The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Machine Learning for Science: State of the Art and Future Prospects
Abstract
Recent advances in machine learning methods, along with successful applications across a wide variety of fields such as planetary science and bioinformatics, promise powerful new tools for practicing scientists. This viewpoint highlights some useful characteristics of modern machine learning methods and their relevance to scientific applications. We conclude with some speculations on near-term progress and promising directions.
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Cites
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Cited by
TRY – a global database of plant traits
KATTGE, J., DÍAZ, S., LAVOREL, S., PRENTICE, I. C., LEADLEY, P., BÖNISCH, G., GARNIER, E., WESTOBY, M., REICH, P. B., WRIGHT, I. J., et al. (2011) · Global Change Biology · doi:10.1111/j.1365-2486.2011.02451.x · Cited by 2,638
Motwani, M., Dey, D., Berman, D. S., Germano, G., Achenbach, S., Al-Mallah, M. H., Andreini, D., Budoff, M. J., Cademartiri, F., Callister, T. Q., et al. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehw188 · Cited by 692
Human factors in model interpretability: Industry practices, challenges, and needs
Hong, S. R., Hullman, J., & Bertini, E. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3392878 · Cited by 219
Ethical Management of Artificial Intelligence
Brendel, A. B., Mirbabaie, M., Lembcke, T.-B., & Hofeditz, L. (2021) · Sustainability · doi:10.3390/su13041974 · Cited by 174
Goldberg, S. B., Flemotomos, N., Martinez, V. R., Tanana, M. J., Kuo, P. B., Pace, B. T., Villatte, J. L., Georgiou, P. G., Van Epps, J., Imel, Z. E., et al. (2020) · Journal of Counseling Psychology · doi:10.1037/cou0000382 · Cited by 141
Metadata
- Authors
- Mjolsness, E., & DeCoste, D.
- Venue
- Science
- Published
- 2001-09-14
- Volume
- 293
- Issue
- 5537
- Pages
- 2051-2055
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2159793693
- Cited by
- 379
- References
- 1
- FWCI
- 7.55
- Type
- journal article
- Language
- en
- Open access
- closed
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- Mjolsness, E., & DeCoste, D. (2001). Machine Learning for Science: State of the Art and Future Prospects. Science. https://doi.org/10.1126/science.293.5537.2051
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
