William P. Accomando

4.3k citations
14 papers · 2.5k · 1 hit paper · h-index 9

Impact in

Papers in

    • Epigenetics and DNA Methylation 5
    • RNA modifications and cancer 2
    • Glioma Diagnosis and Treatment 3
    • Genetic Syndromes and Imprinting 1

William P. Accomando

12 papers receiving 2.5k citations

William P. Accomando's Hit Papers

DNA methylation arrays as surrogate measures of cell mixture distribution 2012 · 2.1k citations
2.1k0+4+9Years since publication50010001.5k2.0k

Peers

William P. Accomando
Comparison fields: 5 of 97
  • Molecular Biology 1.7k
  • Pediatrics, Perinatology and Child Health 368
  • Biological Psychiatry 47
  • Genetics 455
  • Health, Toxicology and Mutagenesis 197
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William P. Accomando relative to Peter Murakami United States Peter Murakami's profile →
Citations per field
00.5×4.7×
Peter Murakami · 1×
Citations per year

Countries citing papers authored by William P. Accomando

Since Specialization
Citations

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

Fields of papers citing papers by William P. Accomando

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William P. Accomando. 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 William P. Accomando. The network helps show where William P. Accomando may publish in the future.

Co-authors

The 25 scholars most cited alongside William P. Accomando, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with William P. Accomando Line = papers co-authored together William P. Accomando links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1
DNA methylation arrays as surrogate measures of cell mixture distribution
Hit paper breakdown →
20122136
2 2014110
3 201294
4 201250
5 201230
6 201430
7 201726
8 202017
9 201912
10 20204
11 20171
12 19931
13 20250
14 20180

About William P. Accomando

William P. Accomando is a scholar working on Molecular Biology, Genetics, Oncology, Cancer Research and Radiology, Nuclear Medicine and Imaging, having authored 14 papers that have together received 2.5k indexed citations. Recurring topics across this work include Epigenetics and DNA Methylation (5 papers), Cancer Immunotherapy and Biomarkers (3 papers), Glioma Diagnosis and Treatment (3 papers), Cancer Genomics and Diagnostics (2 papers), RNA modifications and cancer (2 papers), Environmental DNA in Biodiversity Studies (1 paper), Radiomics and Machine Learning in Medical Imaging (1 paper) and Genetic Syndromes and Imprinting (1 paper). The work is most often cited by research in Molecular Biology (1.7k citations), Pediatrics, Perinatology and Child Health (368 citations), Biological Psychiatry (47 citations), Genetics (455 citations) and Health, Toxicology and Mutagenesis (197 citations). William P. Accomando has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Heather H. Nelson, E. Andrés Houseman, John K. Wiencke, Karl T. Kelsey, Brock C. Christensen, Devin C. Koestler, Carmen J. Marsit, Scott M. Langevin, Margaret R. Karagas and Shichun Zheng. Their work appears in journals such as Clinical Cancer Research, Epigenetics, Molecular Therapy, npj Genomic Medicine and Drug Information Journal.

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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