Yan Chak Li

434 total citations
8 papers, 210 citations indexed

About

Yan Chak Li is a scholar working on Molecular Biology, Artificial Intelligence and Health, Toxicology and Mutagenesis. According to data from OpenAlex, Yan Chak Li has authored 8 papers receiving a total of 210 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 3 papers in Artificial Intelligence and 2 papers in Health, Toxicology and Mutagenesis. Recurrent topics in Yan Chak Li's work include Machine Learning in Healthcare (3 papers), Air Quality and Health Impacts (2 papers) and Biomedical Text Mining and Ontologies (2 papers). Yan Chak Li is often cited by papers focused on Machine Learning in Healthcare (3 papers), Air Quality and Health Impacts (2 papers) and Biomedical Text Mining and Ontologies (2 papers). Yan Chak Li collaborates with scholars based in United States and United Kingdom. Yan Chak Li's co-authors include Gaurav Pandey, Supinda Bunyavanich, Arjun S. Yadaw, Sonali Bose, Ravi Iyengar, Jeffrey Law, Yoojin Chun, Hsiao‐Hsien Leon Hsu, T. M. Murali and Luz Cláudio and has published in prestigious journals such as Journal of Clinical Investigation, The Science of The Total Environment and PLoS Computational Biology.

In The Last Decade

Yan Chak Li

6 papers receiving 207 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yan Chak Li United States	4	103	93	55	44	33	8	210
Zachary H. Strasser United States	7	74 0.7×	53 0.6×	58 1.1×	30 0.7×	29 0.9×	12	222
Pranai Tandon United States	7	64 0.6×	78 0.8×	55 1.0×	24 0.5×	27 0.8×	16	215
Luca Mingardi United States	4	56 0.5×	58 0.6×	47 0.9×	21 0.5×	25 0.8×	5	183
Lara Tramontan Italy	10	73 0.7×	51 0.5×	26 0.5×	32 0.7×	82 2.5×	21	321
Ling Ma China	3	101 1.0×	49 0.5×	27 0.5×	76 1.7×	26 0.8×	5	310
Nakeya Dewaswala United States	9	67 0.7×	62 0.7×	21 0.4×	44 1.0×	26 0.8×	39	228
Majid Sorouri Iran	8	65 0.6×	90 1.0×	23 0.4×	44 1.0×	26 0.8×	17	291
Lin Luo China	6	130 1.3×	130 1.4×	20 0.4×	35 0.8×	21 0.6×	20	273
Mohak Gupta India	7	69 0.7×	56 0.6×	17 0.3×	18 0.4×	24 0.7×	22	238
Chiara D. Arru United States	9	74 0.7×	229 2.5×	49 0.9×	45 1.0×	17 0.5×	14	302

Countries citing papers authored by Yan Chak Li

Since Specialization

Citations

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

Fields of papers citing papers by Yan Chak Li

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yan Chak 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 Yan Chak Li. The network helps show where Yan Chak Li may publish in the future.

Co-authorship network of co-authors of Yan Chak Li

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

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Li, Yan Chak, et al.. (2025). The heterogeneity of youth at risk of prediabetes and diabetes: A latent class analysis of a United States national sample. Preventive Medicine. 199. 108384–108384.

2.

Li, Yan Chak, et al.. (2024). A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures. PLoS Computational Biology. 20(7). e1012302–e1012302. 7 indexed citations

3.

Sekar, John A. P., Yan Chak Li, Avner Schlessinger, & Gaurav Pandey. (2024). A web portal for exploring kinase-substrate interactions. npj Systems Biology and Applications. 10(1). 113–113. 1 indexed citations

4.

Li, Yan Chak, et al.. (2024). A Comprehensive Youth Diabetes Epidemiological Data Set and Web Portal: Resource Development and Case Studies. JMIR Public Health and Surveillance. 10. e53330–e53330. 1 indexed citations

5.

Amiri, Solmaz, Yan Chak Li, Dedra Buchwald, & Gaurav Pandey. (2024). Machine learning-driven identification of air toxic combinations associated with asthma symptoms among elementary school children in Spokane, Washington, USA. The Science of The Total Environment. 921. 171102–171102.

6.

Li, Yan Chak, et al.. (2022). Integrating multimodal data through interpretable heterogeneous ensembles. Bioinformatics Advances. 2(1). vbac065–vbac065. 9 indexed citations

7.

Li, Yan Chak, Hsiao‐Hsien Leon Hsu, Yoojin Chun, et al.. (2021). Machine learning–driven identification of early-life air toxic combinations associated with childhood asthma outcomes. Journal of Clinical Investigation. 131(22). 12 indexed citations

8.

Yadaw, Arjun S., Yan Chak Li, Sonali Bose, et al.. (2020). Clinical features of COVID-19 mortality: development and validation of a clinical prediction model. The Lancet Digital Health. 2(10). e516–e525. 180 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.

Explore authors with similar magnitude of impact