Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
bcl-2 gene hypomethylation and high-level expression in B-cell chronic lymphocytic leukemia
1993533 citationsM Hanada, D Delia et al.Bloodprofile →
Interactions among members of the Bcl-2 protein family analyzed with a yeast two-hybrid system.
1994523 citationsTakaaki Sato, M Hanada et al.Proceedings of the National Academy of Sciencesprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of M Hanada'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 M Hanada with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M Hanada more than expected).
This network shows the impact of papers produced by M Hanada. 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 M Hanada. The network helps show where M Hanada may publish in the future.
Co-authorship network of co-authors of M Hanada
This figure shows the co-authorship network connecting the top 25 collaborators of M Hanada.
A scholar is included among the top collaborators of M Hanada 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 M Hanada. M Hanada is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hanada, M, Yukihiro Kishimoto, K Nakai, et al.. (2000). [Antithymocyte globulin treatment in 11 patients with aplastic anemia].. PubMed. 41(7). 563–7.2 indexed citations
3.
Yokota, Soichiro, et al.. (1998). [Pleural malignant mesothelioma with osseous cartilaginous and rhabdomyogenic differentiation].. PubMed. 36(8). 696–701.4 indexed citations
4.
Krajewski, Stanisław, Jane Chatten, M Hanada, & JC Reed. (1995). Immunohistochemical analysis of the Bcl-2 oncoprotein in human neuroblastomas. Comparisons with tumor cell differentiation and N-Myc protein.. PubMed. 72(1). 42–54.42 indexed citations
5.
Bodrug, Sharon, Christine Aimé‐Sempé, Takaaki Sato, et al.. (1995). Biochemical and functional comparisons of Mcl-1 and Bcl-2 proteins: evidence for a novel mechanism of regulating Bcl-2 family protein function.. PubMed. 2(3). 173–82.62 indexed citations
6.
Sato, Takaaki, M Hanada, Sharon Bodrug, et al.. (1994). Interactions among members of the Bcl-2 protein family analyzed with a yeast two-hybrid system.. Proceedings of the National Academy of Sciences. 91(20). 9238–9242.523 indexed citations breakdown →
Hanada, M, D Delia, Antonella Aiello, Edward A. Stadtmauer, & JC Reed. (1993). bcl-2 gene hypomethylation and high-level expression in B-cell chronic lymphocytic leukemia. Blood. 82(6). 1820–1828.533 indexed citations breakdown →
9.
Hanada, M, S Krajewski, Shigeki Tanaka, et al.. (1993). Regulation of Bcl-2 oncoprotein levels with differentiation of human neuroblastoma cells.. PubMed. 53(20). 4978–86.131 indexed citations
Hanada, M & M Shimoyama. (1987). Potential limitation of growth-inhibitory action of recombinant human tumor necrosis factor (PAC-4D) due to easy induction of resistance: evidence in vitro.. PubMed. 78(11). 1266–73.4 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.