Yu-Shi Lin

829 total citations
13 papers, 250 citations indexed

About

Yu-Shi Lin is a scholar working on Artificial Intelligence, Molecular Biology and Biophysics. According to data from OpenAlex, Yu-Shi Lin has authored 13 papers receiving a total of 250 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Molecular Biology and 5 papers in Biophysics. Recurrent topics in Yu-Shi Lin's work include Cell Image Analysis Techniques (5 papers), Biomedical Text Mining and Ontologies (4 papers) and Topic Modeling (4 papers). Yu-Shi Lin is often cited by papers focused on Cell Image Analysis Techniques (5 papers), Biomedical Text Mining and Ontologies (4 papers) and Topic Modeling (4 papers). Yu-Shi Lin collaborates with scholars based in Taiwan, Italy and United States. Yu-Shi Lin's co-authors include Chun‐Nan Hsu, Yu-Ming Chang, Cheng-Ju Kuo, I‐Fang Chung, Chung‐Chih Lin, Shou-De Lin, Yi‐Hung Huang, Yuh‐Show Tsai, Chih‐Jen Lin and Che‐Wei Chang and has published in prestigious journals such as Bioinformatics, Expert Systems with Applications and ACM Transactions on Intelligent Systems and Technology.

In The Last Decade

Yu-Shi Lin

12 papers receiving 240 citations

Peers

Yu-Shi Lin

AI

MB

BIOPH

CSA

CVPR

Raúl Giráldez Spain

Beatriz Pontes Spain

B. McKinnon United Kingdom

Fang Kong China

Jennifer Vendetti United States

Rudy Becarelli Italy

Nadia Essoussi Tunisia

David Vickrey United States

Fernando Pereira United States

Longhua Qian China

Raúl Giráldez Spain

Yu-Shi Lin

140 ×0.9

AI

188 ×1.5

MB

7 ×0.2

BIOPH

3 ×0.1

CSA

24 ×0.8

CVPR

Citations per year, relative to Yu-Shi Lin Yu-Shi Lin (= 1×) peers Raúl Giráldez

Countries citing papers authored by Yu-Shi Lin

Since Specialization

Citations

This map shows the geographic impact of Yu-Shi 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-Shi 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-Shi Lin more than expected).

Fields of papers citing papers by Yu-Shi Lin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yu-Shi Lin

This figure shows the co-authorship network connecting the top 25 collaborators of Yu-Shi Lin. A scholar is included among the top collaborators of Yu-Shi Lin 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-Shi Lin. Yu-Shi Lin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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13 of 13 papers shown

1.

Lin, Yu-Shi & Anthony J.T. Lee. (2024). MMF: Winning Solution to Social Media Popularity Prediction Challenge 2024. 11445–11449.

2.

Chen, Yu‐Chih, et al.. (2013). A modified random walk framework for handling negative ratings and generating explanations. ACM Transactions on Intelligent Systems and Technology. 4(1). 1–21. 8 indexed citations

3.

Yu, Hsiang‐Fu, Hung-Yi Lo, Hsun-Ping Hsieh, et al.. (2010). Feature Engineering and Classifier Ensemble for KDD Cup 2010. Knowledge Discovery and Data Mining. 89 indexed citations

4.

Lin, Yu-Shi, et al.. (2010). A spectral graph theoretic approach to quantification and calibration of collective morphological differences in cell images. Bioinformatics. 26(12). i29–i37. 6 indexed citations

5.

Nanni, Loris, Chun‐Nan Hsu, Alessandra Lumini, Yu-Shi Lin, & Chung‐Chih Lin. (2009). Automated cell phenotype image classification combining different methods.. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 1 indexed citations

6.

Lin, Yu-Shi, Yi‐Hung Huang, Chung‐Chih Lin, & Chun‐Nan Hsu. (2009). Feature space transformation for semi-supervised learning for protein subcellular localization in fluorescence microscopy images. 414–417. 3 indexed citations

7.

Nanni, Loris, Alessandra Lumini, Yu-Shi Lin, Chun‐Nan Hsu, & Chung‐Chih Lin. (2009). Fusion of systems for automated cell phenotype image classification. Expert Systems with Applications. 37(2). 1556–1562. 15 indexed citations

8.

Lin, Yu-Shi, et al.. (2008). Re-weighting Graph Links for Quantifying Difference.. Neural Information Processing Systems. 1 indexed citations

9.

Hsu, Chun‐Nan, et al.. (2008). Integrating high dimensional bi-directional parsing models for gene mention tagging. Bioinformatics. 24(13). i286–i294. 63 indexed citations

10.

Kuo, Cheng-Ju, Yu-Ming Chang, Kuan‐Ting Lin, et al.. (2007). Rich Feature Set, Unification of Bidirectional Parsing and Dictionary Filtering for High F-Score Gene Mention Tagging.. 28 indexed citations

11.

Lin, Chung‐Chih, et al.. (2007). Boosting multiclass learning with repeating codes and weak detectors for protein subcellular localization. Bioinformatics. 23(24). 3374–3381. 28 indexed citations

12.

Chang, Yu-Ming, et al.. (2007). Analysis and Enhancement of Conditional Random Fields Gene Mention Taggers in BioCreative II Challenge Evaluation.. 5 indexed citations

13.

Kuo, Cheng-Ju, Yu-Ming Chang, Kuan‐Ting Lin, et al.. (2007). Exploring Match Scores to Boost Precision of Gene Normalization.. 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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