Jingyu Hou

1.2k citations
83 papers · 718 · h-index 13

Impact in

Papers in

Jingyu Hou

78 papers receiving 693 citations

Peers

Jingyu Hou
Comparison fields: 5 of 108
  • Media Technology 91
  • Computer Vision and Pattern Recognition 135
  • Cancer Research 83
  • Molecular Biology 324
  • Artificial Intelligence 137
Replace De-Shuang Huang with:
De-Shuang Huang China
Shanshan Liu China
Gianvito Pio Italy
Mingzhu Lu China
Ying-Lian Gao China
Elham Pashaei Türkiye
Elnaz Pashaei Türkiye
Stijn Meganck Belgium
Robin Duqué Belgium
David Steenhoff Belgium
Jingyu Hou relative to De-Shuang Huang China De-Shuang Huang's profile →
Citations per field
00.5×7.5×
De-Shuang Huang · 1×
Citations per year

Countries citing papers authored by Jingyu Hou

Since Specialization
Citations

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

Fields of papers citing papers by Jingyu Hou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jingyu Hou, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jingyu Hou Line = papers co-authored together Jingyu Hou links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 83 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201974
2 201858
3 201645
4 202139
5 202434
6 201827
7 201125
8 201025
9 201923
10 202019
11 202318
12 202218
13 202316
14 202012
15 202312
16 202212
17 201812
18 201011
19 201710
20 202110

About Jingyu Hou

Jingyu Hou is a scholar working on Molecular Biology, Artificial Intelligence, Computer Vision and Pattern Recognition, Cancer Research and Media Technology, having authored 83 papers that have together received 718 indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (17 papers), Gene expression and cancer classification (11 papers), RNA modifications and cancer (8 papers), Cancer-related molecular mechanisms research (7 papers), Advanced Image Fusion Techniques (7 papers), Computational Drug Discovery Methods (6 papers), Machine Learning in Bioinformatics (5 papers) and Remote-Sensing Image Classification (4 papers). The work is most often cited by research in Media Technology (91 citations), Computer Vision and Pattern Recognition (135 citations), Cancer Research (83 citations), Molecular Biology (324 citations) and Artificial Intelligence (137 citations). Jingyu Hou has collaborated with scholars based in China, Australia and Taiwan. Frequent co-authors include Yong Xiang, Shaowen Yao, Xin Jin, Qian Jiang, Dongming Zhou, Wanlei Zhou, Chen Gao, Ashish Saini, Shin‐Jye Lee and Yi‐Ping Phoebe Chen. Their work appears in journals such as IET Systems Biology, Mathematical Biosciences, PLoS ONE, Journal of High Speed Networks and Nature Communications.

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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