Chong-Yaw Wee

3.1k total citations
37 papers, 2.3k citations indexed

About

Chong-Yaw Wee is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Psychiatry and Mental health. According to data from OpenAlex, Chong-Yaw Wee has authored 37 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Cognitive Neuroscience, 15 papers in Radiology, Nuclear Medicine and Imaging and 7 papers in Psychiatry and Mental health. Recurrent topics in Chong-Yaw Wee's work include Functional Brain Connectivity Studies (23 papers), Neural dynamics and brain function (11 papers) and Advanced MRI Techniques and Applications (10 papers). Chong-Yaw Wee is often cited by papers focused on Functional Brain Connectivity Studies (23 papers), Neural dynamics and brain function (11 papers) and Advanced MRI Techniques and Applications (10 papers). Chong-Yaw Wee collaborates with scholars based in United States, China and South Korea. Chong-Yaw Wee's co-authors include Dinggang Shen, Pew‐Thian Yap, Daoqiang Zhang, Heung‐Il Suk, Biao Jie, Guy G. Potter, Kevin Denny, Kathleen A. Welsh‐Bohmer, Jeffrey N. Browndyke and Seong‐Whan Lee and has published in prestigious journals such as PLoS ONE, NeuroImage and Scientific Reports.

In The Last Decade

Chong-Yaw Wee

37 papers receiving 2.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chong-Yaw Wee United States 24 1.5k 756 484 362 289 37 2.3k
Jonas Richiardi Switzerland 23 1.3k 0.9× 710 0.9× 290 0.6× 113 0.3× 254 0.9× 78 2.5k
Nichols Thomas United Kingdom 3 1.5k 1.0× 813 1.1× 242 0.5× 186 0.5× 132 0.5× 7 2.5k
Liang Zhan United States 24 941 0.6× 1.0k 1.3× 294 0.6× 137 0.4× 163 0.6× 120 2.0k
Guillaume Auzias France 16 667 0.5× 467 0.6× 412 0.9× 307 0.8× 173 0.6× 50 1.4k
Tracy H. Wang United States 15 1.1k 0.7× 486 0.6× 269 0.6× 340 0.9× 282 1.0× 19 2.1k
Biao Jie China 22 835 0.6× 383 0.5× 274 0.6× 248 0.7× 248 0.9× 62 1.4k
Iman Beheshti Canada 22 635 0.4× 400 0.5× 525 1.1× 538 1.5× 275 1.0× 63 1.7k
H. Jeremy Bockholt United States 21 1.3k 0.8× 790 1.0× 320 0.7× 160 0.4× 175 0.6× 32 2.4k
Boris A. Gutman United States 36 981 0.7× 822 1.1× 749 1.5× 335 0.9× 100 0.3× 95 2.9k
Ling‐Li Zeng China 30 2.6k 1.7× 1.2k 1.5× 364 0.8× 253 0.7× 223 0.8× 123 3.4k

Countries citing papers authored by Chong-Yaw Wee

Since Specialization
Citations

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

Fields of papers citing papers by Chong-Yaw Wee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chong-Yaw Wee. 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 Chong-Yaw Wee. The network helps show where Chong-Yaw Wee may publish in the future.

Co-authorship network of co-authors of Chong-Yaw Wee

This figure shows the co-authorship network connecting the top 25 collaborators of Chong-Yaw Wee. A scholar is included among the top collaborators of Chong-Yaw Wee 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 Chong-Yaw Wee. Chong-Yaw Wee 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.
Li, Yang, Jingyu Liu, Ziwen Peng, et al.. (2019). Fusion of ULS Group Constrained High- and Low-Order Sparse Functional Connectivity Networks for MCI Classification. Neuroinformatics. 18(1). 1–24. 23 indexed citations
2.
Li, Yang, Jingyu Liu, Ke Li, et al.. (2017). Fusion of High-Order and Low-Order Effective Connectivity Networks for MCI Classification. Lecture notes in computer science. 2017. 307–315. 4 indexed citations
3.
Li, Yang, Jingyu Liu, Ke Li, et al.. (2017). Structural Connectivity Guided Sparse Effective Connectivity for MCI Identification. Lecture notes in computer science. 10541. 299–306. 4 indexed citations
4.
Suk, Heung‐Il, Chong-Yaw Wee, Seong‐Whan Lee, & Dinggang Shen. (2016). State-space model with deep learning for functional dynamics estimation in resting-state fMRI. NeuroImage. 129. 292–307. 214 indexed citations
5.
Chen, Geng, Pei Zhang, Ke Li, et al.. (2016). Improving Estimation of Fiber Orientations in Diffusion MRI Using Inter-Subject Information Sharing. Scientific Reports. 6(1). 37847–37847. 12 indexed citations
6.
Chen, Geng, Pei Zhang, Ke Li, et al.. (2016). Angular Resolution Enhancement of Diffusion MRI Data Using Inter-Subject Information Transfer. PubMed. 2016. 145–157. 2 indexed citations
7.
Gao, Yue, Chong-Yaw Wee, Minjeong Kim, et al.. (2015). MCI Identification by Joint Learning on Multiple MRI Data. Lecture notes in computer science. 9350. 78–85. 23 indexed citations
8.
Li, Yang, Chong-Yaw Wee, Biao Jie, Ziwen Peng, & Dinggang Shen. (2014). Sparse Multivariate Autoregressive Modeling for Mild Cognitive Impairment Classification. Neuroinformatics. 12(3). 455–469. 32 indexed citations
9.
Thung, Kim‐Han, Chong-Yaw Wee, Pew‐Thian Yap, & Dinggang Shen. (2014). Neurodegenerative disease diagnosis using incomplete multi-modality data via matrix shrinkage and completion. NeuroImage. 91. 386–400. 82 indexed citations
10.
Wee, Chong-Yaw, Zhimin Zhao, Pew‐Thian Yap, et al.. (2014). Disrupted Brain Functional Network in Internet Addiction Disorder: A Resting-State Functional Magnetic Resonance Imaging Study. PLoS ONE. 9(9). e107306–e107306. 74 indexed citations
11.
Suk, Heung‐Il, Chong-Yaw Wee, Seong‐Whan Lee, & Dinggang Shen. (2014). Supervised Discriminative Group Sparse Representation for Mild Cognitive Impairment Diagnosis. Neuroinformatics. 13(3). 277–295. 35 indexed citations
12.
Zhang, Pei, Chong-Yaw Wee, Marc Niethammer, Dinggang Shen, & Pew‐Thian Yap. (2013). Large Deformation Image Classification Using Generalized Locality-Constrained Linear Coding. Lecture notes in computer science. 16(Pt 1). 292–299. 13 indexed citations
13.
Shi, Feng, Li Wang, Ziwen Peng, Chong-Yaw Wee, & Dinggang Shen. (2013). Altered Modular Organization of Structural Cortical Networks in Children with Autism. PLoS ONE. 8(5). e63131–e63131. 39 indexed citations
14.
Wee, Chong-Yaw, Pew‐Thian Yap, Daoqiang Zhang, Lihong Wang, & Dinggang Shen. (2013). Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification. Brain Structure and Function. 219(2). 641–656. 134 indexed citations
15.
Liu, Feng, Heung‐Il Suk, Chong-Yaw Wee, Huafu Chen, & Dinggang Shen. (2013). High-Order Graph Matching Based Feature Selection for Alzheimer’s Disease Identification. Lecture notes in computer science. 311–318. 32 indexed citations
16.
Liu, Feng, Chong-Yaw Wee, Huafu Chen, & Dinggang Shen. (2013). Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer's Disease and mild cognitive impairment identification. NeuroImage. 84. 466–475. 185 indexed citations
17.
Wee, Chong-Yaw, Pew-Thian Yap, Kevin Denny, et al.. (2012). Resting-State Multi-Spectrum Functional Connectivity Networks for Identification of MCI Patients. PLoS ONE. 7(5). e37828–e37828. 113 indexed citations
18.
Wee, Chong-Yaw, Pew‐Thian Yap, Daoqiang Zhang, et al.. (2011). Identification of MCI individuals using structural and functional connectivity networks. NeuroImage. 59(3). 2045–2056. 312 indexed citations
19.
Wee, Chong-Yaw, Pew‐Thian Yap, Wenbin Li, et al.. (2010). Enriched white matter connectivity networks for accurate identification of MCI patients. NeuroImage. 54(3). 1812–1822. 169 indexed citations
20.
Wee, Chong-Yaw, et al.. (2003). Classification of rice grains using fuzzy artmap neural network. 2. 223–226. 5 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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