Keith Noto
Impact in
- Aging top 5%
- Genetics, Aging, and Longevity in Model Organisms
- Artificial Intelligence top 2%
- Machine Learning and Data Classification
- Anomaly Detection Techniques and Applications
- Imbalanced Data Classification Techniques
- Machine Learning and Algorithms
- Text and Document Classification Technologies
Papers in
-
- RNA and protein synthesis mechanisms 3
- Genomics and Chromatin Dynamics 2
- Epigenetics and DNA Methylation 2
-
- Imbalanced Data Classification Techniques 2
- Co-authors
- Charles Elkan (1 shared paper)Donna K. Slonim (4 shared papers)Carla E. Brodley (1 shared paper)Natalie M. Myres (3 shared papers)Catherine A. Ball (4 shared papers)Jake Byrnes (4 shared papers)Kristin A. Rand (3 shared papers)Julie M. Granka (3 shared papers)
- Journals
- BMC Bioinformatics (3 papers)PLoS Computational Biology (1 paper)Data Mining and Knowledge Discovery (1 paper)Genetics (1 paper)Nature Communications (1 paper)
- Partner nations
- United States
In The Last Decade
Keith Noto
13 papers receiving 901 citations
Hit Papers
Peers
Comparison fields: 5 of 136
- Aging 62
- Artificial Intelligence 475
- Signal Processing 61
- Neuropsychology and Physiological Psychology 7
- Computer Vision and Pattern Recognition 86
Countries citing papers authored by Keith Noto
This map shows the geographic impact of Keith Noto'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 Keith Noto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Keith Noto more than expected).
Fields of papers citing papers by Keith Noto
This network shows the impact of papers produced by Keith Noto. 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 Keith Noto. The network helps show where Keith Noto may publish in the future.
Co-authors
The 25 scholars most cited alongside Keith Noto, 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 | Learning classifiers from only positive and unlabeled data Hit paper breakdown → | 2008 | 606 |
| 2 | 2018 | 123 | |
| 3 | 2017 | 61 | |
| 4 | 2011 | 52 | |
| 5 | 2019 | 35 | |
| 6 | 2010 | 23 | |
| 7 | 2007 | 13 | |
| 8 | 2014 | 9 | |
| 9 | 2015 | 4 | |
| 10 | 2021 | 4 | |
| 11 | 2006 | 3 | |
| 12 | 2022 | 3 | |
| 13 | Learning Hidden Markov Models for Regression using Path Aggregation. | 2008 | 2 |
About Keith Noto
Keith Noto is a scholar working on Molecular Biology, Artificial Intelligence, Genetics, Computer Networks and Communications and Pediatrics, Perinatology and Child Health, having authored 13 papers that have together received 938 indexed citations. Recurring topics across this work include Genetic Associations and Epidemiology (4 papers), RNA and protein synthesis mechanisms (3 papers), Genetic and phenotypic traits in livestock (2 papers), Genetic Mapping and Diversity in Plants and Animals (2 papers), Genomics and Chromatin Dynamics (2 papers), Imbalanced Data Classification Techniques (2 papers), Epigenetics and DNA Methylation (2 papers) and Birth, Development, and Health (2 papers). The work is most often cited by research in Aging (62 citations), Artificial Intelligence (475 citations), Signal Processing (61 citations), Neuropsychology and Physiological Psychology (7 citations) and Computer Vision and Pattern Recognition (86 citations). Keith Noto has collaborated with scholars based in United States. Frequent co-authors include Charles Elkan, Donna K. Slonim, Carla E. Brodley, Natalie M. Myres, Catherine A. Ball, Jake Byrnes, Kristin A. Rand, Julie M. Granka, Amir R. Kermany and Kevin M. Wright. Their work appears in journals such as BMC Bioinformatics, PLoS Computational Biology, Data Mining and Knowledge Discovery, Genetics and Nature Communications.
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.