Scott Cost
Impact in
- Artificial Intelligence top 2%
- Machine Learning and Data Classification
- Imbalanced Data Classification Techniques
- Neural Networks and Applications
- Machine Learning and Algorithms
- Signal Processing top 5%
- Data Management and Algorithms
Papers in
-
- Machine Learning in Bioinformatics 4
- Protein Structure and Dynamics 2
- Biochemical and Structural Characterization 1
-
- Text and Document Classification Technologies 2
- Natural Language Processing Techniques 1
- Co-authors
- Steven L. Salzberg (4 shared papers)Eileen P.G. Vining (1 shared paper)Pratibha Singhi (1 shared paper)John M. Freeman (1 shared paper)Yun Peng (1 shared paper)Ye Chen (1 shared paper)Tim Finin (1 shared paper)Yannis Labrou (1 shared paper)
- Journals
- Machine Learning (2 papers)PEDIATRICS (1 paper)Journal of Molecular Biology (1 paper)PubMed Central (1 paper)Maryland Shared Open Access Repository (USMAI Consortium) (1 paper)
- Partner nations
- United States
In The Last Decade
Scott Cost
6 papers receiving 707 citations
Peers
Comparison fields: 5 of 94
- Artificial Intelligence 553
- Signal Processing 112
- Information Systems 199
- Computer Vision and Pattern Recognition 159
- Computational Theory and Mathematics 88
Countries citing papers authored by Scott Cost
This map shows the geographic impact of Scott Cost'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 Scott Cost with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Scott Cost more than expected).
Fields of papers citing papers by Scott Cost
This network shows the impact of papers produced by Scott Cost. 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 Scott Cost. The network helps show where Scott Cost may publish in the future.
Co-authors
The 8 scholars most cited alongside Scott Cost, 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 | 1993 | 435 | |
| 2 | 1993 | 263 | |
| 3 | 1992 | 61 | |
| 4 | 1994 | 20 | |
| 5 | 1999 | 10 | |
| 6 | Exemplar-Based Learning to Predict Protein Folding | 1990 | 1 |
About Scott Cost
Scott Cost is a scholar working on Molecular Biology, Artificial Intelligence, Computer Networks and Communications, Management Information Systems and Pediatrics, Perinatology and Child Health, having authored 6 papers that have together received 790 indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (4 papers), Protein Structure and Dynamics (2 papers), Text and Document Classification Technologies (2 papers), Metabolism and Genetic Disorders (1 paper), Natural Language Processing Techniques (1 paper), Biochemical and Structural Characterization (1 paper), Pharmacological Effects and Toxicity Studies (1 paper) and Epilepsy research and treatment (1 paper). The work is most often cited by research in Artificial Intelligence (553 citations), Signal Processing (112 citations), Information Systems (199 citations), Computer Vision and Pattern Recognition (159 citations) and Computational Theory and Mathematics (88 citations). Scott Cost has collaborated with scholars based in United States. Frequent co-authors include Steven L. Salzberg, Eileen P.G. Vining, Pratibha Singhi, John M. Freeman, Yun Peng, Ye Chen, Tim Finin and Yannis Labrou. Their work appears in journals such as Machine Learning, PEDIATRICS, Journal of Molecular Biology, PubMed Central and Maryland Shared Open Access Repository (USMAI Consortium).
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.