Wisdom of crowds for robust gene network inference
Impact in
- Genetics 89
Classified as
- Journal
- Nature Methods
In The Last Decade
doi.org/10.1038/nmeth.2016 →Countries where authors are citing Wisdom of crowds for robust gene network inference
This map shows the geographic impact of Wisdom of crowds for robust gene network inference. 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 Wisdom of crowds for robust gene network inference with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wisdom of crowds for robust gene network inference more than expected).
Fields of papers citing Wisdom of crowds for robust gene network inference
This network shows the impact of Wisdom of crowds for robust gene network inference. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Wisdom of crowds for robust gene network inference.
About Wisdom of crowds for robust gene network inference
This paper, published in 2012, received 1.1k indexed citations . Written by Daniel Marbach, James C. Costello, Robert Küffner, Nic M. Vega, Robert J. Prill, Diogo M. Camacho, Kyle R. Allison, Manolis Kellis, James J. Collins and Gustavo Stolovitzky covering the research area of Molecular Biology. It is primarily cited by scholars working on Molecular Biology (882 citations), Genetics (89 citations), Artificial Intelligence (70 citations), Plant Science (59 citations) and Statistical and Nonlinear Physics (49 citations). Published in Nature Methods.
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/10.1038/nmeth.2016.