Noah Youngs
Impact in
- Molecular Biology top 10%
- RNA Research and Splicing
- RNA modifications and cancer
- RNA and protein synthesis mechanisms
- Bioinformatics and Genomic Networks
- Machine Learning in Bioinformatics
- Information Systems top 2%
- Blockchain Technology Applications and Security
Papers in
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- RNA modifications and cancer 1
- Microbial Metabolic Engineering and Bioproduction 1
- Machine Learning in Bioinformatics 1
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- Machine Learning and Data Classification 1
- Cryptography and Data Security 1
- Co-authors
- David Schwartz (1 shared paper)Richard Bonneau (4 shared papers)Duncan Penfold-Brown (3 shared papers)Kevin Drew (2 shared papers)Yasuhiro Murakawa (1 shared paper)Christoph Dieterich (1 shared paper)Markus Landthaler (1 shared paper)Matthias Selbach (1 shared paper)
- Journals
- Genome biology (1 paper)PLoS Computational Biology (1 paper)Molecular Cell (1 paper)Bioinformatics (1 paper)Figshare (1 paper)
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Noah Youngs
7 papers receiving 1.7k citations
Noah Youngs's Hit Papers
Peers
Comparison fields: 5 of 87
- Molecular Biology 1.2k
- Information Systems 355
- Cancer Research 159
- Computer Networks and Communications 235
- Computational Theory and Mathematics 66
Countries citing papers authored by Noah Youngs
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
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-authors
The 14 scholars most cited alongside Noah Youngs, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | The mRNA-Bound Proteome and Its Global Occupancy Profile on Protein-Coding Transcripts Hit paper breakdown → | 2012 | 951 |
| 2 | The Ripple Protocol Consensus Algorithm Hit paper breakdown → | 2014 | 393 |
| 3 | 2016 | 289 | |
| 4 | 2014 | 31 | |
| 5 | 2013 | 27 | |
| 6 | 2015 | 8 | |
| 7 | 2016 | 1 |
About Noah Youngs
Noah Youngs is a scholar working on Molecular Biology, Artificial Intelligence, Spectroscopy, Information Systems and Computer Networks and Communications, having authored 7 papers that have together received 1.7k indexed citations. Recurring topics across this work include Machine Learning and Data Classification (1 paper), RNA modifications and cancer (1 paper), Microbial Metabolic Engineering and Bioproduction (1 paper), Blockchain Technology Applications and Security (1 paper), Advanced Proteomics Techniques and Applications (1 paper), Cryptography and Data Security (1 paper), Machine Learning in Bioinformatics (1 paper) and Distributed systems and fault tolerance (1 paper). The work is most often cited by research in Molecular Biology (1.2k citations), Information Systems (355 citations), Cancer Research (159 citations), Computer Networks and Communications (235 citations) and Computational Theory and Mathematics (66 citations). Noah Youngs has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include David Schwartz, Richard Bonneau, Duncan Penfold-Brown, Kevin Drew, Yasuhiro Murakawa, Christoph Dieterich, Markus Landthaler, Matthias Selbach, Mathias Munschauer and Markus Schueler. Their work appears in journals such as Genome biology, PLoS Computational Biology, Molecular Cell, Bioinformatics and Figshare.
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.