Maria Francisca Ham
- Pathology and Forensic Medicine top 10%
- Lymphoma Diagnosis and Treatment 16
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- Viral-associated cancers and disorders 11
- Cancer Immunotherapy and Biomarkers 4
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- Immune Cell Function and Interaction 7
- T-cell and Retrovirus Studies 4
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- Thyroid Cancer Diagnosis and Treatment 9
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- Radiomics and Machine Learning in Medical Imaging 5
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- DNA Repair Mechanisms 4
- Co-authors
- Katsuyuki AozasaTetsuya TakakuwaNaoki WadaWenjuan LuoYoung Hyeh KoShigeki FujitaRuth NorthwayNur Rahadiani
- Partner nations
- IndonesiaJapanUnited Kingdom
In The Last Decade
Maria Francisca Ham
30 papers receiving 384 citations
Peers
Comparison fields: 5 of 68
- Pathology and Forensic Medicine 168
- Oncology 188
- Immunology 99
- Genetics 22
- Hematology 23
Countries citing papers authored by Maria Francisca Ham
This map shows the geographic impact of Maria Francisca Ham'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 Maria Francisca Ham with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maria Francisca Ham more than expected).
Fields of papers citing papers by Maria Francisca Ham
This network shows the impact of papers produced by Maria Francisca Ham. 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 Maria Francisca Ham. The network helps show where Maria Francisca Ham may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Maria Francisca Ham, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 0 | |
| 5 | 2025 | 1 | |
| 6 | 2024 | 1 | |
| 7 | 2024 | 0 | |
| 8 | 2024 | 0 | |
| 9 | 2023 | 2 | |
| 10 | 2023 | 1 | |
| 11 | 2023 | 1 | |
| 12 | 2023 | 1 | |
| 13 | MUM1 Expression versus Hans Algorithm to Predict Prognosis in Indonesian Diffuse Large B-Cell Lymphoma Patients Receiving R-CHOP | 2022 | 4 |
| 14 | 2017 | 51 | |
| 15 | 2007 | 6 | |
| 16 | 2007 | 28 | |
| 17 | 2006 | 24 | |
| 18 | 2004 | 28 | |
| 19 | 2004 | 10 | |
| 20 | 2003 | 37 |
About Maria Francisca Ham
Maria Francisca Ham is a scholar working on Pathology and Forensic Medicine, Oncology and Endocrinology, Diabetes and Metabolism, having authored 37 papers that have together received 388 indexed citations. Recurring topics across this work include Lymphoma Diagnosis and Treatment (16 papers), Viral-associated cancers and disorders (11 papers), Thyroid Cancer Diagnosis and Treatment (9 papers), Immune Cell Function and Interaction (7 papers), Radiomics and Machine Learning in Medical Imaging (5 papers), T-cell and Retrovirus Studies (4 papers), DNA Repair Mechanisms (4 papers) and Cancer Immunotherapy and Biomarkers (4 papers). The work is most often cited by research in Pathology and Forensic Medicine (168 citations), Oncology (188 citations) and Immunology (99 citations). Maria Francisca Ham has collaborated with scholars based in Indonesia, Japan and United Kingdom. Frequent co-authors include Katsuyuki Aozasa, Tetsuya Takakuwa, Naoki Wada, Wenjuan Luo, Young Hyeh Ko, Shigeki Fujita, Ruth Northway, Nur Rahadiani, Ian Mansell and Nicola Jones. Their work appears in journals such as Cancer Science, Laboratory Investigation, Virus Research, Oncology Reports and Leukemia.
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.