The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
An Introduction to Machine Learning
Open accessAbstract
In the last few years, machine learning (ML) and artificial intelligence have seen a new wave of publicity fueled by the huge and ever‐increasing amount of data and computational power as well as the discovery of improved learning algorithms. However, the idea of a computer learning some abstract concept from data and applying them to yet unseen situations is not new and has been around at least since the 1950s. Many of these basic principles are very familiar to the pharmacometrics and clinical pharmacology community. In this paper, we want to introduce the foundational ideas of ML to this community such that readers obtain the essential tools they need to understand publications on the topic. Although we will not go into the very details and theoretical background, we aim to point readers to relevant literature and put applications of ML in molecular biology as well as the fields of pharmacometrics and clinical pharmacology into perspective.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
(2008) · Journal of Machine Learning Research · Cited by 33,051
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,522
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
(1996) · Cited by 20,179
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
Least squares quantization in PCM
Lloyd, S. (1982) · IEEE Transactions on Information Theory · doi:10.1109/tit.1982.1056489 · Cited by 15,836
Research works printed but not linked: 9
- Dorogush A.V. Ershov V.&Gulin A.CatBoost: gradient boosting with categorical features support.arXiv preprint arXiv:1810.11363(2018). · arXiv
- 10.1021/ci300348u · doi:10.1021/ci300348u
- Mitchell T.M., 1997, Machine Learning
- 10.1287/opre.28.1.97 · doi:10.1287/opre.28.1.97
- 10.1007/978-3-642-69928-3_27 · doi:10.1007/978-3-642-69928-3_27
- 10.1164/ajrccm/140.6.1573 · doi:10.1164/ajrccm/140.6.1573
- Goodfellow I., 2016, Deep Learning
- 10.1007/s40262-014-0211-9 · doi:10.1007/s40262-014-0211-9
- 10.1111/j.1365-2125.2005.02533.x · doi:10.1111/j.1365-2125.2005.02533.x
Cited by
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473
“Some describe ML as the primary AI application, while others describe it as a subset of AI [11, 12].”
BackgroundUsing machine learning approaches for multi-omics data analysis: A review
Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107739 · Cited by 903
CANN: An intrusion detection system based on combining cluster centers and nearest neighbors
Lin, W.-C., Ke, S.-W., & Tsai, C.-F. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.01.009 · Cited by 484
Giordano, M. R., Malings, C., Pandis, S. N., Presto, A. A., McNeill, V. F., Westervelt, D. M., Beekmann, M., & Subramanian, R. (2021) · Journal of Aerosol Science · doi:10.1016/j.jaerosci.2021.105833 · Cited by 426
The promise of artificial intelligence and deep learning in PET and SPECT imaging
Arabi, H., AkhavanAllaf, A., Sanaat, A., Shiri, I., & Zaidi, H. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.03.008 · Cited by 268
Metadata
- Authors
- Badillo, S., Banfai, B., Birzele, F., Davydov, I. I., Hutchinson, L., Kam‐Thong, T., Siebourg‐Polster, J., Steiert, B., & Zhang, J. D.
- Venue
- Clinical Pharmacology & Therapeutics
- Publisher
- Wiley
- Published
- 2020-03-03
- Volume
- 107
- Issue
- 4
- Pages
- 871-885
- ISSN
- 0009-9236 (ISSN-L)
- 1532-6535
- OpenAlex
- W3010521003
- Cited by
- 799
- References
- 68
- FWCI
- 29.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Badillo, S., Banfai, B., Birzele, F., Davydov, I. I., Hutchinson, L., Kam‐Thong, T., Siebourg‐Polster, J., Steiert, B., & Zhang, J. D. (2020). An Introduction to Machine Learning. Clinical Pharmacology & Therapeutics. https://doi.org/10.1002/cpt.1796
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