Data-driven hypothesis weighting increases detection power in genome-scale multiple testing

347 indexed citations

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This paper, published in 2016, received 347 indexed citations. Written by Nikolaos Ignatiadis, Bernd Klaus, Judith B. Zaugg and Wolfgang Huber covering the research area of Molecular Biology and Statistics and Probability. It is primarily cited by scholars working on Molecular Biology (190 citations), Genetics (69 citations) and Immunology (47 citations). Published in Nature Methods.

Countries where authors are citing Data-driven hypothesis weighting increases detection power in genome-scale multiple testing

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This map shows the geographic impact of Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. 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 Data-driven hypothesis weighting increases detection power in genome-scale multiple testing with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Data-driven hypothesis weighting increases detection power in genome-scale multiple testing more than expected).

Fields of papers citing Data-driven hypothesis weighting increases detection power in genome-scale multiple testing

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Data-driven hypothesis weighting increases detection power in genome-scale multiple testing.

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.3885.

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