Amelia L.M. Tan

1.5k total citations
10 papers, 53 citations indexed

About

Amelia L.M. Tan is a scholar working on Molecular Biology, Artificial Intelligence and Health Information Management. According to data from OpenAlex, Amelia L.M. Tan has authored 10 papers receiving a total of 53 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 4 papers in Artificial Intelligence and 2 papers in Health Information Management. Recurrent topics in Amelia L.M. Tan's work include Biomedical Text Mining and Ontologies (2 papers), Bioinformatics and Genomic Networks (2 papers) and Metabolism, Diabetes, and Cancer (2 papers). Amelia L.M. Tan is often cited by papers focused on Biomedical Text Mining and Ontologies (2 papers), Bioinformatics and Genomic Networks (2 papers) and Metabolism, Diabetes, and Cancer (2 papers). Amelia L.M. Tan collaborates with scholars based in United States, Singapore and France. Amelia L.M. Tan's co-authors include Enrico Petretto, John G. Logan, Theodore G. Drivas, Pankhuri Singhal, Jeong‐Hun Ko, Peter I. Croucher, Kee‐Beom Kim, Marylyn D. Ritchie, Maxime Rotival and Graham R. Williams and has published in prestigious journals such as Bioinformatics, The Journal of Clinical Endocrinology & Metabolism and Scientific Reports.

In The Last Decade

Amelia L.M. Tan

9 papers receiving 53 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Amelia L.M. Tan United States	4	28	8	7	7	5	10	53
Paul Hale United States	2	35 1.3×	4 0.5×	6 0.9×	19 2.7×	7 1.4×	2	55
Florian Buerger United States	4	49 1.8×	12 1.5×	6 0.9×	15 2.1×	10 2.0×	11	92
Yoann Vial France	5	31 1.1×	5 0.6×	11 1.6×	13 1.9×	5 1.0×	13	57
Miriam Mengoni Germany	6	18 0.6×	7 0.9×	23 3.3×	3 0.4×	5 1.0×	15	72
Jolanta Kalniņa Latvia	3	17 0.6×	3 0.4×	8 1.1×	4 0.6×	3 0.6×	10	38
Christine N. Goldfarb United States	2	20 0.7×	5 0.6×	4 0.6×	5 0.7×	7 1.4×	2	31
Beatriz Gómez‐Santos Spain	3	23 0.8×	11 1.4×	4 0.6×	4 0.6×	18 3.6×	5	50
Zhi-Hong Cheng China	2	26 0.9×	4 0.5×	3 0.4×	7 1.0×	5 1.0×	5	46
Sinjini Sikdar United States	5	31 1.1×	9 1.1×	2 0.3×	5 0.7×	6 1.2×	19	69
Victor E. Ortega United States	4	19 0.7×	4 0.5×	3 0.4×	7 1.0×	4 0.8×	5	41

Countries citing papers authored by Amelia L.M. Tan

Since Specialization

Citations

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

Fields of papers citing papers by Amelia L.M. Tan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Amelia L.M. Tan. 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 Amelia L.M. Tan. The network helps show where Amelia L.M. Tan may publish in the future.

Co-authorship network of co-authors of Amelia L.M. Tan

This figure shows the co-authorship network connecting the top 25 collaborators of Amelia L.M. Tan. A scholar is included among the top collaborators of Amelia L.M. Tan 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 Amelia L.M. Tan. Amelia L.M. Tan 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:

10 of 10 papers shown

1.

Healey, Elizabeth, et al.. (2025). A case study on using a large language model to analyze continuous glucose monitoring data. Scientific Reports. 15(1). 1143–1143. 3 indexed citations

2.

Gonçalves, Rafael S., et al.. (2024). The text2term tool to map free-text descriptions of biomedical terms to ontologies. Database. 2024. 2 indexed citations

3.

Chen, Siyuan, Amelia L.M. Tan, Jenny Mao, et al.. (2024). Polygenic risk scores for autoimmune related diseases are significantly different in cancer exceptional responders. npj Precision Oncology. 8(1). 120–120. 1 indexed citations

4.

Noori, Ayush, Michelle M. Li, Amelia L.M. Tan, & Marinka Žitnik. (2023). Metapaths: similarity search in heterogeneous knowledge graphs via meta-paths. Bioinformatics. 39(5). 3 indexed citations

5.

Kohane, Isaac S., et al.. (2023). The digital–physical divide for pathology research. The Lancet Digital Health. 5(12). e859–e861.

6.

Singhal, Pankhuri, Amelia L.M. Tan, Theodore G. Drivas, et al.. (2023). Opportunities and challenges for biomarker discovery using electronic health record data. Trends in Molecular Medicine. 29(9). 765–776. 9 indexed citations

7.

Tan, Amelia L.M., Esteban J. Parra, Miguel Cruz, et al.. (2020). Genome-wide meta-analysis associates GPSM1 with type 2 diabetes, a plausible gene involved in skeletal muscle function. Journal of Human Genetics. 65(4). 411–420. 7 indexed citations

8.

Pereira, Marie, Jeong‐Hun Ko, John G. Logan, et al.. (2020). A trans-eQTL network regulates osteoclast multinucleation and bone mass. eLife. 9. 23 indexed citations

9.

Tan, Amelia L.M., Sarah R. Langley, Chee Fan Tan, et al.. (2018). Ethnicity-Specific Skeletal Muscle Transcriptional Signatures and Their Relevance to Insulin Resistance in Singapore. The Journal of Clinical Endocrinology & Metabolism. 104(2). 465–486. 4 indexed citations

10.

Tan, Amelia L.M., et al.. (2014). CATIONIC BOLAAMPHIPHILES FOR GENE DELIVERY. 10(1). 25–38. 1 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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