Mike J. Tsay

771 total citations · 1 hit paper
7 papers, 518 citations indexed

About

Mike J. Tsay is a scholar working on Molecular Biology, Oncology and Cancer Research. According to data from OpenAlex, Mike J. Tsay has authored 7 papers receiving a total of 518 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 2 papers in Oncology and 2 papers in Cancer Research. Recurrent topics in Mike J. Tsay's work include RNA modifications and cancer (2 papers), MicroRNA in disease regulation (2 papers) and Acute Myeloid Leukemia Research (1 paper). Mike J. Tsay is often cited by papers focused on RNA modifications and cancer (2 papers), MicroRNA in disease regulation (2 papers) and Acute Myeloid Leukemia Research (1 paper). Mike J. Tsay collaborates with scholars based in Canada, United States and Slovakia. Mike J. Tsay's co-authors include Igor Jurišica, Chiara Pastrello, Tomáš Tokár, Mark Abovsky, Anne-Christin Hauschild, John SantaLucia, Donald A. Hicks, Nana Lee, John Yee and Calum MacAulay and has published in prestigious journals such as Nucleic Acids Research, Blood and Cancer Research.

In The Last Decade

Mike J. Tsay

7 papers receiving 513 citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

mirDIP 4.1—integrative database of human microRNA target predictions

2017 419 citations Tomáš Tokár, Chiara Pastrello et al. Nucleic Acids Research profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mike J. Tsay Canada	5	384	318	45	45	37	7	518
M Chen China	7	228 0.6×	183 0.6×	63 1.4×	47 1.0×	41 1.1×	16	370
Saibyasachi N. Choudhury United States	7	270 0.7×	254 0.8×	44 1.0×	43 1.0×	25 0.7×	7	404
Chenqi Lu China	13	471 1.2×	230 0.7×	54 1.2×	38 0.8×	47 1.3×	28	622
Wanju Jiao China	12	383 1.0×	284 0.9×	54 1.2×	44 1.0×	21 0.6×	15	490
Shangxin Zhang China	12	320 0.8×	231 0.7×	59 1.3×	44 1.0×	73 2.0×	32	506
Zhonghua Du China	10	553 1.4×	367 1.2×	43 1.0×	48 1.1×	21 0.6×	29	647
Tait Huso United States	8	273 0.7×	194 0.6×	55 1.2×	44 1.0×	22 0.6×	10	359
Dawei Zheng China	13	462 1.2×	225 0.7×	53 1.2×	35 0.8×	99 2.7×	30	620

Countries citing papers authored by Mike J. Tsay

Since Specialization

Citations

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

Fields of papers citing papers by Mike J. Tsay

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mike J. Tsay. 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 Mike J. Tsay. The network helps show where Mike J. Tsay may publish in the future.

Co-authorship network of co-authors of Mike J. Tsay

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

All Works

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7 of 7 papers shown

1.

Geraci, Joseph, Bessi Qorri, Katherine A. Gifford, et al.. (2024). Using Machine Learning to Explore Multimodal Digital Markers for Early Detection of Cognitive Impairment in Alzheimer’s Disease. Alzheimer s & Dementia. 20(S2). 1 indexed citations

2.

Qorri, Bessi, et al.. (2024). Abstract LB395: NetraAI-driven discovery of novel biomarkers in MSI-high colon cancer for precision immunotherapy. Cancer Research. 84(7_Supplement). LB395–LB395. 1 indexed citations

3.

Kaufmann, Kerstin B., Laura García‐Prat, Qiang Liu, et al.. (2019). A stemness screen reveals C3orf54/INKA1 as a promoter of human leukemia stem cell latency. Blood. 133(20). 2198–2211. 18 indexed citations

4.

Tokár, Tomáš, Chiara Pastrello, Mark Abovsky, et al.. (2017). mirDIP 4.1—integrative database of human microRNA target predictions. Nucleic Acids Research. 46(D1). D360–D370. 419 indexed citations breakdown →

5.

Becker‐Santos, Daiana D., Kelsie L. Thu, John C. English, et al.. (2016). Developmental transcription factor NFIB is a putative target of oncofetal miRNAs and is associated with tumour aggressiveness in lung adenocarcinoma. The Journal of Pathology. 240(2). 161–172. 38 indexed citations

6.

Tsay, Mike J., et al.. (2010). Thermodynamic contributions of single internal rA·dA, rC·dC, rG·dG and rU·dT mismatches in RNA/DNA duplexes. Nucleic Acids Research. 39(5). 1894–1902. 31 indexed citations

7.

Tsay, Mike J., et al.. (2003). Identification of the “missing domain” of the rat copper-transporting ATPase, atp7b: insight into the structural and metal binding characteristics of its N-terminal copper-binding domain. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease. 1688(1). 78–85. 10 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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