Katherine Pak

2.8k total citations · 1 hit paper
33 papers, 1.5k citations indexed

About

Katherine Pak is a scholar working on Epidemiology, Molecular Biology and Rheumatology. According to data from OpenAlex, Katherine Pak has authored 33 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Epidemiology, 15 papers in Molecular Biology and 11 papers in Rheumatology. Recurrent topics in Katherine Pak's work include Inflammatory Myopathies and Dermatomyositis (26 papers), Muscle Physiology and Disorders (13 papers) and Eosinophilic Disorders and Syndromes (9 papers). Katherine Pak is often cited by papers focused on Inflammatory Myopathies and Dermatomyositis (26 papers), Muscle Physiology and Disorders (13 papers) and Eosinophilic Disorders and Syndromes (9 papers). Katherine Pak collaborates with scholars based in United States, Spain and Palestinian Territory. Katherine Pak's co-authors include Andrew L. Mammen, Lisa Christopher‐Stine, Thomas E. Lloyd, Iago Pinal‐Fernandez, Frederick W. Miller, María Casal-Domínguez, Julie J. Paik, Jemima Albayda, Natalie Daya and Sharon R. Ghazarian and has published in prestigious journals such as The Journal of Immunology, Neurology and Scientific Reports.

In The Last Decade

Katherine Pak

33 papers receiving 1.5k citations

Hit Papers

Human endogenous retrovirus-K contributes to motor neuron... 2015 2026 2018 2022 2015 100 200 300
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.

  1. Human endogenous retrovirus-K contributes to motor neuron disease
    2015 340 citations Katherine Pak, Andrew L. Mammen et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Katherine Pak United States 18 1.0k 557 380 331 309 33 1.5k
Satoko Shimizu Japan 18 211 0.2× 406 0.7× 81 0.2× 53 0.2× 110 0.4× 67 1.2k
Marja W. Wessels Netherlands 24 341 0.3× 993 1.8× 50 0.1× 91 0.3× 59 0.2× 64 2.1k
Wassila Carpentier France 21 174 0.2× 685 1.2× 130 0.3× 787 2.4× 42 0.1× 32 2.0k
Mari‐Paz Rubio Spain 20 182 0.2× 703 1.3× 90 0.2× 85 0.3× 282 0.9× 29 1.7k
Barbara Rosicarelli Italy 18 254 0.3× 303 0.5× 362 1.0× 1.4k 4.1× 495 1.6× 22 2.8k
Nina Horelli‐Kuitunen Finland 18 90 0.1× 720 1.3× 105 0.3× 509 1.5× 131 0.4× 37 1.9k
Yuki Hitomi Japan 16 227 0.2× 444 0.8× 99 0.3× 267 0.8× 28 0.1× 39 1.1k
Wenwu Zhai United States 16 93 0.1× 543 1.0× 68 0.2× 365 1.1× 159 0.5× 19 1.3k
Alan M. Roth United States 20 150 0.1× 433 0.8× 97 0.3× 72 0.2× 421 1.4× 52 1.5k
Ron J. Feldman United States 19 87 0.1× 258 0.5× 359 0.9× 371 1.1× 37 0.1× 42 1.2k

Countries citing papers authored by Katherine Pak

Since Specialization
Citations

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

Fields of papers citing papers by Katherine Pak

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Katherine Pak

This figure shows the co-authorship network connecting the top 25 collaborators of Katherine Pak. A scholar is included among the top collaborators of Katherine Pak 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 Katherine Pak. Katherine Pak 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.
Pak, Katherine, Iago Pinal‐Fernandez, María Casal-Domínguez, et al.. (2025). Myositis-specific autoantibodies recognising Mi2 also target the AIRE protein at a shared PHD zinc finger. Annals of the Rheumatic Diseases. 85(2). 351–359. 1 indexed citations
2.
Pinal‐Fernandez, Iago, María Casal-Domínguez, Katherine Pak, et al.. (2025). Distinct cytokine and cytokine receptor expression patterns characterize different forms of myositis. Lara D. Veeken. 64(11). 5751–5760. 1 indexed citations
3.
Pinal‐Fernandez, Iago, María Casal-Domínguez, Katherine Pak, et al.. (2025). Anti-Mi2 autoantibodies target PHD fingers of SP140L and TIF1γ, while anti-TIF1γ autoantibodies primarily bind TIF1γ outside the PHD region. Lara D. Veeken. 64(12). 6371–6377. 2 indexed citations
4.
Torres‐Ruiz, Jiram, Iago Pinal‐Fernandez, Albert Selva-O’Callaghan, et al.. (2024). Nailfold capillaroscopy findings of a multicentric multi-ethnic cohort of patients with idiopathic inflammatory myopathies. Clinical and Experimental Rheumatology. 42(2). 367–376. 4 indexed citations
5.
Pak, Katherine, Edward P. Trieu, Kakali Sarkar, et al.. (2024). Myositis‐Associated Autoantibodies in Patients With Juvenile Myositis Are Associated With Refractory Disease and Mortality. Arthritis & Rheumatology. 76(6). 963–972. 1 indexed citations
6.
Pak, Katherine, Iago Pinal‐Fernandez, Willy A. Flegel, et al.. (2023). Autoantibodies Recognizing Specificity Protein 4 Co‐occur With Anti–Transcription Intermediary Factor 1 and Are Associated With Distinct Clinical Features and Immunogenetic Risk Factors in Juvenile Myositis. Arthritis & Rheumatology. 75(9). 1668–1677. 9 indexed citations
7.
Yang, Qingyuan, Laura Gutierrez‐Alamillo, Katherine Pak, et al.. (2023). Clinical Features and Immunogenetic Risk Factors Associated With Additional Autoantibodies in Anti‐Transcriptional Intermediary Factor 1γ Juvenile‐Onset Dermatomyositis. Arthritis & Rheumatology. 76(4). 631–637. 2 indexed citations
8.
Pinal‐Fernandez, Iago, María Casal-Domínguez, Katherine Pak, et al.. (2023). Identification of Unique microRNA Profiles in Different Types of Idiopathic Inflammatory Myopathy. Cells. 12(17). 2198–2198. 9 indexed citations
9.
Casal-Domínguez, María, Iago Pinal‐Fernandez, Katherine Pak, et al.. (2021). Performance of the 2017 European Alliance of Associations for Rheumatology/American College of Rheumatology Classification Criteria for Idiopathic Inflammatory Myopathies in Patients With Myositis‐Specific Autoantibodies. Arthritis & Rheumatology. 74(3). 508–517. 29 indexed citations
10.
Pinal‐Fernandez, Iago, Katherine Pak, Albert Gil‐Vila, et al.. (2021). Anti‐Cortactin Autoantibodies Are Associated With Key Clinical Features in Adult Myositis But Are Rarely Present in Juvenile Myositis. Arthritis & Rheumatology. 74(2). 358–364. 6 indexed citations
11.
Casal-Domínguez, María, Iago Pinal‐Fernandez, Katherine Pak, et al.. (2021). The indirect immunofluorescence assay autoantibody profiles of myositis patients without known myositis-specific autoantibodies. Clinical and Experimental Rheumatology. 39(3). 519–524. 2 indexed citations
12.
Pinal‐Fernandez, Iago, María Casal-Domínguez, Assia Derfoul, et al.. (2020). Machine learning algorithms reveal unique gene expression profiles in muscle biopsies from patients with different types of myositis. Annals of the Rheumatic Diseases. 79(9). 1234–1242. 101 indexed citations
13.
Pinal‐Fernandez, Iago, Katherine Pak, María Casal-Domínguez, et al.. (2019). Validation of anti-Mi2 autoantibody testing by line blot. Autoimmunity Reviews. 19(1). 102425–102425. 5 indexed citations
14.
Pinal‐Fernandez, Iago, María Casal-Domínguez, Assia Derfoul, et al.. (2019). Identification of distinctive interferon gene signatures in different types of myositis. Neurology. 93(12). e1193–e1204. 140 indexed citations
15.
Parks, Cassie, Katherine Pak, Iago Pinal‐Fernandez, et al.. (2019). Trim33 (Tif1γ) is not required for skeletal muscle development or regeneration but suppresses cholecystokinin expression. Scientific Reports. 9(1). 18507–18507. 3 indexed citations
16.
Sek, Albert C., Ian N. Moore, Margery Smelkinson, et al.. (2019). Eosinophils Do Not Drive Acute Muscle Pathology in the mdx Mouse Model of Duchenne Muscular Dystrophy. The Journal of Immunology. 203(2). 476–484. 18 indexed citations
17.
Pinal‐Fernandez, Iago, Takayuki Kishi, Katherine Pak, et al.. (2018). Anti-NT5C1A autoantibodies are associated with more severe disease in patients with juvenile myositis. Annals of the Rheumatic Diseases. 77(5). 714–719. 30 indexed citations
18.
Christopher‐Stine, Lisa, Sharon R. Ghazarian, Katherine Pak, et al.. (2012). Antibody levels correlate with creatine kinase levels and strength in anti–3‐hydroxy‐3‐methylglutaryl‐coenzyme A reductase–associated autoimmune myopathy. Arthritis & Rheumatism. 64(12). 4087–4093. 189 indexed citations
19.
Mammen, Andrew L., Katherine Pak, Emma Williams, et al.. (2011). Rarity of anti–3‐hydroxy‐3‐methylglutaryl‐coenzyme A reductase antibodies in statin users, including those with self‐limited musculoskeletal side effects. Arthritis Care & Research. 64(2). 269–272. 157 indexed citations
20.
Pak, Katherine, Seok Ju Seong, Tae Jin Kim, et al.. (2002). Clinical diversity of struma ovarii. Obstetrics & Gynecology Science. 45(5). 748–752. 12 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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