Simon Lin

6.0k total citations · 2 hit papers
85 papers, 3.6k citations indexed

About

Simon Lin is a scholar working on Molecular Biology, Artificial Intelligence and General Health Professions. According to data from OpenAlex, Simon Lin has authored 85 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Molecular Biology, 20 papers in Artificial Intelligence and 17 papers in General Health Professions. Recurrent topics in Simon Lin's work include Biomedical Text Mining and Ontologies (22 papers), Bioinformatics and Genomic Networks (13 papers) and Topic Modeling (10 papers). Simon Lin is often cited by papers focused on Biomedical Text Mining and Ontologies (22 papers), Bioinformatics and Genomic Networks (13 papers) and Topic Modeling (10 papers). Simon Lin collaborates with scholars based in United States, China and Austria. Simon Lin's co-authors include Warren A. Kibbe, Pan Du, Chiang‐Ching Huang, Lifang Hou, Nadereh Jafari, Xiao Zhang, Yungui Huang, Emre Sezgın, Thomas Ristenpart and Somesh Jha and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Simon Lin

84 papers receiving 3.5k citations

Hit Papers

Comparison of Beta-value and M-value methods for quantify... 2010 2026 2015 2020 2010 2014 400 800 1.2k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Simon Lin United States 21 2.0k 718 523 302 297 85 3.6k
Mahlet G. Tadesse United States 31 1.3k 0.7× 430 0.6× 266 0.5× 354 1.2× 204 0.7× 95 3.6k
Wei‐Qi Wei United States 25 1.1k 0.6× 680 0.9× 1.0k 1.9× 171 0.6× 115 0.4× 100 4.1k
Cui Tao United States 37 1.8k 0.9× 1.8k 2.5× 188 0.4× 299 1.0× 148 0.5× 349 5.2k
Anna Goldenberg Canada 32 2.4k 1.2× 1.1k 1.5× 310 0.6× 667 2.2× 146 0.5× 128 5.9k
Donglin Zeng United States 41 603 0.3× 585 0.8× 655 1.3× 173 0.6× 182 0.6× 328 6.2k
Jyotishman Pathak United States 40 1.3k 0.7× 1.8k 2.5× 459 0.9× 150 0.5× 173 0.6× 232 5.6k
Degui Zhi United States 33 1.8k 0.9× 628 0.9× 882 1.7× 279 0.9× 194 0.7× 131 3.8k
Elizabeth Chen United States 33 1.8k 0.9× 271 0.4× 389 0.7× 344 1.1× 73 0.2× 161 4.0k
Ju Han Kim South Korea 32 1.6k 0.8× 296 0.4× 305 0.6× 573 1.9× 89 0.3× 244 4.0k
Xuefeng B. Ling United States 39 2.3k 1.2× 288 0.4× 232 0.4× 287 1.0× 187 0.6× 147 5.6k

Countries citing papers authored by Simon Lin

Since Specialization
Citations

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

Fields of papers citing papers by Simon Lin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Simon Lin

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

All Works

20 of 20 papers shown
1.
Lin, Simon, et al.. (2021). Multi-View Deep Learning Framework for Predicting Patient Expenditure in Healthcare. SHILAP Revista de lepidopterología. 2. 62–71. 18 indexed citations
2.
Militello, Lisa K., Emre Sezgın, Yungui Huang, & Simon Lin. (2021). Delivering Perinatal Health Information via a Voice Interactive App (SMILE): Mixed Methods Feasibility Study. JMIR Formative Research. 5(3). e18240–e18240. 13 indexed citations
3.
Peng, Jin, et al.. (2021). A Machine Learning Approach to Uncovering Hidden Utilization Patterns of Early Childhood Dental Care Among Medicaid-Insured Children. Frontiers in Public Health. 8. 599187–599187. 9 indexed citations
4.
Zhang, Xu, Yungui Huang, Jennifer Lee, & Simon Lin. (2021). Turning Digital Trails into a Telehealth Competitive Edge. Telemedicine Journal and e-Health. 28(1). 39–43. 1 indexed citations
5.
Rust, Steve, Sven Bambach, Jeffrey Hoffman, et al.. (2020). The Deterioration Risk Index: Predicting Pediatric Inpatient Deterioration with Machine Learning and the Electronic Health Record. PEDIATRICS. 146. 229–230. 1 indexed citations
7.
Sezgın, Emre, et al.. (2020). Detecting Screams From Home Audio Recordings to Identify Tantrums: Exploratory Study Using Transfer Machine Learning. JMIR Formative Research. 4(6). e18279–e18279. 1 indexed citations
8.
Sezgın, Emre, Garey Noritz, Steve Rust, et al.. (2019). Capturing At-Home Health and Care Information for Children With Medical Complexity Using Voice Interactive Technologies: Multi-Stakeholder Viewpoint. Journal of Medical Internet Research. 22(2). e14202–e14202. 24 indexed citations
9.
Huang, Yungui, et al.. (2019). Factors Associated With Electronic Health Record Usage Among Primary Care Physicians After Hours: Retrospective Cohort Study. JMIR Human Factors. 6(3). e13779–e13779. 10 indexed citations
10.
Yang, Jing, Steve Rust, Jeffrey Hoffman, et al.. (2018). Chasing the Holy Grail - Predicting Pediatric Inpatient Deterioration Using a Vitals Risk Index. PEDIATRICS. 142. 570–570. 1 indexed citations
11.
Moosavinasab, Soheil, et al.. (2018). Char2Vec: Learning the Semantic Embedding of Rare and Unseen Words in the Biomedical Literature.. AMIA. 1 indexed citations
12.
Chen, Wei, Soheil Moosavinasab, Steve Rust, et al.. (2016). Evaluation of a Machine Learning Method to Rank PubMed Central Articles For Clinical Relevancy: NCH at TREC 2016 Clinical Decision Support Track.. Text REtrieval Conference. 1 indexed citations
13.
Regan, Kelly, Soheil Moosavinasab, Philip Payne, & Simon Lin. (2016). Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System. Journal of Visualized Experiments. 3 indexed citations
14.
Patel, Anup D., Robert Moss, Steven W. Rust, et al.. (2016). Patient-centered design criteria for wearable seizure detection devices. Epilepsy & Behavior. 64(Pt A). 116–121. 65 indexed citations
15.
Cheng, Wenqing, et al.. (2015). Context-Sensitive Spelling Correction of Consumer-Generated Content on Health Care. JMIR Medical Informatics. 3(3). e27–e27. 12 indexed citations
16.
Rastegar-Mojarad, Majid, et al.. (2015). Collecting and Analyzing Patient Experiences of Health Care From Social Media. SHILAP Revista de lepidopterología. 4(3). e78–e78. 45 indexed citations
17.
Ji, Xiaonan, Tara Borlawsky, Zhan Ye, et al.. (2014). Enabling Online Studies of Conceptual Relationships Between Medical Terms: Developing an Efficient Web Platform. JMIR Medical Informatics. 2(2). e23–e23. 8 indexed citations
18.
Rastegar-Mojarad, Majid, et al.. (2014). A Fuzzy-Match Search Engine for Physician Directories. JMIR Medical Informatics. 2(2). e30–e30. 4 indexed citations
19.
Osborne, John D., Li Zhu, Simon Lin, & Warren A. Kibbe. (2007). Interpreting Microarray Results With Gene Ontology and MeSH. Methods in molecular biology. 377. 223–241. 18 indexed citations
20.
Lin, Simon, et al.. (2004). MedlineR: an open source library in R for Medline literature data mining. Bioinformatics. 20(18). 3659–3661. 16 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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