Masaya Okada
- Hematology top 5%
- Genetics top 5%
- Immunology
- Oncology
- Surgery
- Co-authors
- Masahiro TadaHiroyuki TakatsukaEizo KakishitaYoshihiro FujimoriY TakemotoKoichi MiyamuraShuichi TaniguchiTakahiro Okamoto
- Topics
- Hematopoietic Stem Cell Transplantation (19 papers)Chronic Myeloid Leukemia Treatments (7 papers)Lymphoma Diagnosis and Treatment (7 papers)
- Cited by
- HematologyGeneticsImmunology
- Journals
- BloodCancerTransplantation
- Partner nations
- JapanSwitzerlandGermany
In The Last Decade
Masaya Okada
56 papers receiving 467 citations
Peers
Comparison fields: 5 of 93
- Hematology 249
- Genetics 169
- Immunology 88
- Oncology 77
- Surgery 51
Countries citing papers authored by Masaya Okada
This map shows the geographic impact of Masaya Okada'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 Masaya Okada with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Masaya Okada more than expected).
Fields of papers citing papers by Masaya Okada
This network shows the impact of papers produced by Masaya Okada. 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 Masaya Okada. The network helps show where Masaya Okada may publish in the future.
Co-authorship network of co-authors of Masaya Okada
This figure shows the co-authorship network connecting the top 25 collaborators of Masaya Okada. A scholar is included among the top collaborators of Masaya Okada 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 Masaya Okada. Masaya Okada is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 6 | |
| 5 | Multimodal Method to Understand Real-world Learning Driven by Internal Strategies | 1 |
| 6 | 117 | |
| 7 | Multi-perspective Indexing of Diverse Spatial Characteristics of an Outdoor Field toward Redesigning of Real-world Learning | 1 |
| 8 | Analytics of Real-world Learning by Re-constructing Time-series Occurrence of Qualitatively Different Learning and 3D Human Attention | 1 |
| 9 | 15 | |
| 10 | Elderly driver retraining using automatic evaluation system of safe driving skill | 1 |
| 11 | 22 | |
| 12 | 16 | |
| 13 | Method to Analyze Spatial Characteristics of a Real-World Learning Field | 3 |
| 14 | 4 | |
| 15 | 7 | |
| 16 | 9 | |
| 17 | Collaborative Environmental Learning with the DigitalEE II System Augmenting Real and Virtual Experiences | 3 |
| 18 | 4 | |
| 19 | 7 | |
| 20 | 4 |
About Masaya Okada
Masaya Okada is a scholar working on Hematology, Computer Science Applications and Genetics, having authored 59 papers that have together received 479 indexed citations. Recurring topics across this work include Hematopoietic Stem Cell Transplantation (19 papers), Chronic Myeloid Leukemia Treatments (7 papers) and Lymphoma Diagnosis and Treatment (7 papers). The work is most often cited by research in Hematology (249 citations), Genetics (169 citations) and Immunology (88 citations). Masaya Okada has collaborated with scholars based in Japan, Switzerland and Germany. Frequent co-authors include Masahiro Tada, Hiroyuki Takatsuka, Eizo Kakishita, Yoshihiro Fujimori, Y Takemoto, Koichi Miyamura, Shuichi Taniguchi, Takahiro Okamoto, Kazuteru Ohashi and Masahiro Imamura. Their work appears in journals such as Blood, Cancer and Transplantation.
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.