Maximum likelihood inference of protein phylogeny and the origin of chloroplasts
- Journal
- Journal of Molecular Evolution
In The Last Decade
doi.org/10.1007/bf02109483 →Countries where authors are citing Maximum likelihood inference of protein phylogeny and the origin of chloroplasts
This map shows the geographic impact of Maximum likelihood inference of protein phylogeny and the origin of chloroplasts. 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 Maximum likelihood inference of protein phylogeny and the origin of chloroplasts with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maximum likelihood inference of protein phylogeny and the origin of chloroplasts more than expected).
Fields of papers citing Maximum likelihood inference of protein phylogeny and the origin of chloroplasts
This network shows the impact of Maximum likelihood inference of protein phylogeny and the origin of chloroplasts. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Maximum likelihood inference of protein phylogeny and the origin of chloroplasts.
About Maximum likelihood inference of protein phylogeny and the origin of chloroplasts
This paper, published in 1990, received 702 indexed citations . Written by Hirohisa Kishino, Takashi Miyata and Masami Hasegawa covering the research area of Molecular Biology. It is primarily cited by scholars working on Molecular Biology (460 citations), Genetics (207 citations) and Ecology (165 citations). Published in Journal of Molecular Evolution.
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.
This paper is also available at doi.org/10.1007/bf02109483.