Gavin Ha
Impact in
- Cancer Research top 0.02%
- Cancer Genomics and Diagnostics
- Cancer-related molecular mechanisms research
- Oncology top 0.05%
- Cancer Immunotherapy and Biomarkers
- Cancer Cells and Metastasis
Papers in
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- Cancer Genomics and Diagnostics 80
-
- RNA Research and Splicing 13
- RNA modifications and cancer 12
- Single-cell and spatial transcriptomics 10
- Genomics and Chromatin Dynamics 9
- Co-authors
- Sohrab P. Shah (12 shared papers)Samuel A. J. R. Aparicio (8 shared papers)Andrew J. L. Roth (3 shared papers)David George Huntsman (9 shared papers)Alexandre Bouchard‐Côté (2 shared papers)Jaswinder Khattra (1 shared paper)Damian Yap (1 shared paper)Justina Biele (1 shared paper)
- Journals
- Nature Communications (18 papers)Cell Reports (15 papers)Cell (11 papers)Nature (11 papers)Nature Genetics (9 papers)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
Gavin Ha
135 papers receiving 50.0k citations
Gavin Ha's Hit Papers
Peers
Comparison fields: 5 of 186
- Cancer Research 18.4k
- Oncology 13.1k
- Molecular Biology 24.5k
- Pulmonary and Respiratory Medicine 8.6k
- Pathology and Forensic Medicine 4.2k
Countries citing papers authored by Gavin Ha
This map shows the geographic impact of Gavin Ha'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 Gavin Ha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gavin Ha more than expected).
Fields of papers citing papers by Gavin Ha
This network shows the impact of papers produced by Gavin Ha. 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 Gavin Ha. The network helps show where Gavin Ha may publish in the future.
Co-authors
The 25 scholars most cited alongside Gavin Ha, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 139 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups Hit paper breakdown → | 2012 | 4636 |
| 2 | The Immune Landscape of Cancer Hit paper breakdown → | 2018 | 4331 |
| 3 | An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics Hit paper breakdown → | 2018 | 2602 |
| 4 | The repertoire of mutational signatures in human cancer Hit paper breakdown → | 2020 | 2339 |
| 5 | Oncogenic Signaling Pathways in The Cancer Genome Atlas Hit paper breakdown → | 2018 | 2334 |
| 6 | Pan-cancer analysis of whole genomes Hit paper breakdown → | 2020 | 2109 |
| 7 | Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer Hit paper breakdown → | 2018 | 1720 |
| 8 | Comprehensive Characterization of Cancer Driver Genes and Mutations Hit paper breakdown → | 2018 | 1714 |
| 9 | The clonal and mutational evolution spectrum of primary triple-negative breast cancers Hit paper breakdown → | 2012 | 1644 |
| 10 | Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation Hit paper breakdown → | 2018 | 1617 |
| 11 | ARID1A Mutations in Endometriosis-Associated Ovarian Carcinomas Hit paper breakdown → | 2010 | 1395 |
| 12 | Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas Hit paper breakdown → | 2018 | 810 |
| 13 | Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images Hit paper breakdown → | 2018 | 761 |
| 14 | PyClone: statistical inference of clonal population structure in cancer Hit paper breakdown → | 2014 | 756 |
| 15 | Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors Hit paper breakdown → | 2017 | 735 |
| 16 | The evolutionary history of 2,658 cancers Hit paper breakdown → | 2020 | 705 |
| 17 | Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients Hit paper breakdown → | 2018 | 685 |
| 18 | Genetic and transcriptional evolution alters cancer cell line drug response Hit paper breakdown → | 2018 | 653 |
| 19 | Pathogenic Germline Variants in 10,389 Adult Cancers Hit paper breakdown → | 2018 | 606 |
| 20 | Patient-derived xenografts undergo mouse-specific tumor evolution Hit paper breakdown → | 2017 | 586 |
About Gavin Ha
Gavin Ha is a scholar working on Cancer Research, Molecular Biology, Oncology, Pathology and Forensic Medicine and Pulmonary and Respiratory Medicine, having authored 139 papers that have together received 50.6k indexed citations. Recurring topics across this work include Cancer Genomics and Diagnostics (80 papers), Genetic factors in colorectal cancer (19 papers), RNA Research and Splicing (13 papers), Prostate Cancer Treatment and Research (13 papers), RNA modifications and cancer (12 papers), Single-cell and spatial transcriptomics (10 papers), Genomics and Chromatin Dynamics (9 papers) and Lung Cancer Treatments and Mutations (9 papers). The work is most often cited by research in Cancer Research (18.4k citations), Oncology (13.1k citations), Molecular Biology (24.5k citations), Pulmonary and Respiratory Medicine (8.6k citations) and Pathology and Forensic Medicine (4.2k citations). Gavin Ha has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Sohrab P. Shah, Samuel A. J. R. Aparicio, Andrew J. L. Roth, David George Huntsman, Alexandre Bouchard‐Côté, Jaswinder Khattra, Damian Yap, Justina Biele, Emma Laks and Adrian N. C. Wan. Their work appears in journals such as Nature Communications, Cell Reports, Cell, Nature and Nature Genetics.
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.