Cody Ashby

3.6k total citations
51 papers, 561 citations indexed

About

Cody Ashby is a scholar working on Hematology, Molecular Biology and Cancer Research. According to data from OpenAlex, Cody Ashby has authored 51 papers receiving a total of 561 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Hematology, 34 papers in Molecular Biology and 11 papers in Cancer Research. Recurrent topics in Cody Ashby's work include Multiple Myeloma Research and Treatments (37 papers), Protein Degradation and Inhibitors (13 papers) and Cancer Genomics and Diagnostics (9 papers). Cody Ashby is often cited by papers focused on Multiple Myeloma Research and Treatments (37 papers), Protein Degradation and Inhibitors (13 papers) and Cancer Genomics and Diagnostics (9 papers). Cody Ashby collaborates with scholars based in United States, United Kingdom and Germany. Cody Ashby's co-authors include Xiuzhen Huang, Guojun Li, Carole L. Cramer, Zheng Chang, Juntao Liu, Deli Liu, Yu Zhang, Gareth J. Morgan, Brian A. Walker and Michael Bauer and has published in prestigious journals such as Journal of Clinical Investigation, Nature Communications and Blood.

In The Last Decade

Cody Ashby

47 papers receiving 556 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Cody Ashby United States 13 400 193 92 78 74 51 561
Fuhong He China 14 571 1.4× 128 0.7× 40 0.4× 88 1.1× 82 1.1× 24 734
Grace R. Jeschke United States 13 488 1.2× 343 1.8× 125 1.4× 38 0.5× 57 0.8× 28 841
Julia M. Rogers United States 13 659 1.6× 101 0.5× 104 1.1× 157 2.0× 53 0.7× 22 955
Zuojian Tang United States 15 512 1.3× 80 0.4× 72 0.8× 111 1.4× 88 1.2× 25 779
Ashley Kuenzi Davis United States 15 262 0.7× 108 0.6× 79 0.9× 61 0.8× 53 0.7× 26 529
Jizhou Yan China 15 703 1.8× 145 0.8× 85 0.9× 140 1.8× 115 1.6× 31 968
Winfried Hofmann Germany 12 202 0.5× 137 0.7× 54 0.6× 74 0.9× 41 0.6× 51 447
Tina Hambuch United States 10 396 1.0× 260 1.3× 151 1.6× 243 3.1× 93 1.3× 15 862
Annalisa Mancini Germany 14 426 1.1× 55 0.3× 92 1.0× 62 0.8× 48 0.6× 17 660
Andrew Martens United States 12 762 1.9× 149 0.8× 57 0.6× 126 1.6× 46 0.6× 15 872

Countries citing papers authored by Cody Ashby

Since Specialization
Citations

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

Fields of papers citing papers by Cody Ashby

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Cody Ashby

This figure shows the co-authorship network connecting the top 25 collaborators of Cody Ashby. A scholar is included among the top collaborators of Cody Ashby 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 Cody Ashby. Cody Ashby 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.
Cheng, Yan, Fumou Sun, Daisy Alapat, et al.. (2024). Multi-omics reveal immune microenvironment alterations in multiple myeloma and its precursor stages. Blood Cancer Journal. 14(1). 194–194. 13 indexed citations
2.
Cheng, Yan, Fumou Sun, Daisy Alapat, et al.. (2024). Multi-Omics Reveal Immune Microenvironment Alterations in Multiple Myeloma and Its Precursor Stages. Blood. 144(Supplement 1). 4729–4729.
3.
Ashby, Cody, Manish Adhikari, Japneet Kaur, et al.. (2024). A NOTCH3-CXCL12-driven myeloma-tumor niche signaling axis promotes chemoresistance in multiple myeloma. Haematologica. 109(8). 2606–2618. 5 indexed citations
4.
Kaur, Japneet, Sharmin Khan, Cody Ashby, et al.. (2024). A novel CCL3-HMGB1 signaling axis regulating osteocyte RANKL expression in multiple myeloma. Haematologica. 110(4). 952–966. 2 indexed citations
5.
Marino, Silvia, Daniela N. Petrusca, Ryan T. Bishop, et al.. (2023). Pharmacologic targeting of the p62 ZZ domain enhances both anti-tumor and bone-anabolic effects of bortezomib in multiple myeloma. Haematologica. 109(5). 1501–1513. 2 indexed citations
6.
7.
Siegel, Eric R., Fumou Sun, Cody Ashby, et al.. (2023). Prognostic Value of Ferritin in ASCT MM Patients: Integration with GEP Models and ISS Series Systems. Blood. 142(Supplement 1). 6573–6573. 1 indexed citations
8.
Wanchai, Visanu, Cody Ashby, Hongwei Xu, et al.. (2023). Global Proteomics Identifies CDK11A/B As Integral to Myeloma Proliferation and High-Risk Disease. Blood. 142(Supplement 1). 1932–1932. 1 indexed citations
9.
Ashby, Cody, Eileen M. Boyle, Michael Bauer, et al.. (2022). Structural variants shape the genomic landscape and clinical outcome of multiple myeloma. Blood Cancer Journal. 12(5). 85–85. 10 indexed citations
10.
Ashby, Cody, John D. Shaughnessy, Fenghuang Zhan, et al.. (2022). AKT supports the metabolic fitness of multiple myeloma cells by restricting FOXO activity. Blood Advances. 7(9). 1697–1712. 13 indexed citations
11.
Maura, Francesco, Eileen M. Boyle, Even H Rustad, et al.. (2021). Chromothripsis as a pathogenic driver of multiple myeloma. Seminars in Cell and Developmental Biology. 123. 115–123. 25 indexed citations
12.
Wardell, Christopher P., Cody Ashby, & Michael Bauer. (2021). FiNGS: high quality somatic mutations using filters for next generation sequencing. BMC Bioinformatics. 22(1). 77–77. 6 indexed citations
13.
Jones, John R, Yann‐Vaï Le Bihan, Niels Weinhold, et al.. (2021). Mutations in CRBN and other cereblon pathway genes are infrequently associated with acquired resistance to immunomodulatory drugs. Leukemia. 35(10). 3017–3020. 12 indexed citations
14.
Deshpande, Shayu, Ruslana G. Tytarenko, Yan Wang, et al.. (2020). Monitoring treatment response and disease progression in myeloma with circulating cell‐free DNA. European Journal Of Haematology. 106(2). 230–240. 22 indexed citations
15.
Bauer, Michael, Cody Ashby, Christopher P. Wardell, et al.. (2020). Differential RNA splicing as a potentially important driver mechanism in multiple myeloma. Haematologica. 106(3). 736–745. 22 indexed citations
16.
Ashby, Cody, Eileen M. Boyle, Ruslana G. Tytarenko, et al.. (2018). Long-Term Follow-up Identifies Double Hit and Key Mutations As Impacting Progression Free and Overall Survival in Multiple Myeloma. Blood. 132(Supplement 1). 110–110. 1 indexed citations
17.
Danziger, Samuel A., Andrew Dervan, Frank Schmitz, et al.. (2017). Deconvolution of the Immune Microenvironment Can Predict the Outcome of Myeloma Patients and Inform Potential Intervention Strategies. Blood. 130. 1751–1751. 1 indexed citations
18.
19.
Chang, Zheng, Guojun Li, Juntao Liu, et al.. (2015). Bridger: a new framework for de novo transcriptome assembly using RNA-seq data. Genome Biology. 16(1). 30–30. 202 indexed citations
20.
Ma, Shiqian, Cody Ashby, Donghai Xiong, et al.. (2015). SPARCoC: A New Framework for Molecular Pattern Discovery and Cancer Gene Identification. PLoS ONE. 10(3). e0117135–e0117135. 6 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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