Craig H. Mermel

48.5k total citations · 4 hit papers
31 papers, 8.0k citations indexed

About

Craig H. Mermel is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Oncology. According to data from OpenAlex, Craig H. Mermel has authored 31 papers receiving a total of 8.0k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Radiology, Nuclear Medicine and Imaging, 11 papers in Artificial Intelligence and 9 papers in Oncology. Recurrent topics in Craig H. Mermel's work include Radiomics and Machine Learning in Medical Imaging (12 papers), AI in cancer detection (11 papers) and Cancer Genomics and Diagnostics (6 papers). Craig H. Mermel is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (12 papers), AI in cancer detection (11 papers) and Cancer Genomics and Diagnostics (6 papers). Craig H. Mermel collaborates with scholars based in United States, Austria and United Kingdom. Craig H. Mermel's co-authors include Matthew Meyerson, Gad Getz, Rameen Beroukhim, Steven E. Schumacher, Barbara Hill, Michael S. Lawrence, Stacey Gabriel, Todd R. Golub, Levi A. Garraway and Petar Stojanov and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Craig H. Mermel

31 papers receiving 7.9k citations

Hit Papers

align trajectories

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.

Discovery and saturation analysis of cancer genes across 21 tumour types

2014 2.1k citations Michael S. Lawrence, Petar Stojanov et al. Nature profile →
GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers

2011 2.0k citations Craig H. Mermel, Steven E. Schumacher et al. Genome biology profile →
Pan-cancer patterns of somatic copy number alteration

2013 1.2k citations Travis Zack, Steven E. Schumacher et al. Nature Genetics profile →
Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

2019 302 citations Kunal Nagpal, Davis Foote et al. npj Digital Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Craig H. Mermel United States	24	4.5k	3.0k	2.1k	1.8k	880	31	8.0k
Kenna Shaw United States	28	4.7k 1.0×	3.2k 1.1×	2.3k 1.1×	1.6k 0.9×	988 1.1×	83	7.9k
Nicolai J. Birkbak Denmark	31	3.0k 0.7×	2.7k 0.9×	2.8k 1.4×	1.6k 0.9×	655 0.7×	67	7.1k
Phillip G. Febbo United States	38	4.8k 1.1×	2.7k 0.9×	1.9k 0.9×	3.3k 1.8×	477 0.5×	114	8.9k
Lao H. Saal Sweden	30	4.7k 1.1×	2.0k 0.7×	2.1k 1.0×	1.1k 0.6×	471 0.5×	76	7.2k
Katherine A. Hoadley United States	41	3.5k 0.8×	3.0k 1.0×	3.8k 1.8×	1.5k 0.9×	401 0.5×	140	8.1k
Julien Caldéraro France	45	3.2k 0.7×	2.4k 0.8×	3.3k 1.6×	1.6k 0.9×	984 1.1×	164	10.2k
Shridar Ganesan United States	52	7.8k 1.8×	3.1k 1.0×	3.5k 1.7×	1.4k 0.8×	663 0.8×	204	12.7k
Ronglai Shen United States	48	6.2k 1.4×	2.6k 0.9×	3.3k 1.6×	4.1k 2.3×	704 0.8×	102	11.0k
David Voduc Canada	14	3.1k 0.7×	4.5k 1.5×	4.5k 2.2×	1.6k 0.9×	1.0k 1.2×	20	8.0k
James D. Brenton United Kingdom	47	5.9k 1.3×	6.2k 2.0×	4.0k 1.9×	2.6k 1.4×	1.7k 1.9×	167	12.5k

Countries citing papers authored by Craig H. Mermel

Since Specialization

Citations

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

Fields of papers citing papers by Craig H. Mermel

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Craig H. Mermel. 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 Craig H. Mermel. The network helps show where Craig H. Mermel may publish in the future.

Co-authorship network of co-authors of Craig H. Mermel

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

All Works

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20 of 20 papers shown

Konrad, Peter E., et al.. (2025). First-in-human experience performing high-resolution cortical mapping using a novel microelectrode array containing 1024 electrodes. Journal of Neural Engineering. 22(2). 26009–26009. 3 indexed citations

Rapoport, Benjamin I., Mark Hettick, Elton Ho, et al.. (2024). High-resolution Cortical Mapping with Conformable Microelectrodes on a Thousand-electrode Scale: A First-in-human Study (S23.008). Neurology. 102(7_supplement_1). 2 indexed citations

Krogue, Justin D., Shekoofeh Azizi, Fraser Elisabeth Tan, et al.. (2023). Predicting lymph node metastasis from primary tumor histology and clinicopathologic factors in colorectal cancer using deep learning. SHILAP Revista de lepidopterología. 3(1). 59–59. 12 indexed citations

Gamble, Paul, Ronnachai Jaroensri, Hongwu Wang, et al.. (2021). Determining breast cancer biomarker status and associated morphological features using deep learning. Communications Medicine. 1(1). 14–14. 86 indexed citations

Chen, Po-Hsuan Cameron, Craig H. Mermel, & Yun Liu. (2021). Evaluation of artificial intelligence on a reference standard based on subjective interpretation. The Lancet Digital Health. 3(11). e693–e695. 33 indexed citations

Sadhwani, Apaar, Ali Behrooz, Trissia Brown, et al.. (2021). Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images. Scientific Reports. 11(1). 16605–16605. 34 indexed citations

Steiner, David F., Po-Hsuan Cameron Chen, & Craig H. Mermel. (2020). Closing the translation gap: AI applications in digital pathology. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer. 1875(1). 188452–188452. 51 indexed citations

Nagpal, Kunal, Davis Foote, Yun Liu, et al.. (2019). Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. npj Digital Medicine. 2(1). 48–48. 302 indexed citations breakdown →

Parikh, Ravi B., Andrew Gdowski, Debra A. Patt, et al.. (2019). Using Big Data and Predictive Analytics to Determine Patient Risk in Oncology. American Society of Clinical Oncology Educational Book. 39(39). e53–e58. 25 indexed citations

10.

Chen, Po-Hsuan Cameron, Krishna Gadepalli, Robert MacDonald, et al.. (2019). An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis. Nature Medicine. 25(9). 1453–1457. 186 indexed citations

11.

Kugel, Sita, Jessica L. Feldman, Mark A. Klein, et al.. (2015). Identification of and Molecular Basis for SIRT6 Loss-of-Function Point Mutations in Cancer. Cell Reports. 13(3). 479–488. 59 indexed citations

12.

Lawrence, Michael S., Petar Stojanov, Craig H. Mermel, et al.. (2014). Discovery and saturation analysis of cancer genes across 21 tumour types. Nature. 505(7484). 495–501. 2059 indexed citations breakdown →

13.

Zack, Travis, Steven E. Schumacher, Scott L. Carter, et al.. (2013). Pan-cancer patterns of somatic copy number alteration. Nature Genetics. 45(10). 1134–1140. 1243 indexed citations breakdown →

14.

Monti, Stefano, Bjoern Chapuy, Kunihiko Takeyama, et al.. (2012). Integrative Analysis Reveals an Outcome-Associated and Targetable Pattern of p53 and Cell Cycle Deregulation in Diffuse Large B Cell Lymphoma. Cancer Cell. 22(3). 359–372. 127 indexed citations

15.

Solimini, Nicole L., Qikai Xu, Craig H. Mermel, et al.. (2012). Recurrent Hemizygous Deletions in Cancers May Optimize Proliferative Potential. Science. 337(6090). 104–109. 137 indexed citations

16.

Rosell, Rafael, B. Massuti Sureda, Carlota Costa, et al.. (2012). Concomitant Actionable Mutations and Overall Survival (OS) in Egfr-Mutant Non-Small-Cell Lung Cancer (NSCLC) Patients (P) Included in The Eurtac Trial: EGFR L858R, EGFR T790M, TP53 R273H and EML4-ALK (V3). Annals of Oncology. 23. ixe22–ixe22. 15 indexed citations

17.

Baron, Jason M., Craig H. Mermel, Kent Lewandrowski, & Anand S. Dighe. (2012). Detection of Preanalytic Laboratory Testing Errors Using a Statistically Guided Protocol. American Journal of Clinical Pathology. 138(3). 406–413. 29 indexed citations

18.

Dutt, Amit, Alex H. Ramos, Peter S. Hammerman, et al.. (2011). Inhibitor-Sensitive FGFR1 Amplification in Human Non-Small Cell Lung Cancer. PLoS ONE. 6(6). e20351–e20351. 296 indexed citations

19.

Mermel, Craig H., Steven E. Schumacher, Barbara Hill, et al.. (2011). GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome biology. 12(4). R41–R41. 2008 indexed citations breakdown →

20.

Viswanathan, Srinivas R., Craig H. Mermel, Jun Lü, et al.. (2009). microRNA Expression during Trophectoderm Specification. PLoS ONE. 4(7). e6143–e6143. 77 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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