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.
The neighbor-joining method: a new method for reconstructing phylogenetic trees.
198752.4k citationsNaruya Saitou, M NeiMolecular Biology and Evolutionprofile →
MEGA5: Molecular Evolutionary Genetics Analysis Using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods
201135.4k citationsKoichiro Tamura, Daniel G. Peterson et al.Molecular Biology and Evolutionprofile →
MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) Software Version 4.0
200725.9k citationsKoichiro Tamura, Joel T. Dudley et al.Molecular Biology and Evolutionprofile →
Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees.
19939.4k citationsKoichiro Tamura, M NeiMolecular Biology and Evolutionprofile →
Mathematical model for studying genetic variation in terms of restriction endonucleases.
This map shows the geographic impact of M Nei'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 Nei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M Nei more than expected).
This network shows the impact of papers produced by M Nei. 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 Nei. The network helps show where M Nei may publish in the future.
Co-authorship network of co-authors of M Nei
This figure shows the co-authorship network connecting the top 25 collaborators of M Nei.
A scholar is included among the top collaborators of M Nei 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 Nei. M Nei 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.
Tamura, Koichiro, et al.. (2011). MEGA5: Molecular Evolutionary Genetics Analysis Using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods. Molecular Biology and Evolution. 28(10). 2731–2739.35449 indexed citations breakdown →
Kumar, Sudhir, M Nei, Joel T. Dudley, & Koichiro Tamura. (2008). MEGA: A biologist-centric software for evolutionary analysis of DNA and protein sequences. Briefings in Bioinformatics. 9(4). 299–306.2981 indexed citations breakdown →
Pamilo, Pekka & M Nei. (1988). Relationships between gene trees and species trees.. Molecular Biology and Evolution. 5(5). 568–83.1288 indexed citations breakdown →
17.
Saitou, Naruya & M Nei. (1987). The neighbor-joining method: a new method for reconstructing phylogenetic trees.. Molecular Biology and Evolution. 4(4). 406–25.52445 indexed citations breakdown →
18.
Nei, M, et al.. (1986). Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions.. Molecular Biology and Evolution. 3(5). 418–26.4220 indexed citations breakdown →
19.
Nei, M & Dan Graur. (1984). Extent of protein polymosphism and the neutral mutation theory. Evolutionary Biology. 17. 73–118.226 indexed citations
20.
Tajima, Fumio & M Nei. (1984). Estimation of evolutionary distance between nucleotide sequences.. Molecular Biology and Evolution. 1(3). 269–85.825 indexed citations breakdown →
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.