Lishuang Li
- Artificial Intelligence top 2%
- Topic Modeling 55
- Natural Language Processing Techniques 26
- Advanced Text Analysis Techniques 17
- Text and Document Classification Technologies 12
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- Biomedical Text Mining and Ontologies 51
- Machine Learning in Bioinformatics 9
- Bioinformatics and Genomic Networks 7
- Health Information Management top 10%
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- Nonlinear Differential Equations Analysis 7
- Co-authors
- Degen HuangHongbin LuYang LiuLi ZouYuxin JiangJia WanJian WangLiu Yang
In The Last Decade
Lishuang Li
85 papers receiving 866 citations
Peers
Comparison fields: 5 of 124
- Artificial Intelligence 617
- Health Informatics 9
- Molecular Biology 448
- Computational Theory and Mathematics 75
- Health Information Management 21
Countries citing papers authored by Lishuang Li
This map shows the geographic impact of Lishuang Li'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 Lishuang Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lishuang Li more than expected).
Fields of papers citing papers by Lishuang Li
This network shows the impact of papers produced by Lishuang Li. 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 Lishuang Li. The network helps show where Lishuang Li may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Lishuang Li, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 7 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 0 | |
| 6 | 2023 | 8 | |
| 7 | 2023 | 5 | |
| 8 | 2022 | 8 | |
| 9 | 2022 | 1 | |
| 10 | 2020 | 63 | |
| 11 | 2019 | 3 | |
| 12 | Biomedical Named Entity Recognition Based on Feature Selection and Word Representations. | 2016 | 2 |
| 13 | Protein-Protein Interaction Extraction Based on Ensemble Kernel | 2013 | 3 |
| 14 | Improving Feature-Based Biomedical Event Extraction System by Integrating Argument Information | 2013 | 6 |
| 15 | Intervention Research on Preventing Oral Mucositis Afer Using High Dose Methotrexate Chemotherapy in Osteosarcoma by Gargling with Calcium Folinic | 2011 | 1 |
| 16 | 2011 | 19 | |
| 17 | Mining Large-scale Comparable Corpora from Chinese-English News Collections | 2010 | 6 |
| 18 | 2009 | 60 | |
| 19 | HMM and CRF Based Hybrid Model for Chinese Lexical Analysis | 2008 | 2 |
| 20 | Hybrid Models for Chinese Named Entity Recognition | 2006 | 12 |
About Lishuang Li
Lishuang Li is a scholar working on Artificial Intelligence, Modeling and Simulation and Numerical Analysis, having authored 99 papers that have together received 899 indexed citations. Recurring topics across this work include Topic Modeling (55 papers), Biomedical Text Mining and Ontologies (51 papers), Natural Language Processing Techniques (26 papers), Advanced Text Analysis Techniques (17 papers), Text and Document Classification Technologies (12 papers), Machine Learning in Bioinformatics (9 papers), Nonlinear Differential Equations Analysis (7 papers) and Bioinformatics and Genomic Networks (7 papers). The work is most often cited by research in Artificial Intelligence (617 citations), Health Informatics (9 citations) and Molecular Biology (448 citations). Lishuang Li has collaborated with scholars based in China, Australia and Japan. Frequent co-authors include Degen Huang, Hongbin Lu, Yang Liu, Li Zou, Yuxin Jiang, Jia Wan, Jian Wang, Liu Yang, Wenting Fan and Hongfei Lin. Their work appears in journals such as The Journal of Immunology, PLoS ONE and International Journal of Molecular Sciences.
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.