Qi Mi

4.3k total citations
79 papers, 2.1k citations indexed

About

Qi Mi is a scholar working on Molecular Biology, Occupational Therapy and Epidemiology. According to data from OpenAlex, Qi Mi has authored 79 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 22 papers in Occupational Therapy and 19 papers in Epidemiology. Recurrent topics in Qi Mi's work include Occupational Health and Performance (17 papers), Sepsis Diagnosis and Treatment (10 papers) and Immune Response and Inflammation (9 papers). Qi Mi is often cited by papers focused on Occupational Health and Performance (17 papers), Sepsis Diagnosis and Treatment (10 papers) and Immune Response and Inflammation (9 papers). Qi Mi collaborates with scholars based in United States, China and Australia. Qi Mi's co-authors include Yoram Vodovotz, Gary An, Joyeeta Dutta‐Moscato, Rubén Zamora, Timothy R. Billiar, Rami A. Namas, Gilles Clermont, Derek Barclay, Cordelia Ziraldo and Khalid Almahmoud and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Annals of Surgery.

In The Last Decade

Qi Mi

78 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Qi Mi United States 26 602 453 346 218 206 79 2.1k
Idalia Garza‐Veloz Mexico 22 614 1.0× 177 0.4× 281 0.8× 229 1.1× 164 0.8× 95 2.4k
John F. O’Brien United States 33 1.1k 1.8× 328 0.7× 179 0.5× 706 3.2× 218 1.1× 144 4.0k
Bing Ma China 20 315 0.5× 245 0.5× 177 0.5× 133 0.6× 431 2.1× 85 1.9k
Bian Wu China 25 501 0.8× 186 0.4× 247 0.7× 494 2.3× 372 1.8× 109 1.8k
Isabella Ellinger Austria 21 495 0.8× 250 0.6× 229 0.7× 98 0.4× 66 0.3× 62 1.9k
Srinivasan Rajaraman United States 35 1.2k 2.0× 709 1.6× 324 0.9× 906 4.2× 316 1.5× 154 4.3k
Yasuhiro Aoki Japan 27 726 1.2× 128 0.3× 86 0.2× 146 0.7× 137 0.7× 92 2.4k
Xiaoyan Jiang China 26 489 0.8× 353 0.8× 71 0.2× 108 0.5× 95 0.5× 81 1.6k
Matthew D. Davis United States 43 1.3k 2.1× 1.0k 2.2× 213 0.6× 311 1.4× 185 0.9× 85 10.5k
Katsuhito Fujiu Japan 30 1.3k 2.2× 393 0.9× 355 1.0× 434 2.0× 253 1.2× 191 3.6k

Countries citing papers authored by Qi Mi

Since Specialization
Citations

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

Fields of papers citing papers by Qi Mi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qi Mi

This figure shows the co-authorship network connecting the top 25 collaborators of Qi Mi. A scholar is included among the top collaborators of Qi Mi 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 Qi Mi. Qi Mi 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.
Qi, Qiuchen, et al.. (2024). Systematic analysis of PANoptosis-related genes identifies XIAP as a functional oncogene in breast cancer. Gene. 912. 148355–148355. 7 indexed citations
2.
Li, Peilong, Qi Mi, Suzhen Yan, et al.. (2023). Characterization of circSCL38A1 as a novel oncogene in bladder cancer via targeting ILF3/TGF-β2 signaling axis. Cell Death and Disease. 14(1). 59–59. 22 indexed citations
3.
Conkright, William R., Aaron M. Sinnott, Meaghan E. Beckner, et al.. (2022). Less daytime sleepiness and slow wave activity during sleep predict better physical readiness in military personnel. Sleep Health. 9(1). 93–99. 3 indexed citations
4.
Koltun, Kristen J., et al.. (2022). Tibial Bone Geometry Is Associated With Bone Stress Injury During Military Training in Men and Women. Frontiers in Physiology. 13. 803219–803219. 14 indexed citations
5.
6.
Mi, Qi, Richard J. Simpson, Scott M. Graham, et al.. (2020). Load Magnitude and Locomotion Pattern Alter Locomotor System Function in Healthy Young Adult Women. Frontiers in Bioengineering and Biotechnology. 8. 582219–582219. 14 indexed citations
7.
Xu, Bin, Lujun Chen, Xiao Zheng, et al.. (2020). Using machine learning to predict ovarian cancer. International Journal of Medical Informatics. 141. 104195–104195. 86 indexed citations
8.
Zamora, Rubén, Sebastian Korff, Qi Mi, et al.. (2018). A computational analysis of dynamic, multi-organ inflammatory crosstalk induced by endotoxin in mice. PLoS Computational Biology. 14(11). e1006582–e1006582. 21 indexed citations
9.
Turner, Rose, Abrahim I. Orabi, Craig A. Byersdorfer, et al.. (2017). Risk Factors for Asparaginase-associated Pancreatitis. Journal of Clinical Gastroenterology. 51(10). 907–913. 33 indexed citations
10.
Constantine, Gregory, Qi Mi, Andrew Abboud, et al.. (2016). Dynamic Profiling: Modeling the Dynamics of Inflammation and Predicting Outcomes in Traumatic Brain Injury Patients. Frontiers in Pharmacology. 7. 383–383. 9 indexed citations
11.
Namas, Rami A., Khalid Almahmoud, Qi Mi, et al.. (2016). Individual-specific principal component analysis of circulating inflammatory mediators predicts early organ dysfunction in trauma patients. Journal of Critical Care. 36. 146–153. 44 indexed citations
12.
Zaaqoq, Akram, Rami A. Namas, Khalid Almahmoud, et al.. (2014). Inducible Protein-10, a Potential Driver of Neurally Controlled Interleukin-10 and Morbidity in Human Blunt Trauma*. Critical Care Medicine. 42(6). 1487–1497. 46 indexed citations
13.
Ziraldo, Cordelia, Qi Mi, Gary An, & Yoram Vodovotz. (2013). Computational Modeling of Inflammation and Wound Healing. Advances in Wound Care. 2(9). 527–537. 31 indexed citations
14.
Ziraldo, Cordelia, Yoram Vodovotz, Rami A. Namas, et al.. (2013). Central Role for MCP-1/CCL2 in Injury-Induced Inflammation Revealed by In Vitro, In Silico, and Clinical Studies. PLoS ONE. 8(12). e79804–e79804. 76 indexed citations
15.
Solovyev, Alexey, et al.. (2013). Hybrid Equation/Agent-Based Model of Ischemia-Induced Hyperemia and Pressure Ulcer Formation Predicts Greater Propensity to Ulcerate in Subjects with Spinal Cord Injury. PLoS Computational Biology. 9(5). e1003070–e1003070. 29 indexed citations
16.
Arciero, Julia, Qi Mi, Maria Branca, David J. Hackam, & David Swigon. (2011). Continuum Model of Collective Cell Migration in Wound Healing and Colony Expansion. Biophysical Journal. 100(3). 535–543. 94 indexed citations
17.
Mi, Qi, Gregory Constantine, Cordelia Ziraldo, et al.. (2011). A Dynamic View of Trauma/Hemorrhage-Induced Inflammation in Mice: Principal Drivers and Networks. PLoS ONE. 6(5). e19424–e19424. 83 indexed citations
18.
Swigon, David, Julia Arciero, Qi Mi, & David J. Hackam. (2010). Continuum Elastic Model of Epithelial Sheet Migration. Biophysical Journal. 98(3). 163a–163a. 2 indexed citations
19.
Vodovotz, Yoram, Gregory Constantine, James R. Faeder, et al.. (2010). Translational Systems Approaches to the Biology of Inflammation and Healing. Immunopharmacology and Immunotoxicology. 32(2). 181–195. 50 indexed citations
20.
Mi, Qi, David Swigon, Béatrice Rivière, et al.. (2007). One-Dimensional Elastic Continuum Model of Enterocyte Layer Migration. Biophysical Journal. 93(11). 3745–3752. 21 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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