Kuo‐Chen Chou
Impact in
- Molecular Biology top 0.02%
- Machine Learning in Bioinformatics
- RNA and protein synthesis mechanisms
- Genomics and Phylogenetic Studies
- Protein Structure and Dynamics
- vaccines and immunoinformatics approaches
- Biochemical and Structural Characterization
- RNA modifications and cancer
- Computational Theory and Mathematics top 0.05%
- Computational Drug Discovery Methods
Papers in
-
- Machine Learning in Bioinformatics 242
- RNA and protein synthesis mechanisms 140
- Genomics and Phylogenetic Studies 117
- Protein Structure and Dynamics 63
- vaccines and immunoinformatics approaches 17
- Biochemical and Structural Characterization 14
-
- Computational Drug Discovery Methods 56
- Co-authors
- Xuan Xiao (47 shared papers)Hong‐Bin Shen (16 shared papers)Hao Lin (22 shared papers)Wei Chen (23 shared papers)Yu‐Dong Cai (37 shared papers)Pengmian Feng (13 shared papers)Bin Liu (12 shared papers)Xiang Cheng (15 shared papers)
- Journals
- Bioinformatics (26 papers)PLoS ONE (23 papers)Analytical Biochemistry (16 papers)Journal of Theoretical Biology (16 papers)Oncotarget (13 papers)
- Partner nations
- United StatesChinaSaudi Arabia
In The Last Decade
Kuo‐Chen Chou
365 papers receiving 37.4k citations
Kuo‐Chen Chou's Hit Papers
Peers
Comparison fields: 5 of 198
- Molecular Biology 33.8k
- Computational Theory and Mathematics 5.2k
- Microbiology 1.0k
- Cancer Research 1.2k
- Virology 237
Countries citing papers authored by Kuo‐Chen Chou
This map shows the geographic impact of Kuo‐Chen Chou'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 Kuo‐Chen Chou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kuo‐Chen Chou more than expected).
Fields of papers citing papers by Kuo‐Chen Chou
This network shows the impact of papers produced by Kuo‐Chen Chou. 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 Kuo‐Chen Chou. The network helps show where Kuo‐Chen Chou may publish in the future.
Co-authors
The 25 scholars most cited alongside Kuo‐Chen Chou, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 370 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Prediction of protein cellular attributes using pseudo‐amino acid composition Hit paper breakdown → | 2001 | 1654 |
| 2 | Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization Hit paper breakdown → | 2010 | 866 |
| 3 | Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes Hit paper breakdown → | 2004 | 861 |
| 4 | Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences Hit paper breakdown → | 2015 | 663 |
| 5 | iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition Hit paper breakdown → | 2013 | 571 |
| 6 | iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences Hit paper breakdown → | 2018 | 541 |
| 7 | Structural Bioinformatics and its Impact to Biomedical Science Hit paper breakdown → | 2004 | 533 |
| 8 | Impacts of Bioinformatics to Medicinal Chemistry Hit paper breakdown → | 2015 | 487 |
| 9 | iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition Hit paper breakdown → | 2014 | 470 |
| 10 | 2009 | 446 | |
| 11 | 2016 | 392 | |
| 12 | Some remarks on predicting multi-label attributes in molecular biosystems Hit paper breakdown → | 2013 | 384 |
| 13 | 2014 | 384 | |
| 14 | 2007 | 378 | |
| 15 | 2014 | 341 | |
| 16 | iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data Hit paper breakdown → | 2019 | 329 |
| 17 | 2015 | 320 | |
| 18 | 2006 | 316 | |
| 19 | 2011 | 315 | |
| 20 | 2010 | 297 |
About Kuo‐Chen Chou
Kuo‐Chen Chou is a scholar working on Molecular Biology, Computational Theory and Mathematics, Materials Chemistry, Spectroscopy and Radiology, Nuclear Medicine and Imaging, having authored 370 papers that have together received 38.0k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (242 papers), RNA and protein synthesis mechanisms (140 papers), Genomics and Phylogenetic Studies (117 papers), Protein Structure and Dynamics (63 papers), Computational Drug Discovery Methods (56 papers), Enzyme Structure and Function (19 papers), vaccines and immunoinformatics approaches (17 papers) and Biochemical and Structural Characterization (14 papers). The work is most often cited by research in Molecular Biology (33.8k citations), Computational Theory and Mathematics (5.2k citations), Microbiology (1.0k citations), Cancer Research (1.2k citations) and Virology (237 citations). Kuo‐Chen Chou has collaborated with scholars based in United States, China and Saudi Arabia. Frequent co-authors include Xuan Xiao, Hong‐Bin Shen, Hao Lin, Wei Chen, Yu‐Dong Cai, Pengmian Feng, Bin Liu, Xiang Cheng, Hui Ding and Wang‐Ren Qiu. Their work appears in journals such as Bioinformatics, PLoS ONE, Analytical Biochemistry, Journal of Theoretical Biology and Oncotarget.
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.