Yu Yao

505 total citations
9 papers, 317 citations indexed

About

Yu Yao is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems. According to data from OpenAlex, Yu Yao has authored 9 papers receiving a total of 317 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 4 papers in Molecular Biology and 3 papers in Information Systems. Recurrent topics in Yu Yao's work include Advanced Text Analysis Techniques (3 papers), Machine Learning in Bioinformatics (2 papers) and Topic Modeling (2 papers). Yu Yao is often cited by papers focused on Advanced Text Analysis Techniques (3 papers), Machine Learning in Bioinformatics (2 papers) and Topic Modeling (2 papers). Yu Yao collaborates with scholars based in China and Canada. Yu Yao's co-authors include Xiuquan Du, Yanping Zhang, Shiwei Sun, Chang-Lin Hu, Yuanting Yan, Peng Yang, Huajian Zhou, Ying Yang, Shuo Li and Guangzhen Zhao and has published in prestigious journals such as International Journal of Molecular Sciences, IEEE Access and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Yu Yao

9 papers receiving 311 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yu Yao China	5	267	112	34	10	6	9	317
Shuwei Yao China	5	232 0.9×	63 0.6×	20 0.6×	7 0.7×	6 1.0×	5	255
X.-S. Zhang China	7	248 0.9×	68 0.6×	20 0.6×	17 1.7×	8 1.3×	7	290
Chunqiu Xia China	8	271 1.0×	102 0.9×	35 1.0×	17 1.7×	2 0.3×	10	309
Darby Tien-Hao Chang Taiwan	10	264 1.0×	73 0.7×	16 0.5×	42 4.2×	7 1.2×	19	318
Wenjia He China	8	271 1.0×	36 0.3×	35 1.0×	7 0.7×	3 0.5×	14	320
Samuel Sledzieski United States	7	233 0.9×	102 0.9×	12 0.4×	21 2.1×	13 2.2×	11	291
Qiguo Dai China	12	589 2.2×	61 0.5×	29 0.9×	11 1.1×	9 1.5×	24	670
Tong-Liang Zhang China	7	579 2.2×	135 1.2×	28 0.8×	4 0.4×	3 0.5×	12	632
Yixiao Zhai China	8	268 1.0×	48 0.4×	21 0.6×	24 2.4×	17 2.8×	15	341

Countries citing papers authored by Yu Yao

Since Specialization

Citations

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

Fields of papers citing papers by Yu Yao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yu Yao

This figure shows the co-authorship network connecting the top 25 collaborators of Yu Yao. A scholar is included among the top collaborators of Yu Yao 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 Yu Yao. Yu Yao 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:

9 of 9 papers shown

1.

Yao, Yu, et al.. (2024). KGAgent: Learning a Deep Reinforced Agent for Keyphrase Generation. IEEE/ACM Transactions on Audio Speech and Language Processing. 32. 1928–1940. 2 indexed citations

2.

Yao, Yu, et al.. (2023). Probabilistic Keyphrase Generation From Copy and Generating Spaces. IEEE Transactions on Neural Networks and Learning Systems. 35(11). 15956–15970. 2 indexed citations

3.

Yang, Peng, et al.. (2022). GCN-based document representation for keyphrase generation enhanced by maximizing mutual information. Knowledge-Based Systems. 243. 108488–108488. 15 indexed citations

4.

Yang, Peng, Yu Yao, & Huajian Zhou. (2020). Leveraging Global and Local Topic Popularities for LDA-Based Document Clustering. IEEE Access. 8. 24734–24745. 15 indexed citations

5.

Yao, Yu, et al.. (2019). An integration of deep learning with feature embedding for protein–protein interaction prediction. PeerJ. 7. e7126–e7126. 74 indexed citations

6.

Du, Xiuquan, et al.. (2018). DeepSS: Exploring Splice Site Motif Through Convolutional Neural Network Directly From DNA Sequence. IEEE Access. 6. 32958–32978. 21 indexed citations

7.

Du, Xiuquan, Shiwei Sun, Chang-Lin Hu, et al.. (2017). DeepPPI: Boosting Prediction of Protein–Protein Interactions with Deep Neural Networks. Journal of Chemical Information and Modeling. 57(6). 1499–1510. 183 indexed citations

8.

Hu, Chang-Lin, et al.. (2017). Analysis and Prediction of Exon Skipping Events from RNA-Seq with Sequence Information Using Rotation Forest. International Journal of Molecular Sciences. 18(12). 2691–2691. 2 indexed citations

9.

Yao, Yu, et al.. (2016). Multi-dimensions Clustering Approach for Physical Health Data Based on Aritificial Ant Colony Optimization. 406. 601–604. 3 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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