Daniel G. Pankratz

1.5k total citations
17 papers, 1.0k citations indexed

About

Daniel G. Pankratz is a scholar working on Molecular Biology, Pulmonary and Respiratory Medicine and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Daniel G. Pankratz has authored 17 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 5 papers in Pulmonary and Respiratory Medicine and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Daniel G. Pankratz's work include DNA Repair Mechanisms (4 papers), Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (3 papers) and Molecular Biology Techniques and Applications (3 papers). Daniel G. Pankratz is often cited by papers focused on DNA Repair Mechanisms (4 papers), Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (3 papers) and Molecular Biology Techniques and Applications (3 papers). Daniel G. Pankratz collaborates with scholars based in United States and Italy. Daniel G. Pankratz's co-authors include Carrolee Barlow, Todd A. Carter, David J. Lockhart, Rie Yasuda, Lisa Wodicka, Mark Mayford, Rickard Sandberg, Anthony Wynshaw‐Boris, Zoë Weaver Ohler and Allen Coleman and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Clinical Oncology and Blood.

In The Last Decade

Daniel G. Pankratz

17 papers receiving 974 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel G. Pankratz United States 11 559 247 211 114 108 17 1.0k
Joyce C. Wu United States 16 905 1.6× 153 0.6× 242 1.1× 180 1.6× 129 1.2× 20 1.5k
Sönke Friedrichsen Germany 15 448 0.8× 188 0.8× 157 0.7× 82 0.7× 50 0.5× 19 827
Mette A. Peters United States 17 580 1.0× 56 0.2× 286 1.4× 87 0.8× 104 1.0× 29 1.1k
Brandon C. Sos United States 9 773 1.4× 145 0.6× 139 0.7× 46 0.4× 152 1.4× 13 1.1k
Bartosz Wojtaś Poland 23 715 1.3× 196 0.8× 94 0.4× 138 1.2× 326 3.0× 65 1.5k
A. John Clark United Kingdom 15 594 1.1× 137 0.6× 299 1.4× 98 0.9× 47 0.4× 19 1.1k
Chia-Ping Chang United States 10 756 1.4× 395 1.6× 107 0.5× 137 1.2× 40 0.4× 10 1.4k
Hooshang Lahooti Australia 14 547 1.0× 201 0.8× 749 3.5× 117 1.0× 46 0.4× 32 1.1k
Keiko Nakanishi Japan 17 583 1.0× 38 0.2× 92 0.4× 111 1.0× 80 0.7× 30 989
Mie Nakaya Japan 7 1.1k 1.9× 116 0.5× 113 0.5× 120 1.1× 55 0.5× 8 1.4k

Countries citing papers authored by Daniel G. Pankratz

Since Specialization
Citations

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

Fields of papers citing papers by Daniel G. Pankratz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel G. Pankratz. 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 Daniel G. Pankratz. The network helps show where Daniel G. Pankratz may publish in the future.

Co-authorship network of co-authors of Daniel G. Pankratz

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

All Works

17 of 17 papers shown
1.
Pankratz, Daniel G., et al.. (2022). Analysis of Research Productivity and Assessment of Geographical Region in the General Surgery Match: How Much is Enough?. Journal of surgical education. 79(6). 1426–1434. 6 indexed citations
2.
Johnson, Marla, Daniel G. Pankratz, Grazyna Fedorowicz, et al.. (2021). Analytical validation of the Percepta genomic sequencing classifier; an RNA next generation sequencing assay for the assessment of Lung Cancer risk of suspicious pulmonary nodules. BMC Cancer. 21(1). 400–400. 5 indexed citations
3.
Mazzone, Peter J., Carla Lamb, Kimberly Rieger‐Christ, et al.. (2021). Early candidate nasal swab classifiers developed using machine learning and whole transcriptome sequencing may improve early lung cancer detection.. Journal of Clinical Oncology. 39(15_suppl). 8551–8551. 3 indexed citations
4.
Choi, Yoonha, Jianghan Qu, Yangyang Hao, et al.. (2020). Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts. BMC Medical Genomics. 13(S10). 151–151. 17 indexed citations
5.
Choi, Yoonha, Daniel G. Pankratz, Thomas V. Colby, et al.. (2018). Identification of usual interstitial pneumonia pattern using RNA-Seq and machine learning: challenges and solutions. BMC Genomics. 19(S2). 101–101. 15 indexed citations
6.
Pankratz, Daniel G., Yoonha Choi, Grazyna Fedorowicz, et al.. (2017). Usual Interstitial Pneumonia Can Be Detected in Transbronchial Biopsies Using Machine Learning. Annals of the American Thoracic Society. 14(11). 1646–1654. 53 indexed citations
7.
Choi, Yoonha, Jiayi Lu, Zhanzhi Hu, et al.. (2017). Analytical performance of Envisia: a genomic classifier for usual interstitial pneumonia. BMC Pulmonary Medicine. 17(1). 141–141. 19 indexed citations
8.
Pankratz, Daniel G., Zhanzhi Hu, Su Yeon Kim, et al.. (2016). Analytical Performance of a Gene Expression Classifier for Medullary Thyroid Carcinoma. Thyroid. 26(11). 1573–1580. 10 indexed citations
9.
10.
Walsh, P. Sean, Jonathan I. Wilde, Edward Tom, et al.. (2012). Analytical Performance Verification of a Molecular Diagnostic for Cytology-Indeterminate Thyroid Nodules. The Journal of Clinical Endocrinology & Metabolism. 97(12). E2297–E2306. 45 indexed citations
11.
Winrow, Christopher J., Daniel G. Pankratz, Cecile Rose T. Vibat, et al.. (2005). Aberrant recombination involving the granzyme locus occurs in Atm−/− T-cell lymphomas. Human Molecular Genetics. 14(18). 2671–2684. 10 indexed citations
12.
Pankratz, Daniel G. & Susan L. Forsburg. (2005). Meiotic S-Phase Damage Activates Recombination without Checkpoint Arrest. Molecular Biology of the Cell. 16(4). 1651–1660. 21 indexed citations
13.
Zapala, Matthew A., et al.. (2002). Software and methods for oligonucleotide and cDNA array data analysis. Genome biology. 3(6). SOFTWARE0001–SOFTWARE0001. 37 indexed citations
14.
Liyanage, Marek, Zoë Weaver Ohler, Carrolee Barlow, et al.. (2000). Abnormal rearrangement within the α/δ T-cell receptor locus in lymphomas from Atm-deficient mice. Blood. 96(5). 1940–1946. 127 indexed citations
15.
Sandberg, Rickard, Rie Yasuda, Daniel G. Pankratz, et al.. (2000). Regional and strain-specific gene expression mapping in the adult mouse brain. Proceedings of the National Academy of Sciences. 97(20). 11038–11043. 380 indexed citations
16.
Liyanage, Marek, Zoë Weaver Ohler, Carrolee Barlow, et al.. (2000). Abnormal rearrangement within the α/δ T-cell receptor locus in lymphomas from Atm-deficient mice. Blood. 96(5). 1940–1946. 10 indexed citations
17.
Kaneshige, Masahiro, Kumiko Kaneshige, Alexandra Dace, et al.. (2000). Mice with a targeted mutation in the thyroid hormone β receptor gene exhibit impaired growth and resistance to thyroid hormone. Proceedings of the National Academy of Sciences. 97(24). 13209–13214. 225 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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