Fang Jin

8.1k total citations · 1 hit paper
166 papers, 5.6k citations indexed

About

Fang Jin is a scholar working on Molecular Biology, Oncology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Fang Jin has authored 166 papers receiving a total of 5.6k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Molecular Biology, 34 papers in Oncology and 30 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Fang Jin's work include Advanced biosensing and bioanalysis techniques (14 papers), Cancer Cells and Metastasis (13 papers) and Functional Brain Connectivity Studies (11 papers). Fang Jin is often cited by papers focused on Advanced biosensing and bioanalysis techniques (14 papers), Cancer Cells and Metastasis (13 papers) and Functional Brain Connectivity Studies (11 papers). Fang Jin collaborates with scholars based in China, United States and South Korea. Fang Jin's co-authors include Jan M. van Deursen, John Lowengrub, Caroline H. Damsky, Vittorio Cristini, Steven M. Wise, Yao-Li Chuang, Hermann B. Frieboes, Karthik B. Jeganathan, Darren J. Baker and Duško Ilić and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Journal of Clinical Investigation.

In The Last Decade

Fang Jin

147 papers receiving 5.5k citations

Hit Papers

Inflammation‐Responsive Hydrogel Accelerates Diabetic Wou... 2024 2026 2025 2024 10 20 30 40

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fang Jin China 40 2.2k 918 894 706 695 166 5.6k
Elaine L. Bearer United States 33 1.3k 0.6× 324 0.4× 786 0.9× 318 0.5× 386 0.6× 104 3.6k
Peter Canoll United States 59 6.2k 2.8× 1.8k 2.0× 1.2k 1.3× 865 1.2× 889 1.3× 232 12.5k
Frank Winkler Germany 43 3.4k 1.5× 2.3k 2.5× 703 0.8× 1.1k 1.6× 599 0.9× 163 8.2k
M. Waleed Gaber United States 25 1.3k 0.6× 1.1k 1.2× 169 0.2× 443 0.6× 393 0.6× 71 4.1k
Jeffrey N. Bruce United States 61 3.8k 1.7× 2.3k 2.5× 424 0.5× 1.1k 1.6× 1.1k 1.5× 316 12.6k
Michelle A. Digman United States 54 5.0k 2.2× 511 0.6× 1.2k 1.3× 1.2k 1.7× 394 0.6× 163 8.3k
Mitsutoshi Nakada Japan 47 3.6k 1.6× 1.3k 1.5× 1.0k 1.2× 460 0.7× 561 0.8× 324 7.8k
Steven Brem United States 45 2.9k 1.3× 1.5k 1.6× 304 0.3× 716 1.0× 1.3k 1.8× 193 7.7k
Jian Chen China 44 4.4k 2.0× 1.7k 1.9× 483 0.5× 613 0.9× 144 0.2× 195 8.7k
David T. Shima United Kingdom 45 7.8k 3.5× 1.2k 1.3× 2.2k 2.4× 677 1.0× 2.0k 2.9× 83 12.0k

Countries citing papers authored by Fang Jin

Since Specialization
Citations

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

Fields of papers citing papers by Fang Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fang Jin

This figure shows the co-authorship network connecting the top 25 collaborators of Fang Jin. A scholar is included among the top collaborators of Fang Jin 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 Jin. Fang Jin 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
4.
Wang, Yu, Yirong Jiang, Jie Zhang, et al.. (2024). Pick-up single-cell proteomic analysis for quantifying up to 3000 proteins in a Mammalian cell. Nature Communications. 15(1). 1279–1279. 34 indexed citations
5.
6.
Chen, Qiuying, Lu Zhang, Xiaokai Mo, et al.. (2021). Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: a systematic review and meta-analysis. European Journal of Nuclear Medicine and Molecular Imaging. 49(1). 345–360. 47 indexed citations
7.
Yan, Jing, Bin Zhang, Shuaitong Zhang, et al.. (2021). Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients. npj Precision Oncology. 5(1). 71 indexed citations
8.
Zhang, Yongxin, Xiaofei Lv, Jiliang Qiu, et al.. (2021). Deep Learning With 3D Convolutional Neural Network for Noninvasive Prediction of Microvascular Invasion in Hepatocellular Carcinoma. Journal of Magnetic Resonance Imaging. 54(1). 134–143. 58 indexed citations
9.
Malo, Courtney S., Katayoun Ayasoufi, Cori E. Fain, et al.. (2020). Conditional Silencing of H-2Db Class I Molecule Expression Modulates the Protective and Pathogenic Kinetics of Virus-Antigen–Specific CD8 T Cell Responses during Theiler's Virus Infection. The Journal of Immunology. 205(5). 1228–1238. 7 indexed citations
10.
Zhang, Lu, Xiangjun Wu, Jing Liu, et al.. (2020). MRI‐Based Deep‐Learning Model for Distant Metastasis‐Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma. Journal of Magnetic Resonance Imaging. 53(1). 167–178. 34 indexed citations
11.
Li, Wanming, Guan-Sheng Du, Hansang Cho, et al.. (2018). Droplet Array-Based 3D Coculture System for High-Throughput Tumor Angiogenesis Assay. Analytical Chemistry. 90(5). 3253–3261. 39 indexed citations
12.
Zhang, Kai, Yu Lin, Shuai Li, et al.. (2017). Location study of hand motor cortex by using navigated transcranial magnetic stimulation. Zhonghua shiyan waike zazhi. 34(8). 1281–1284.
13.
Li, Wanming & Fang Jin. (2016). Quantitative Multi-targeted Imaging of Metastatic Colorectal Cancer Cells Using Aptamer Probes in Combination†. Gaodeng xuexiao huaxue xuebao. 37(7). 1262. 1 indexed citations
14.
Ma, Xiaofen, Guihua Jiang, Shumei Li, et al.. (2014). Fraction amplitude of low-frequency fluctuation in irritable bowel syndrome patients: a restingstate fMRI study. Chinese Journal of Neuromedicine. 13(3). 292–295. 1 indexed citations
15.
Shin, Sook, Tae‐Dong Kim, Fang Jin, et al.. (2009). Induction of Prostatic Intraepithelial Neoplasia and Modulation of Androgen Receptor by ETS Variant 1/ETS-Related Protein 81. Cancer Research. 69(20). 8102–8110. 67 indexed citations
16.
Bearer, Elaine L., John Lowengrub, Hermann B. Frieboes, et al.. (2009). Multiparameter Computational Modeling of Tumor Invasion. Cancer Research. 69(10). 4493–4501. 98 indexed citations
17.
Jin, Fang. (2008). Relationship between Free Propofol Concentrations of Different Delivery Systems for Injection and in vitro Hemolytic Activities. Chinese Journal of Pharmaceuticals. 1 indexed citations
18.
Bradley, Sarah V., Mitchell R. Smith, Teresa S. Hyun, et al.. (2007). Aberrant Huntingtin Interacting Protein 1 in Lymphoid Malignancies. Cancer Research. 67(18). 8923–8931. 25 indexed citations
19.
Jin, Fang. (2007). Progress of Freon Propellant Replacement in Metered Dose Inhaler. Chinese Journal of Pharmaceuticals.
20.
Ilić, Duško, Olga Genbačev, Fang Jin, et al.. (2001). Plasma Membrane-Associated pY397FAK Is a Marker of Cytotrophoblast Invasion in Vivo and in Vitro. American Journal Of Pathology. 159(1). 93–108. 85 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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