Jingwen Yan

2.6k total citations · 1 hit paper
76 papers, 1.6k citations indexed

About

Jingwen Yan is a scholar working on Molecular Biology, Genetics and Cognitive Neuroscience. According to data from OpenAlex, Jingwen Yan has authored 76 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Molecular Biology, 30 papers in Genetics and 19 papers in Cognitive Neuroscience. Recurrent topics in Jingwen Yan's work include Bioinformatics and Genomic Networks (38 papers), Genetic Associations and Epidemiology (26 papers) and Gene expression and cancer classification (23 papers). Jingwen Yan is often cited by papers focused on Bioinformatics and Genomic Networks (38 papers), Genetic Associations and Epidemiology (26 papers) and Gene expression and cancer classification (23 papers). Jingwen Yan collaborates with scholars based in United States, China and United Kingdom. Jingwen Yan's co-authors include Andrew J. Saykin, Li Shen, Shannon L. Risacher, Heng Huang, Kwangsik Nho, Yuanyuan Liu, Jiangyu Ye, Jialiang Liang, Xinmiao Huang and Zhiwei Zhao and has published in prestigious journals such as Bioinformatics, PLoS ONE and Analytical Chemistry.

In The Last Decade

Jingwen Yan

74 papers receiving 1.5k citations

Hit Papers

1 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.

A review of the formation of Cr(VI) via Cr(III) oxidation in soils and groundwater

2021 268 citations Jingwen Yan et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jingwen Yan United States	21	720	260	253	185	182	76	1.6k
Xiaojin Li China	27	813 1.1×	224 0.9×	128 0.5×	77 0.4×	101 0.6×	148	2.2k
Burcu F. Darst United States	18	633 0.9×	58 0.2×	349 1.4×	132 0.7×	61 0.3×	44	1.7k
Wei Luo China	29	662 0.9×	312 1.2×	208 0.8×	98 0.5×	258 1.4×	217	2.6k
Xiaotong Wang China	27	843 1.2×	72 0.3×	80 0.3×	76 0.4×	41 0.2×	173	2.5k
Huiran Zhang China	21	498 0.7×	224 0.9×	33 0.1×	83 0.4×	127 0.7×	96	1.6k
Keiko Imamura Japan	22	580 0.8×	67 0.3×	63 0.2×	135 0.7×	135 0.7×	90	1.7k
Fahad Saeed United States	18	527 0.7×	252 1.0×	60 0.2×	50 0.3×	97 0.5×	78	1.5k
Wen Zhong China	22	253 0.4×	232 0.9×	221 0.9×	19 0.1×	27 0.1×	72	1.5k
Ju Wang China	26	1.1k 1.5×	47 0.2×	183 0.7×	59 0.3×	40 0.2×	154	2.2k
Li Fan China	27	681 0.9×	35 0.1×	85 0.3×	79 0.4×	37 0.2×	87	1.9k

Countries citing papers authored by Jingwen Yan

Since Specialization

Citations

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

Fields of papers citing papers by Jingwen Yan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jingwen Yan

This figure shows the co-authorship network connecting the top 25 collaborators of Jingwen Yan. A scholar is included among the top collaborators of Jingwen Yan 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 Jingwen Yan. Jingwen Yan 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

1.

Yan, Jingwen, Yiying Wu, Yuyu Zhang, et al.. (2025). A Novel Lithium Ion-Assisted TPP Approach for Enhanced Target Discovery. Analytical Chemistry. 97(48). 26326–26334.

2.

He, Bing, et al.. (2024). Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation. BioData Mining. 17(1). 50–50. 2 indexed citations

3.

Nho, Kwangsik, et al.. (2023). Decoding the gene subnetworks prioritized in prediction with GLRP in Alzheimer’s Disease. Alzheimer s & Dementia. 19(S1). 1 indexed citations

4.

Gorijala, Priyanka, et al.. (2022). Integrative analysis of eQTL and GWAS summary statistics reveals transcriptomic alteration in Alzheimer brains. BMC Medical Genomics. 15(S2). 93–93. 5 indexed citations

5.

He, Bing, et al.. (2022). Gene co-expression changes underlying the functional connectomic alterations in Alzheimer’s disease. BMC Medical Genomics. 15(S2). 92–92. 2 indexed citations

6.

Zhang, Lu, Li Wang, Jean Gao, et al.. (2021). Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment. Medical Image Analysis. 72. 102082–102082. 51 indexed citations

7.

Salama, Paul, Sha Cao, Kwangsik Nho, et al.. (2020). Differential co-expression analysis reveals early stage transcriptomic decoupling in alzheimer’s disease. PMC. 1 indexed citations

8.

Salama, Paul, et al.. (2020). Differential co-expression analysis reveals early stage transcriptomic decoupling in alzheimer’s disease. BMC Medical Genomics. 13(S5). 53–53. 2 indexed citations

9.

Yao, Xiaohui, Shan Cong, Jingwen Yan, et al.. (2019). Regional imaging genetic enrichment analysis. Bioinformatics. 36(8). 2554–2560. 16 indexed citations

10.

Yan, Jingwen, Kefei Liu, Enrico Amico, et al.. (2018). Joint Exploration and Mining of Memory-Relevant Brain Anatomic and Connectomic Patterns via a Three-Way Association Model. PMC. 2 indexed citations

11.

Yan, Jingwen, Shannon L. Risacher, Li Shen, & Andrew J. Saykin. (2018). Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data. PMC. 44 indexed citations

12.

Amico, Enrico, Paul Salama, Yu‐Chien Wu, et al.. (2018). Heritability Estimation of Reliable Connectomic Features. Lecture notes in computer science. 11083. 58–66. 4 indexed citations

13.

Liu, Kefei, Xiaohui Yao, Jingwen Yan, et al.. (2017). Transcriptome-Guided Imaging Genetic Analysis via a Novel Sparse CCA Algorithm. Lecture notes in computer science. 10551. 220–229. 5 indexed citations

14.

Wang, Cong, Jin Li, Qiushi Zhang, et al.. (2017). Genome-wide network-based pathway analysis of CSF t-tau/Aβ1-42 ratio in the ADNI cohort. BMC Genomics. 18(1). 421–421. 15 indexed citations

15.

Yao, Xiaohui, Jingwen Yan, Sungeun Kim, et al.. (2015). Two-Dimensional Enrichment Analysis for Mining High-Level Imaging Genetic Associations. Lecture notes in computer science. 9250. 115–124. 2 indexed citations

16.

Gao, Hongchang, Chengtao Cai, Jingwen Yan, et al.. (2015). Identifying Connectome Module Patterns via New Balanced Multi-graph Normalized Cut. Lecture notes in computer science. 9350. 169–176. 3 indexed citations

17.

Yan, Jingwen, Sungeun Kim, Kwangsik Nho, et al.. (2015). Hippocampal transcriptome-guided genetic analysis of correlated episodic memory phenotypes in Alzheimer's disease. IUScholarWorks (Indiana University). 1 indexed citations

18.

Yan, Jingwen, Taiyong Li, Hua Wang, et al.. (2014). Cortical surface biomarkers for predicting cognitive outcomes using group l2,1 norm. Neurobiology of Aging. 36. S185–S193. 35 indexed citations

19.

Li, Taiyong, et al.. (2013). Interactive object extraction by merging regions with k-global maximal similarity. Neurocomputing. 120. 610–623. 5 indexed citations

20.

Wang, Hua, Feiping Nie, Heng Huang, et al.. (2012). High-Order Multi-Task Feature Learning to Identify Longitudinal Phenotypic Markers for Alzheimer's Disease Progression Prediction. Neural Information Processing Systems. 25. 1277–1285. 58 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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