David Shih

11.9k total citations
35 papers, 1.1k citations indexed

About

David Shih is a scholar working on Molecular Biology, Genetics and Oncology. According to data from OpenAlex, David Shih has authored 35 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 11 papers in Genetics and 9 papers in Oncology. Recurrent topics in David Shih's work include Glioma Diagnosis and Treatment (10 papers), Hedgehog Signaling Pathway Studies (6 papers) and Cancer Genomics and Diagnostics (5 papers). David Shih is often cited by papers focused on Glioma Diagnosis and Treatment (10 papers), Hedgehog Signaling Pathway Studies (6 papers) and Cancer Genomics and Diagnostics (5 papers). David Shih collaborates with scholars based in United States, Canada and Hong Kong. David Shih's co-authors include Michael D. Taylor, Marc Remke, Éric Bouffet, Paul A. Northcott, James T. Rutka, Stefan M. Pfister, Adrian M. Dubuc, Andrey Korshunov, Charles G. Eberhart and Jing Zhang and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

David Shih

31 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Shih United States 16 668 533 230 155 129 35 1.1k
Pankaj Pathak India 22 545 0.8× 584 1.1× 359 1.6× 106 0.7× 172 1.3× 54 1.1k
Prerana Jha India 20 546 0.8× 502 0.9× 417 1.8× 97 0.6× 154 1.2× 42 969
Martyna Adamowicz‐Brice United Kingdom 6 485 0.7× 446 0.8× 188 0.8× 137 0.9× 209 1.6× 7 826
Carl Koschmann United States 18 428 0.6× 530 1.0× 249 1.1× 181 1.2× 188 1.5× 79 935
Marina Ryzhova Russia 13 542 0.8× 761 1.4× 227 1.0× 95 0.6× 248 1.9× 82 1.0k
Maxwell W. Tom United States 10 493 0.7× 407 0.8× 177 0.8× 131 0.8× 132 1.0× 10 975
Tejus Bale United States 17 383 0.6× 478 0.9× 255 1.1× 243 1.6× 167 1.3× 59 1.1k
Jan Sadones Belgium 12 437 0.7× 428 0.8× 348 1.5× 151 1.0× 80 0.6× 17 822
Hilary Blair United States 12 429 0.6× 703 1.3× 319 1.4× 169 1.1× 152 1.2× 17 1.2k
Sergey Popov United Kingdom 20 825 1.2× 358 0.7× 349 1.5× 316 2.0× 169 1.3× 48 1.5k

Countries citing papers authored by David Shih

Since Specialization
Citations

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

Fields of papers citing papers by David Shih

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Shih

This figure shows the co-authorship network connecting the top 25 collaborators of David Shih. A scholar is included among the top collaborators of David Shih 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 David Shih. David Shih 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.
Liu, Yantao, Yu Peng, Zongran Liu, et al.. (2025). Tumor-associated macrophage-derived exosomes modulate the immunotherapeutic sensitivity of SHH-medulloblastoma by targeting m6A-modified FOXD1. Neuro-Oncology. 27(11). 3000–3015. 1 indexed citations
2.
Wong, Jason W.H., David Shih, Isaac Ho, et al.. (2025). The diagnostic accuracy of next-generation sequencing in advanced NSCLC. PubMed. 9. 100325–100325.
3.
Ye, Xiaohua, David Shih, Zhiqiang Ku, et al.. (2024). Transcriptional signature of durable effector T cells elicited by a replication defective HCMV vaccine. npj Vaccines. 9(1). 70–70. 4 indexed citations
4.
Lan, Lan, Yujia Zhou, Kenneth D. Chavin, et al.. (2024). Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records. Journal of Biomedical Informatics. 152. 104626–104626. 4 indexed citations
5.
Helali, Aya El, David Shih, Charlene H. L. Wong, et al.. (2023). The impact of the multi-disciplinary molecular tumour board and integrative next generation sequencing on clinical outcomes in advanced solid tumours. The Lancet Regional Health - Western Pacific. 36. 100775–100775. 6 indexed citations
6.
Kobayashi, Michihiro, Haichao Wei, Haizi Cheng, et al.. (2023). HSC-independent definitive hematopoiesis persists into adult life. Cell Reports. 42(3). 112239–112239. 24 indexed citations
7.
Chakravarty, Anupam K., Daniel J. McGrail, David Shih, et al.. (2022). Biomolecular Condensation: A New Phase in Cancer Research. Cancer Discovery. 12(9). 2031–2043. 13 indexed citations
8.
Hu, Fang, Johanna Bertl, Xiaoqiang Zhu, et al.. (2022). Tumour mutational burden is overestimated by target cancer gene panels. SHILAP Revista de lepidopterología. 3(1). 56–64. 5 indexed citations
9.
10.
Zhang, Jing, David Shih, & Shiaw-Yih Lin. (2020). Role of DNA repair defects in predicting immunotherapy response. Biomarker Research. 8(1). 23–23. 50 indexed citations
11.
Kawauchi, Daisuke, Robert J. Ogg, David Shih, et al.. (2017). Novel MYC-driven medulloblastoma models from multiple embryonic cerebellar cells. Oncogene. 36(37). 5231–5242. 38 indexed citations
12.
Chow, Kin-Hoe, Dong‐Mi Shin, Molly H. Jenkins, et al.. (2014). Epigenetic States of Cells of Origin and Tumor Evolution Drive Tumor-Initiating Cell Phenotype and Tumor Heterogeneity. Cancer Research. 74(17). 4864–4874. 19 indexed citations
13.
Ramaswamy, Vijay, Marc Remke, David Shih, et al.. (2014). Duration of the pre‐diagnostic interval in medulloblastoma is subgroup dependent. Pediatric Blood & Cancer. 61(7). 1190–1194. 33 indexed citations
14.
Perreault, Sébastien, Vijay Ramaswamy, Achal S. Achrol, et al.. (2014). MRI Surrogates for Molecular Subgroups of Medulloblastoma. American Journal of Neuroradiology. 35(7). 1263–1269. 190 indexed citations
15.
Markant, Shirley L., Lourdes Adriana Esparza, Kelly L. Barton, et al.. (2013). Targeting Sonic Hedgehog-Associated Medulloblastoma through Inhibition of Aurora and Polo-like Kinases. Cancer Research. 73(20). 6310–6322. 46 indexed citations
16.
Natarajan, Sivaraman, Yaochen Li, Emily E. Miller, et al.. (2013). Notch1 -Induced Brain Tumor Models the Sonic Hedgehog Subgroup of Human Medulloblastoma. Cancer Research. 73(17). 5381–5390. 25 indexed citations
17.
Dey, Joyoti, Adrian M. Dubuc, Kyle D. Pedro, et al.. (2013). MyoD Is a Tumor Suppressor Gene in Medulloblastoma. Cancer Research. 73(22). 6828–6837. 23 indexed citations
18.
Diaz, Roberto J., Rocco Romagnuolo, Christian A. Smith, et al.. (2012). High-resolution Whole-Genome Analysis of Skull Base Chordomas Implicates FHIT Loss in Chordoma Pathogenesis. Neoplasia. 14(9). 788–IN4. 31 indexed citations
19.
Northcott, Paul A., David Shih, Marc Remke, et al.. (2011). Rapid, reliable, and reproducible molecular sub-grouping of clinical medulloblastoma samples. Acta Neuropathologica. 123(4). 615–626. 257 indexed citations
20.
Northcott, Paul A., Thomas Hielscher, Adrian M. Dubuc, et al.. (2011). Pediatric and adult sonic hedgehog medulloblastomas are clinically and molecularly distinct. Acta Neuropathologica. 122(2). 231–240. 155 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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