Qingcai Chen

7.2k total citations
169 papers, 3.7k citations indexed

About

Qingcai Chen is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Qingcai Chen has authored 169 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 127 papers in Artificial Intelligence, 54 papers in Molecular Biology and 33 papers in Computer Vision and Pattern Recognition. Recurrent topics in Qingcai Chen's work include Topic Modeling (80 papers), Natural Language Processing Techniques (69 papers) and Biomedical Text Mining and Ontologies (40 papers). Qingcai Chen is often cited by papers focused on Topic Modeling (80 papers), Natural Language Processing Techniques (69 papers) and Biomedical Text Mining and Ontologies (40 papers). Qingcai Chen collaborates with scholars based in China, United States and Hong Kong. Qingcai Chen's co-authors include Xiaolong Wang, Buzhou Tang, Shusen Zhou, Bin Liu, Baotian Hu, Qiwen Dong, Shengyu Liu, Hua Xu, Zengjian Liu and Jun Zhang and has published in prestigious journals such as Bioinformatics, PLoS ONE and Journal of Molecular Biology.

In The Last Decade

Qingcai Chen

159 papers receiving 3.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Qingcai Chen China 35 2.1k 1.4k 585 277 265 169 3.7k
Jinbo Bi United States 34 1.9k 0.9× 477 0.3× 1.6k 2.7× 258 0.9× 183 0.7× 150 4.7k
Gavin Brown United Kingdom 21 1.5k 0.7× 330 0.2× 733 1.3× 163 0.6× 271 1.0× 65 2.7k
Hoifung Poon United States 25 3.3k 1.6× 1.0k 0.7× 389 0.7× 270 1.0× 276 1.0× 66 4.3k
Chiranjib Bhattacharyya India 20 1.6k 0.8× 354 0.2× 973 1.7× 186 0.7× 388 1.5× 90 3.4k
Kamal Berahmand Australia 33 1.2k 0.6× 419 0.3× 447 0.8× 135 0.5× 325 1.2× 54 2.5k
Lifang He China 33 2.2k 1.0× 319 0.2× 1.0k 1.8× 164 0.6× 431 1.6× 175 4.2k
Alan Wee‐Chung Liew Australia 38 1.1k 0.5× 1.0k 0.7× 1.6k 2.8× 228 0.8× 215 0.8× 227 4.4k
S. V. N. Vishwanathan United States 25 2.5k 1.2× 575 0.4× 1.4k 2.4× 302 1.1× 363 1.4× 68 3.9k
Wang Xiang-rui China 3 2.6k 1.2× 460 0.3× 1.8k 3.1× 131 0.5× 512 1.9× 12 4.8k
Ioannis Katakis Greece 13 2.2k 1.0× 434 0.3× 772 1.3× 132 0.5× 842 3.2× 36 2.9k

Countries citing papers authored by Qingcai Chen

Since Specialization
Citations

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

Fields of papers citing papers by Qingcai Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qingcai Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Qingcai Chen. A scholar is included among the top collaborators of Qingcai Chen 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 Qingcai Chen. Qingcai Chen 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.
Chen, Qingcai, et al.. (2024). Learning to Improve Out-of-Distribution Generalization via Self-Adaptive Language Masking. IEEE/ACM Transactions on Audio Speech and Language Processing. 32. 2739–2750. 1 indexed citations
2.
Qin, Yang, et al.. (2023). BioPRO: Context-Infused Prompt Learning for Biomedical Entity Linking. IEEE/ACM Transactions on Audio Speech and Language Processing. 32. 374–385. 1 indexed citations
3.
Peng, Ke, Xiaowen Meng, Xi-sheng Shan, et al.. (2023). Single-nucleus Atlas of Sevoflurane-induced Hippocampal Cell Type– and Sex-specific Effects during Development in Mice. Anesthesiology. 138(5). 477–495. 14 indexed citations
4.
Li, Jia-Hao, et al.. (2023). iEnhancer-ELM: improve enhancer identification by extracting position-related multiscale contextual information based on enhancer language models. Bioinformatics Advances. 3(1). vbad043–vbad043. 13 indexed citations
5.
Hu, Baotian, et al.. (2022). Learning to generate complex question with intent prediction from long passage. Applied Intelligence.
6.
Li, Yunxin, Qingcai Chen, Baotian Hu, et al.. (2022). Fast and Robust Online Handwritten Chinese Character Recognition With Deep Spatial and Contextual Information Fusion Network. IEEE Transactions on Multimedia. 25. 2140–2152. 11 indexed citations
7.
Qin, Yang, et al.. (2021). Distantly supervised biomedical relation extraction using piecewise attentive convolutional neural network and reinforcement learning. Journal of the American Medical Informatics Association. 28(12). 2571–2581. 8 indexed citations
8.
Chen, Qingcai, et al.. (2021). Maternal anesthesia with sevoflurane during the mid-gestation induces social interaction deficits in offspring C57BL/6 mice. Biochemical and Biophysical Research Communications. 553. 65–71. 14 indexed citations
9.
Hu, Baotian, Yang Xiang, Xiaolong Wang, et al.. (2021). A BERT-Based Generation Model to Transform Medical Texts to SQL Queries for Electronic Medical Records: Model Development and Validation. JMIR Medical Informatics. 9(12). e32698–e32698. 10 indexed citations
10.
Liu, Xin, Qingcai Chen, Yan Liu, et al.. (2020). Decomposing word embedding with the capsule network. Knowledge-Based Systems. 212. 106611–106611. 8 indexed citations
11.
Liu, Xin, et al.. (2020). Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition. IEEE Transactions on Neural Networks and Learning Systems. 31(11). 4637–4648. 26 indexed citations
12.
Liu, Xin, Qingcai Chen, Xiangping Wu, et al.. (2020). Gated Semantic Difference Based Sentence Semantic Equivalence Identification. IEEE/ACM Transactions on Audio Speech and Language Processing. 28. 2770–2780. 4 indexed citations
13.
Wu, Xiangping, et al.. (2020). LCSegNet: An Efficient Semantic Segmentation Network for Large-Scale Complex Chinese Character Recognition. IEEE Transactions on Multimedia. 23. 3427–3440. 23 indexed citations
14.
Liu, Zengjian, et al.. (2016). CMedTEX: A Rule-based Temporal Expression Extraction and Normalization System for Chinese Clinical Notes.. PubMed. 2016. 818–826. 4 indexed citations
15.
Hu, Baotian, et al.. (2015). A novel word embedding learning model using the dissociation between nouns and verbs. Neurocomputing. 171. 1108–1117. 22 indexed citations
16.
Zhou, Shusen, Qingcai Chen, Xiaolong Wang, & Xiaoling Li. (2014). Hybrid Deep Belief Networks for Semi-supervised Sentiment Classification. International Conference on Computational Linguistics. 1341–1349. 12 indexed citations
17.
Xu, Jun, et al.. (2014). HITSZ-ICRC at NTCIR-11 Temporalia Task. NTCIR. 4 indexed citations
18.
Yuan, Bo, Qingcai Chen, Yang Xiang, et al.. (2012). A Mixed Deterministic Model for Coreference Resolution. Empirical Methods in Natural Language Processing. 76–82. 6 indexed citations
19.
Zhou, Shusen, Qingcai Chen, & Xiaolong Wang. (2010). Active Deep Networks for Semi-Supervised Sentiment Classification. International Conference on Computational Linguistics. 1515–1523. 55 indexed citations
20.
He, Yancheng, et al.. (2010). An adaptive affinity propagation document clustering. 1–7. 15 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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