William P. Accomando

4.2k total citations · 1 hit paper
14 papers, 2.4k citations indexed

About

William P. Accomando is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, William P. Accomando has authored 14 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 5 papers in Immunology and 4 papers in Oncology. Recurrent topics in William P. Accomando's work include Epigenetics and DNA Methylation (7 papers), Cancer Immunotherapy and Biomarkers (4 papers) and Glioma Diagnosis and Treatment (3 papers). William P. Accomando is often cited by papers focused on Epigenetics and DNA Methylation (7 papers), Cancer Immunotherapy and Biomarkers (4 papers) and Glioma Diagnosis and Treatment (3 papers). William P. Accomando collaborates with scholars based in United States, United Kingdom and Australia. William P. Accomando's co-authors include Karl T. Kelsey, John K. Wiencke, Heather H. Nelson, E. Andrés Houseman, Brock C. Christensen, Devin C. Koestler, Carmen J. Marsit, Margaret R. Karagas, Scott M. Langevin and Shichun Zheng and has published in prestigious journals such as Journal of Clinical Oncology, Clinical Cancer Research and Genome biology.

In The Last Decade

William P. Accomando

12 papers receiving 2.4k 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.

DNA methylation arrays as surrogate measures of cell mixture distribution

2012 2.0k citations E. Andrés Houseman, William P. Accomando et al. BMC Bioinformatics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
William P. Accomando United States	9	2.0k	580	567	271	222	14	2.4k
Brian H. Chen United States	20	1.9k 1.0×	531 0.9×	710 1.3×	368 1.4×	122 0.5×	39	3.5k
Peter Murakami United States	16	1.9k 1.0×	546 0.9×	262 0.5×	65 0.2×	219 1.0×	24	2.8k
C.J.H. Kelnar United Kingdom	31	812 0.4×	529 0.9×	591 1.0×	112 0.4×	144 0.6×	85	2.8k
Lucia L.C. Lam Canada	18	1.3k 0.7×	476 0.8×	371 0.7×	64 0.2×	75 0.3×	35	2.3k
Rosie M. Walker United Kingdom	21	878 0.4×	286 0.5×	259 0.5×	124 0.5×	57 0.3×	59	1.4k
Alexandra M. Binder United States	20	802 0.4×	239 0.4×	535 0.9×	356 1.3×	38 0.2×	49	1.5k
Phillip E. Melton Australia	21	768 0.4×	377 0.7×	283 0.5×	82 0.3×	114 0.5×	82	1.7k
Ee Ming Wong Australia	22	1.1k 0.5×	412 0.7×	267 0.5×	175 0.6×	24 0.1×	56	1.4k
Rudolf P. Talens Netherlands	5	1.0k 0.5×	379 0.7×	686 1.2×	108 0.4×	50 0.2×	5	1.5k
Josine L. Min United Kingdom	16	722 0.4×	357 0.6×	172 0.3×	83 0.3×	78 0.4×	26	1.3k

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-authorship network of co-authors of William P. Accomando

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

All Works

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14 of 14 papers shown

Merchan, Jaime R., Jordi Rodon, Shree Venkat, et al.. (2025). A phase 1B trial of vocimagene amiretrorepvec in patients with advanced solid tumors: Safety, tumor homing, and immune modulatory effects. Molecular Therapy. 33(11). 5505–5515.

Accomando, William P., Daniel J. Hogan, Aaron M. Newman, et al.. (2020). Molecular and Immunologic Signatures are Related to Clinical Benefit from Treatment with Vocimagene Amiretrorepvec (Toca 511) and 5-Fluorocytosine (Toca FC) in Patients with Glioma. Clinical Cancer Research. 26(23). 6176–6186. 17 indexed citations

Falchook, Gerald S., Jordi Rodón, Shree Venkat, et al.. (2020). Immune modulation after Toca 511 and Toca FC treatment of colorectal cancer patients.. Journal of Clinical Oncology. 38(4_suppl). 186–186. 4 indexed citations

Mitchell, Leah, Kader Yagiz, Andrew Hofacre, et al.. (2019). PD-L1 checkpoint blockade delivered by retroviral replicating vector confers anti-tumor efficacy in murine tumor models. Oncotarget. 10(23). 2252–2269. 12 indexed citations

Accomando, William P., Timothy F. Cloughesy, Steven N. Kalkanis, et al.. (2018). ATIM-26. IMMUNOLOGIC TRENDS ASSOCIATED WITH PATIENT OUTCOMES IN A PHASE 1 CLINICAL TRIAL OF TOCA 511 AND TOCA FC IN RECURRENT HIGH GRADE GLIOMA. Neuro-Oncology. 20(suppl_6). vi6–vi7.

Carmona, Juan J., William P. Accomando, Alexandra M. Binder, et al.. (2017). Empirical comparison of reduced representation bisulfite sequencing and Infinium BeadChip reproducibility and coverage of DNA methylation in humans. npj Genomic Medicine. 2(1). 13–13. 24 indexed citations

Accomando, William P. & Karin B. Michels. (2017). Multiplexed Reduced Representation Bisulfite Sequencing with Magnetic Bead Fragment Size Selection. Methods in molecular biology. 1708. 137–159. 1 indexed citations

Langevin, Scott M., E. Andrés Houseman, William P. Accomando, et al.. (2014). Leukocyte-adjusted epigenome-wide association studies of blood from solid tumor patients. Epigenetics. 9(6). 884–895. 27 indexed citations

Accomando, William P., John K. Wiencke, E. Andrés Houseman, Heather H. Nelson, & Karl T. Kelsey. (2014). Quantitative reconstruction of leukocyte subsets using DNA methylation. Genome biology. 15(3). R50–R50. 105 indexed citations

10.

Accomando, William P., John K. Wiencke, E. Andrés Houseman, et al.. (2012). Decreased NK Cells in Patients with Head and Neck Cancer Determined in Archival DNA. Clinical Cancer Research. 18(22). 6147–6154. 46 indexed citations

11.

Koestler, Devin C., Carmen J. Marsit, Brock C. Christensen, et al.. (2012). Peripheral Blood Immune Cell Methylation Profiles Are Associated with Nonhematopoietic Cancers. Cancer Epidemiology Biomarkers & Prevention. 21(8). 1293–1302. 88 indexed citations

12.

Houseman, E. Andrés, William P. Accomando, Devin C. Koestler, et al.. (2012). DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 13(1). 86–86. 2049 indexed citations breakdown →

13.

Wiencke, John K., William P. Accomando, Shichun Zheng, et al.. (2012). Epigenetic biomarkers of T-cells in human glioma. Epigenetics. 7(12). 1391–1402. 29 indexed citations

14.

Accomando, William P.. (1993). CANDA Overview: What Should a CANDA Look Like?. Drug Information Journal. 27(2). 407–411. 1 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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