Kyle Singleton

837 total citations
18 papers, 402 citations indexed

About

Kyle Singleton is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Epidemiology. According to data from OpenAlex, Kyle Singleton has authored 18 papers receiving a total of 402 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Genetics and 3 papers in Epidemiology. Recurrent topics in Kyle Singleton's work include Radiomics and Machine Learning in Medical Imaging (9 papers), Glioma Diagnosis and Treatment (7 papers) and MRI in cancer diagnosis (6 papers). Kyle Singleton is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (9 papers), Glioma Diagnosis and Treatment (7 papers) and MRI in cancer diagnosis (6 papers). Kyle Singleton collaborates with scholars based in United States, Germany and Mexico. Kyle Singleton's co-authors include Kristin R. Swanson, Paula Whitmire, Sara Taylor, Jill S. Barnholtz‐Sloan, Ningying Wu, Michael E. Berens, Albert H. Kim, Nicole M. Warrington, Justin D. Lathia and Joshua B. Rubin and has published in prestigious journals such as PLoS ONE, Scientific Reports and Science Translational Medicine.

In The Last Decade

Kyle Singleton

18 papers receiving 396 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kyle Singleton United States 8 123 86 74 73 59 18 402
Maral Adel Fahmideh United States 9 164 1.3× 132 1.5× 67 0.9× 34 0.5× 54 0.9× 14 482
Sebastian Winter United States 12 152 1.2× 89 1.0× 44 0.6× 62 0.8× 23 0.4× 42 515
Heather Leeper United States 11 255 2.1× 80 0.9× 85 1.1× 69 0.9× 62 1.1× 22 444
Dominic Amara United States 11 63 0.5× 84 1.0× 17 0.2× 66 0.9× 74 1.3× 32 574
Andrew Lin United States 15 292 2.4× 84 1.0× 164 2.2× 49 0.7× 23 0.4× 38 541
Patricia Goldhoff United States 11 98 0.8× 193 2.2× 208 2.8× 55 0.8× 45 0.8× 13 629
Michael Balas Canada 10 57 0.5× 50 0.6× 37 0.5× 123 1.7× 32 0.5× 59 452
Faïza Bessaoud France 14 240 2.0× 56 0.7× 173 2.3× 54 0.7× 107 1.8× 25 627
Paula Whitmire United States 7 134 1.1× 91 1.1× 25 0.3× 42 0.6× 36 0.6× 13 310
Randy Van Ommeren Canada 7 55 0.4× 80 0.9× 24 0.3× 105 1.4× 20 0.3× 10 364

Countries citing papers authored by Kyle Singleton

Since Specialization
Citations

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

Fields of papers citing papers by Kyle Singleton

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kyle Singleton

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

All Works

18 of 18 papers shown
1.
Argenziano, Michael, Hyunsoo Yoon, Deborah Boyett, et al.. (2024). Biologically informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post treatment glioblastoma. npj Digital Medicine. 7(1). 292–292. 2 indexed citations
2.
Wang, Lujia, Leland Hu, Gustavo De León, et al.. (2023). Weakly-Supervised Transfer Learning With Application in Precision Medicine. IEEE Transactions on Automation Science and Engineering. 21(4). 6250–6264. 6 indexed citations
3.
Ranjbar, Sara, et al.. (2022). Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients. PubMed. 1. 832512–832512. 2 indexed citations
4.
Jackson, Pamela, Sara Ranjbar, Kamila M. Bond, et al.. (2022). NIMG-19. IMAGE-BASED MODELING MAP OF EDEMA IS CORRELATED WITH MULTIPLE BLOOD-BRAIN-BARRIER PERMEABILITY RELEVANT TRANSCRIPTOMIC MARKERS IN BRAIN TUMOR PATIENTS. Neuro-Oncology. 24(Supplement_7). vii165–vii165. 1 indexed citations
5.
Hawkins‐Daarud, Andrea, Gustavo De León, Kyle Singleton, et al.. (2021). NIMG-59. RADIOMICS-PREDICTED T CELL DYNAMICS STRATIFY SURVIVAL AFTER DENDRITIC CELL VACCINE THERAPY FOR PRIMARY GLIOBLASTOMA. Neuro-Oncology. 23(Supplement_6). vi142–vi143. 1 indexed citations
6.
Singleton, Kyle, Alyx B. Porter, Leland Hu, et al.. (2020). Days gained response discriminates treatment response in patients with recurrent glioblastoma receiving bevacizumab-based therapies. Neuro-Oncology Advances. 2(1). vdaa085–vdaa085. 1 indexed citations
7.
Whitmire, Paula, Sandra K. Johnston, Kyle Singleton, et al.. (2020). Image-based metric of invasiveness predicts response to adjuvant temozolomide for primary glioblastoma. PLoS ONE. 15(3). e0230492–e0230492. 8 indexed citations
8.
Singleton, Kyle, Andrea Hawkins‐Daarud, Sandra K. Johnston, et al.. (2020). NCOG-69. SEX DIFFERENCES IN GLIOBLASTOMA PATIENT SURVIVAL AS A FUNCTION OF EXTENT OF SURGICAL RESECTION AND CYCLES OF ADJUVANT TEMOZOLOMIDE DURING STANDARD-OF-CARE REGIMENS. Neuro-Oncology. 22(Supplement_2). ii144–ii145. 1 indexed citations
9.
Yang, Wei, Nicole M. Warrington, Sara Taylor, et al.. (2019). Sex differences in GBM revealed by analysis of patient imaging, transcriptome, and survival data. Science Translational Medicine. 11(473). 214 indexed citations
10.
Ranjbar, Sara, Kyle Singleton, Pamela Jackson, et al.. (2019). A Deep Convolutional Neural Network for Annotation of Magnetic Resonance Imaging Sequence Type. Journal of Digital Imaging. 33(2). 439–446. 20 indexed citations
11.
Gaw, Nathan, Andrea Hawkins‐Daarud, Leland Hu, et al.. (2019). Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI. Scientific Reports. 9(1). 10063–10063. 62 indexed citations
12.
Jackson, Pamela, Andrea Hawkins‐Daarud, Kyle Singleton, et al.. (2019). SCIDOT-16. T2-WEIGHTED IMAGING MAY BE INDICATIVE OF DRUG DISTRIBUTION IN GLIOBLASTOMA PATIENTS. Neuro-Oncology. 21(Supplement_6). vi274–vi275. 1 indexed citations
13.
Gelberg, Lillian, Ronald Andersen, Guillermina Natera Rey, et al.. (2017). A pilot replication of QUIT, a randomized controlled trial of a brief intervention for reducing risky drug use, among Latino primary care patients. Drug and Alcohol Dependence. 179. 433–440. 9 indexed citations
14.
Swanson, Kristin R., Nathan Gaw, Andrea Hawkins‐Daarud, et al.. (2017). NIMG-74. RADIOMICS OF TUMOR INVASION 2.0: COMBINING MECHANISTIC TUMOR INVASION MODELS WITH MACHINE LEARNING MODELS TO ACCURATELY PREDICT TUMOR INVASION IN HUMAN GLIOBLASTOMA PATIENTS. Neuro-Oncology. 19(suppl_6). vi159–vi159. 1 indexed citations
15.
Gelberg, Lillian, Ronald Andersen, Abdelmonem A. Afifi, et al.. (2015). Project QUIT (Quit Using Drugs Intervention Trial): a randomized controlled trial of a primary care‐based multi‐component brief intervention to reduce risky drug use. Addiction. 110(11). 1777–1790. 51 indexed citations
16.
Singleton, Kyle, William Speier, Alex Bui, & William Hsu. (2014). Motivating the additional use of external validity: examining transportability in a model of glioblastoma multiforme.. PubMed. 2014. 1930–9. 2 indexed citations
17.
Singleton, Kyle, William Hsu, & Alex Bui. (2012). Comparing predictive models of glioblastoma multiforme built using multi-institutional and local data sources.. PubMed. 2012. 1385–92. 8 indexed citations
18.
Singleton, Kyle, et al.. (2011). Wireless data collection of self-administered surveys using tablet computers.. PubMed. 2011. 1261–9. 12 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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