Noah Youngs

2.3k total citations · 2 hit papers
6 papers, 1.3k citations indexed

About

Noah Youngs is a scholar working on Molecular Biology, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Noah Youngs has authored 6 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Molecular Biology, 2 papers in Artificial Intelligence and 1 paper in Computer Networks and Communications. Recurrent topics in Noah Youngs's work include Machine Learning in Bioinformatics (2 papers), Genomics and Phylogenetic Studies (2 papers) and Imbalanced Data Classification Techniques (1 paper). Noah Youngs is often cited by papers focused on Machine Learning in Bioinformatics (2 papers), Genomics and Phylogenetic Studies (2 papers) and Imbalanced Data Classification Techniques (1 paper). Noah Youngs collaborates with scholars based in United States and Germany. Noah Youngs's co-authors include David Schwartz, Richard Bonneau, Duncan Penfold-Brown, Kevin Drew, Yasuhiro Murakawa, Christoph Dieterich, Emanuel Wyler, Miha Milek, Markus Schueler and Matthias Selbach and has published in prestigious journals such as Molecular Cell, Bioinformatics and PLoS Computational Biology.

In The Last Decade

Noah Youngs

5 papers receiving 1.3k citations

Hit Papers

align trajectories

What are hit papers?

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 mRNA-Bound Proteome and Its Global Occupancy Profile on Protein-Coding Transcripts

2012 923 citations Mathias Munschauer, Björn Schwanhäußer et al. Molecular Cell profile →
The Ripple Protocol Consensus Algorithm

2014 339 citations David Schwartz, Noah Youngs et al. profile →

Peers

Noah Youngs

MB

IS

CNC

CR

AI

Luigi Cerulo Italy

Gilberto Fragoso United States

George Magklaras United Kingdom

Eugene Izumchenko United States

Ken Frazer United States

Katherine Wolstencroft United Kingdom

Sean Whalen United States

Dexter Pratt United States

Steve Harris United Kingdom

Duangdao Wichadakul Thailand

Luigi Cerulo Italy

Noah Youngs

444 ×0.5

MB

1.4k ×4.6

IS

422 ×2.1

CNC

192 ×1.1

CR

431 ×4.5

AI

Citations per year, relative to Noah Youngs Noah Youngs (= 1×) peers Luigi Cerulo

Countries citing papers authored by Noah Youngs

Since Specialization

Citations

This map shows the geographic impact of Noah Youngs'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 Noah Youngs with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Noah Youngs more than expected).

Fields of papers citing papers by Noah Youngs

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Noah Youngs. 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 Noah Youngs. The network helps show where Noah Youngs may publish in the future.

Co-authorship network of co-authors of Noah Youngs

This figure shows the co-authorship network connecting the top 25 collaborators of Noah Youngs. A scholar is included among the top collaborators of Noah Youngs 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 Noah Youngs. Noah Youngs is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

6 of 6 papers shown

1.

Youngs, Noah, Dennis Shasha, & Richard Bonneau. (2015). Positive-Unlabeled Learning in the Face of Labeling Bias. 11. 639–645. 6 indexed citations

2.

Youngs, Noah, Duncan Penfold-Brown, Richard Bonneau, & Dennis Shasha. (2014). Negative Example Selection for Protein Function Prediction: The NoGO Database. PLoS Computational Biology. 10(6). e1003644–e1003644. 26 indexed citations

3.

Youngs, Noah, et al.. (2014). Positive-Unlabeled Learning in the Context of Protein Function Prediction.

4.

Schwartz, David, et al.. (2014). The Ripple Protocol Consensus Algorithm. 339 indexed citations breakdown →

5.

Youngs, Noah, Duncan Penfold-Brown, Kevin Drew, Dennis Shasha, & Richard Bonneau. (2013). Parametric Bayesian priors and better choice of negative examples improve protein function prediction. Bioinformatics. 29(9). 1190–1198. 20 indexed citations

6.

Munschauer, Mathias, Björn Schwanhäußer, Yasuhiro Murakawa, et al.. (2012). The mRNA-Bound Proteome and Its Global Occupancy Profile on Protein-Coding Transcripts. Molecular Cell. 46(5). 674–690. 923 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.

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