Yinxi Wang

1.2k total citations
35 papers, 848 citations indexed

About

Yinxi Wang is a scholar working on Artificial Intelligence, Geophysics and Geochemistry and Petrology. According to data from OpenAlex, Yinxi Wang has authored 35 papers receiving a total of 848 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 10 papers in Geophysics and 9 papers in Geochemistry and Petrology. Recurrent topics in Yinxi Wang's work include AI in cancer detection (12 papers), Geological and Geochemical Analysis (10 papers) and Radiomics and Machine Learning in Medical Imaging (9 papers). Yinxi Wang is often cited by papers focused on AI in cancer detection (12 papers), Geological and Geochemical Analysis (10 papers) and Radiomics and Machine Learning in Medical Imaging (9 papers). Yinxi Wang collaborates with scholars based in China, Sweden and Finland. Yinxi Wang's co-authors include Jun Chen, Wenbo Rao, Jiedong Yang, Kai‐Jun Zhang, Mattias Rantalainen, Johan Hartman, D. Nahon, Enrique Merino, Balázs Ács and Stephanie Robertson and has published in prestigious journals such as Applied Physics Letters, Bioinformatics and Geochimica et Cosmochimica Acta.

In The Last Decade

Yinxi Wang

34 papers receiving 829 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yinxi Wang China 14 290 232 213 168 158 35 848
Li Cao United States 20 96 0.3× 764 3.3× 238 1.1× 61 0.4× 53 0.3× 50 1.8k
Arnaud Goolaerts United States 15 322 1.1× 183 0.8× 873 4.1× 9 0.1× 219 1.4× 22 1.7k
O. Suschny Austria 9 97 0.3× 171 0.7× 89 0.4× 27 0.2× 99 0.6× 29 690
Yuting Zhong China 16 193 0.7× 118 0.5× 615 2.9× 10 0.1× 310 2.0× 59 1.2k
Linlin Li China 17 139 0.5× 95 0.4× 340 1.6× 9 0.1× 60 0.4× 49 808
Kazuo Amano Japan 12 48 0.2× 208 0.9× 318 1.5× 17 0.1× 36 0.2× 93 903
Rongyu Li China 15 40 0.1× 284 1.2× 167 0.8× 6 0.0× 210 1.3× 42 1.0k
Mingyu Zhao China 21 26 0.1× 277 1.2× 160 0.8× 15 0.1× 371 2.3× 65 1.2k
Gianfranco Galli Italy 18 143 0.5× 27 0.1× 319 1.5× 63 0.4× 72 0.5× 65 796
Claire McLeod United States 18 148 0.5× 65 0.3× 452 2.1× 13 0.1× 80 0.5× 35 811

Countries citing papers authored by Yinxi Wang

Since Specialization
Citations

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

Fields of papers citing papers by Yinxi Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yinxi Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Yinxi Wang. A scholar is included among the top collaborators of Yinxi 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 Yinxi Wang. Yinxi 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.
2.
Wang, Yinxi, Henrik Olsson, Bodil Ginnerup Pedersen, et al.. (2025). Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence–Assisted Cancer Diagnosis. Modern Pathology. 38(5). 100715–100715. 1 indexed citations
3.
Wang, Yinxi, Wenwen Sun, Emelié Karlsson, et al.. (2024). Clinical evaluation of deep learning-based risk profiling in breast cancer histopathology and comparison to an established multigene assay. Breast Cancer Research and Treatment. 206(1). 163–175. 4 indexed citations
4.
5.
Wang, Yinxi, et al.. (2024). Validation of an AI-based solution for breast cancer risk stratification using routine digital histopathology images. Breast Cancer Research. 26(1). 123–123. 5 indexed citations
6.
Wang, Yinxi, et al.. (2024). Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer. Breast Cancer Research. 26(1). 17–17. 10 indexed citations
7.
Wang, Yinxi, et al.. (2024). Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images. Breast Cancer Research. 26(1). 90–90. 4 indexed citations
8.
Wang, Yinxi, et al.. (2023). Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information. European Journal of Cancer. 191. 112953–112953. 15 indexed citations
9.
Liu, Bojing, Yinxi Wang, Johan Lindberg, et al.. (2022). Using deep learning to detect patients at risk for prostate cancer despite benign biopsies. iScience. 25(7). 104663–104663. 14 indexed citations
10.
Wang, Yinxi, Kimmo Kartasalo, Balázs Ács, et al.. (2021). Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer. Cancer Research. 81(19). 5115–5126. 41 indexed citations
11.
Wang, Yinxi, Balázs Ács, Stephanie Robertson, et al.. (2021). Improved breast cancer histological grading using deep learning. Annals of Oncology. 33(1). 89–98. 125 indexed citations
12.
Wang, Yinxi, Dan Liu, Huifeng Zhang, et al.. (2020). Double-stranded RNA-induced dopaminergic neuronal loss in the substantia nigra in the presence of Mac1 receptor. Biochemical and Biophysical Research Communications. 533(4). 1148–1154. 3 indexed citations
13.
Liu, Dan, Yujia Zhao, Yun Gao, et al.. (2020). Benzo(a)pyrene exposure induced neuronal loss, plaque deposition, and cognitive decline in APP/PS1 mice. Journal of Neuroinflammation. 17(1). 258–258. 32 indexed citations
14.
Wang, Yinxi, Dan Liu, Huifeng Zhang, et al.. (2017). Ultrafine carbon particles promote rotenone-induced dopamine neuronal loss through activating microglial NADPH oxidase. Toxicology and Applied Pharmacology. 322. 51–59. 16 indexed citations
15.
Rao, Wenbo, Jun Chen, Hongbing Tan, Stephan M. Weise, & Yinxi Wang. (2013). Nd–Sr isotopic and REE geochemical compositions of Late Quaternary deposits in the desert–loess transition, north-central China: Implications for their provenance and past wind systems. Quaternary International. 334-335. 197–212. 14 indexed citations
16.
Feng, Jinfei, et al.. (2011). Source attributions of heavy metals in rice plant along highway in Eastern China. Journal of Environmental Sciences. 23(7). 1158–1164. 94 indexed citations
17.
Wang, Yinxi. (2003). Palaeoweathering Type Rare Earth Element Deposit in Shanxi Determined by Solid Isotope Mass Spectrometry. 6 indexed citations
18.
Ling, Hong‐Fei, et al.. (2003). Sm-Nd Isotope Dating and Geological Implications for the Mesoproterozoic Mamianshan Group in Fujian Province. Gaoxiao dizhi xuebao. 9(4). 566. 6 indexed citations
19.
Weiguo, Sun, et al.. (1994). Sm-Nd isotopic signatures and significance of the Precambrian-Cambrian seawater in China. 37(10). 2 indexed citations
20.
Wang, Yinxi, et al.. (1993). Fossil Sm-Nd Isotopic Dating of the Precambrian-Cambrian Boundary in the Meishucun Section, Jinning, Yunnan, China. Science China Chemistry. 36(4). 463–471. 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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