molecular BioSystems. 10(11). 2907–2913. 12 indexed citations
15.
Yan, Yan, et al.. (2014). Inferring Novel Indications of Approved Drugs via a Learning Method with Local and Global Consistency. PLoS ONE. 9(9). e107100–e107100. 3 indexed citations
16.
Lu, Feng, et al.. (2011). A bioinformatics e-learning lab for undergraduate students. 356–356. 1 indexed citations
17.
Yu, Weiming, Xuan Cheng, Zhibin Li, & Zhenran Jiang. (2010). Predicting drug‐target interactions based on an improved semi‐supervised learning approach. Drug Development Research. 72(2). 219–224. 12 indexed citations
18.
Li, Dandan, et al.. (2009). The impact of hardware improvement for molecular modeling in a grid environment. Expert Opinion on Drug Discovery. 4(8). 873–877. 1 indexed citations
19.
Jiang, Zhenran. (2008). Protein Function Predictions Based on the Phylogenetic Profile Method. Critical Reviews in Biotechnology. 28(4). 233–238. 10 indexed citations
20.
Jiang, Zhenran, Cuiping Guan, & Yanhong Zhou. (2007). Computational prediction of the coupling specificity of G protein-coupled receptors. Applied Biochemistry and Biotechnology. 141(1). 109–118. 1 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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