Noah Youngs
Impact in
- Information Systems top 2%
- Blockchain Technology Applications and Security
- Molecular Biology top 10%
- RNA Research and Splicing
- RNA modifications and cancer
- RNA and protein synthesis mechanisms
- RNA regulation and disease
Papers in
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- Genomics and Phylogenetic Studies 1
- Biomedical Text Mining and Ontologies 1
- RNA modifications and cancer 1
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- Machine Learning and Data Classification 1
- Machine Learning and Algorithms 1
- Imbalanced Data Classification Techniques 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)Markus Schueler (1 shared paper)Matthias Selbach (1 shared paper)Markus Landthaler (1 shared paper)Miha Milek (1 shared paper)
- Partner nations
- United StatesGermany
In The Last Decade
Noah Youngs
5 papers receiving 1.3k citations
Noah Youngs's Hit Papers
Peers
Comparison fields: 5 of 81
- Information Systems 310
- Molecular Biology 907
- Cancer Research 157
- Computer Networks and Communications 203
- Management Information Systems 33
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 | 932 |
| 2 | The Ripple Protocol Consensus Algorithm Hit paper breakdown → | 2014 | 338 |
| 3 | 2014 | 26 | |
| 4 | 2013 | 20 | |
| 5 | 2015 | 6 | |
| 6 | Positive-Unlabeled Learning in the Context of Protein Function Prediction | 2014 | 0 |
About Noah Youngs
Noah Youngs is a scholar working on Molecular Biology, Artificial Intelligence, Computer Networks and Communications, Information Systems and Infectious Diseases, having authored 6 papers that have together received 1.3k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (1 paper), Machine Learning and Data Classification (1 paper), Machine Learning and Algorithms (1 paper), Biomedical Text Mining and Ontologies (1 paper), Imbalanced Data Classification Techniques (1 paper), Cryptography and Data Security (1 paper), Distributed systems and fault tolerance (1 paper) and RNA modifications and cancer (1 paper). The work is most often cited by research in Information Systems (310 citations), Molecular Biology (907 citations), Cancer Research (157 citations), Computer Networks and Communications (203 citations) and Management Information Systems (33 citations). Noah Youngs has collaborated with scholars based in United States and Germany. Frequent co-authors include David Schwartz, Richard Bonneau, Duncan Penfold-Brown, Kevin Drew, Markus Schueler, Matthias Selbach, Markus Landthaler, Miha Milek, Mathias Munschauer and Yasuhiro Murakawa. Their work appears in journals such as Molecular Cell, PLoS Computational Biology and Bioinformatics.
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.