Yu‐Da Lin
Impact in
- Genetics top 10%
- Genetic Associations and Epidemiology
- Genetic and phenotypic traits in livestock
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- Gene expression and cancer classification
- Bioinformatics and Genomic Networks
- Machine Learning in Bioinformatics
Papers in
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- Gene expression and cancer classification 17
- Bioinformatics and Genomic Networks 11
- Machine Learning in Bioinformatics 9
- RNA and protein synthesis mechanisms 6
- Genetics 23
- Genetic Associations and Epidemiology 20
- Genetic and phenotypic traits in livestock 5
- Co-authors
- Cheng‐Hong Yang (54 shared papers)Li‐Yeh Chuang (49 shared papers)Hsueh‐Wei Chang (19 shared papers)Sin‐Hua Moi (9 shared papers)Ming‐Feng Hou (7 shared papers)Jin‐Bor Chen (5 shared papers)Yu‐Huei Cheng (4 shared papers)Li-Yeh Chuang (2 shared papers)
In The Last Decade
Yu‐Da Lin
70 papers receiving 838 citations
Peers
Comparison fields: 5 of 117
- Genetics 215
- Molecular Biology 345
- Artificial Intelligence 158
- Health Information Management 19
- Cancer Research 54
Countries citing papers authored by Yu‐Da Lin
This map shows the geographic impact of Yu‐Da Lin'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‐Da Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yu‐Da Lin more than expected).
Fields of papers citing papers by Yu‐Da Lin
This network shows the impact of papers produced by Yu‐Da Lin. 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‐Da Lin. The network helps show where Yu‐Da Lin may publish in the future.
Co-authors
The 25 scholars most cited alongside Yu‐Da Lin, 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 70 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 49 | |
| 2 | 2012 | 45 | |
| 3 | 2017 | 42 | |
| 4 | 2012 | 33 | |
| 5 | 2015 | 33 | |
| 6 | 2020 | 29 | |
| 7 | 2013 | 28 | |
| 8 | 2013 | 28 | |
| 9 | 2018 | 27 | |
| 10 | 2019 | 26 | |
| 11 | 2016 | 24 | |
| 12 | 2019 | 23 | |
| 13 | 2013 | 20 | |
| 14 | 2014 | 19 | |
| 15 | 2015 | 19 | |
| 16 | 2015 | 18 | |
| 17 | 2013 | 18 | |
| 18 | 2013 | 18 | |
| 19 | 2014 | 18 | |
| 20 | 2016 | 18 |
About Yu‐Da Lin
Yu‐Da Lin is a scholar working on Molecular Biology, Genetics, Artificial Intelligence, Cancer Research and Computer Vision and Pattern Recognition, having authored 70 papers that have together received 862 indexed citations. Recurring topics across this work include Genetic Associations and Epidemiology (20 papers), Gene expression and cancer classification (17 papers), Bioinformatics and Genomic Networks (11 papers), Machine Learning in Bioinformatics (9 papers), RNA and protein synthesis mechanisms (6 papers), Genetic and phenotypic traits in livestock (5 papers), Cancer-related molecular mechanisms research (3 papers) and Forecasting Techniques and Applications (3 papers). The work is most often cited by research in Genetics (215 citations), Molecular Biology (345 citations), Artificial Intelligence (158 citations), Health Information Management (19 citations) and Cancer Research (54 citations). Yu‐Da Lin has collaborated with scholars based in Taiwan, Yemen and Czechia. Frequent co-authors include Cheng‐Hong Yang, Li‐Yeh Chuang, Hsueh‐Wei Chang, Sin‐Hua Moi, Ming‐Feng Hou, Jin‐Bor Chen, Yu‐Huei Cheng, Li-Yeh Chuang, Shyh-Jong Wu and Wen‐Chin Lee. Their work appears in journals such as IEEE Access, BioMed Research International, IEEE/ACM Transactions on Computational Biology and Bioinformatics, PLoS ONE and Bioinformatics.
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.