Harry Subramanian

609 total citations
20 papers, 438 citations indexed

About

Harry Subramanian is a scholar working on Radiology, Nuclear Medicine and Imaging, Neurology and Molecular Biology. According to data from OpenAlex, Harry Subramanian has authored 20 papers receiving a total of 438 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Neurology and 3 papers in Molecular Biology. Recurrent topics in Harry Subramanian's work include Radiomics and Machine Learning in Medical Imaging (11 papers), Brain Tumor Detection and Classification (7 papers) and Artificial Intelligence in Healthcare and Education (3 papers). Harry Subramanian is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (11 papers), Brain Tumor Detection and Classification (7 papers) and Artificial Intelligence in Healthcare and Education (3 papers). Harry Subramanian collaborates with scholars based in United States, Germany and Japan. Harry Subramanian's co-authors include Ari Yasunaga, Jie Xu, Sara Cherry, Beth Gold, Nicolas Buchon, Jonathan Cohen, Beth Gordesky-Gold, Ian T. Lamborn, Leah R. Sabin and Kaycie C. Hopkins and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Neurology and Cancer Research.

In The Last Decade

Harry Subramanian

18 papers receiving 433 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Harry Subramanian United States 10 129 119 119 94 60 20 438
Hung N. Nguyen United States 10 69 0.5× 174 1.5× 359 3.0× 21 0.2× 17 0.3× 10 744
Alice Shia United Kingdom 8 76 0.6× 186 1.6× 143 1.2× 21 0.2× 13 0.2× 12 388
Miao Ling China 13 75 0.6× 154 1.3× 28 0.2× 15 0.2× 20 0.3× 25 422
Xindi Chen United States 7 85 0.7× 241 2.0× 112 0.9× 17 0.2× 17 0.3× 13 534
Ron Baik United States 9 36 0.3× 497 4.2× 21 0.2× 31 0.3× 12 0.2× 12 597
Souphatta Sasorith France 8 16 0.1× 287 2.4× 343 2.9× 12 0.1× 136 2.3× 11 744
Naif A. M. Almontashiri Saudi Arabia 14 14 0.1× 444 3.7× 83 0.7× 35 0.4× 26 0.4× 43 746
D. Greg Hall United States 11 25 0.2× 299 2.5× 32 0.3× 56 0.6× 21 0.3× 14 498
Hanoch Goldshmidt Israel 10 15 0.1× 146 1.2× 111 0.9× 88 0.9× 8 0.1× 19 407

Countries citing papers authored by Harry Subramanian

Since Specialization
Citations

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

Fields of papers citing papers by Harry Subramanian

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Harry Subramanian

This figure shows the co-authorship network connecting the top 25 collaborators of Harry Subramanian. A scholar is included among the top collaborators of Harry Subramanian 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 Harry Subramanian. Harry Subramanian 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.
2.
Subramanian, Harry, Tal Zeevi, Lawrence H. Staib, et al.. (2022). Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis. Frontiers in Oncology. 12. 856231–856231. 10 indexed citations
3.
Aboian, Mariam, Khaled Bousabarah, Tal Zeevi, et al.. (2022). Development of a workflow efficient PACS based automated brain tumor segmentation and radiomic feature extraction for clinical implementation (N2.003). Neurology. 98(18_supplement). 1 indexed citations
4.
Staib, Lawrence H., Harry Subramanian, Tal Zeevi, et al.. (2022). Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review. Cancers. 14(6). 1369–1369. 24 indexed citations
5.
6.
Subramanian, Harry, Alexandria Brackett, Amit Mahajan, et al.. (2021). Trends in Development of Novel Machine Learning Methods for the Identification of Gliomas in Datasets That Include Non-Glioma Images: A Systematic Review. Frontiers in Oncology. 11. 788819–788819. 8 indexed citations
7.
Subramanian, Harry, Tal Zeevi, Seyedmehdi Payabvash, et al.. (2021). OTHR-12. The development of machine learning algorithms for the differentiation of glioma and brain metastases – a systematic review. Neuro-Oncology Advances. 3(Supplement_3). iii17–iii17.
8.
Subramanian, Harry, Tal Zeevi, MingDe Lin, et al.. (2021). NIMG-23. MACHINE LEARNING METHODS IN GLIOMA GRADE PREDICTION: A SYSTEMATIC REVIEW. Neuro-Oncology. 23(Supplement_6). vi133–vi133. 2 indexed citations
9.
Subramanian, Harry, Tal Zeevi, MingDe Lin, et al.. (2021). NIMG-35. MACHINE LEARNING GLIOMA GRADE PREDICTION LITERATURE: A TRIPOD ANALYSIS OF REPORTING QUALITY. Neuro-Oncology. 23(Supplement_6). vi136–vi136.
10.
Subramanian, Harry, MingDe Lin, Khaled Bousabarah, et al.. (2021). NIMG-71. IDENTIFYING CLINICALLY APPLICABLE MACHINE LEARNING ALGORITHMS FOR GLIOMA SEGMENTATION USING A SYSTEMATIC LITERATURE REVIEW. Neuro-Oncology. 23(Supplement_6). vi145–vi145. 1 indexed citations
11.
Subramanian, Harry, Ichiro Ikuta, Richard A. Bronen, et al.. (2021). NIMG-46. SYSTEMATIC LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE ALGORITHMS USING PRE-THERAPY MR IMAGING FOR GLIOMA MOLECULAR SUBTYPE CLASSIFICATION. Neuro-Oncology. 23(Supplement_6). vi139–vi139. 1 indexed citations
12.
Subramanian, Harry, Tal Zeevi, Seyedmehdi Payabvash, et al.. (2021). OTHR-15. Assessment of TRIPOD adherence in articles developing machine learning models for differentiation of glioma from brain metastasis. Neuro-Oncology Advances. 3(Supplement_3). iii17–iii18. 1 indexed citations
13.
Subramanian, Harry, et al.. (2018). The Subjective Experience of Patients Undergoing Shunt Surgery for Idiopathic Normal Pressure Hydrocephalus. World Neurosurgery. 119. e46–e52. 6 indexed citations
14.
Subramanian, Harry, Henry S. Park, Andrea Barbieri, et al.. (2016). Pretreatment predictors of adjuvant chemoradiation in patients receiving transoral robotic surgery for squamous cell carcinoma of the oropharynx: a case control study. PubMed. 1(1). 7–7. 9 indexed citations
15.
Cohen, Jonathan, Ari Yasunaga, Jie Xu, et al.. (2015). Microbiota-Dependent Priming of Antiviral Intestinal Immunity in Drosophila. Cell Host & Microbe. 18(5). 571–581. 121 indexed citations
16.
Natsuizaka, Mitsuteru, Hideaki Kinugasa, Shingo Kagawa, et al.. (2014). IGFBP3 promotes esophageal cancer growth by suppressing oxidative stress in hypoxic tumor microenvironment.. PubMed. 4(1). 29–41. 67 indexed citations
17.
Xu, Jie, Kaycie C. Hopkins, Leah R. Sabin, et al.. (2013). ERK signaling couples nutrient status to antiviral defense in the insect gut. Proceedings of the National Academy of Sciences. 110(37). 15025–15030. 87 indexed citations
18.
Natsuizaka, Mitsuteru, Seiji Naganuma, Shingo Kagawa, et al.. (2012). Hypoxia induces IGFBP3 in esophageal squamous cancer cells through HIF‐1α‐mediated mRNA transcription and continuous protein synthesis. The FASEB Journal. 26(6). 2620–2630. 40 indexed citations
19.
Naganuma, Seiji, Kelly A. Whelan, Mitsuteru Natsuizaka, et al.. (2012). Notch receptor inhibition reveals the importance of cyclin D1 and Wnt signaling in invasive esophageal squamous cell carcinoma.. PubMed. 2(4). 459–75. 30 indexed citations
20.
Kagawa, Shingo, Mitsuteru Natsuizaka, Seiji Naganuma, et al.. (2012). Abstract 70: Loss of cellular senescence checkpoint functions reveals the oncogene characteristics of Notch1 in squamous cell carcinomas. Cancer Research. 72(8_Supplement). 70–70. 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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