Neural language representations predict outcomes of scientific research

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Bagrow, James P.; Berenberg, Daniel; Bongard, Joshua;
  • Subject: Computer Science - Computation and Language | Statistics - Machine Learning | Computer Science - Artificial Intelligence | Computer Science - Computers and Society | Computer Science - Learning

Many research fields codify their findings in standard formats, often by reporting correlations between quantities of interest. But the space of all testable correlates is far larger than scientific resources can currently address, so the ability to accurately predict c... View more
  • References (30)
    30 references, page 1 of 3

    [1] N. Jean, M. Burke, M. Xie, W. M. Davis, D. B. Lobell, and S. Ermon, “Combining satellite imagery and machine learning to predict poverty,” Science, vol. 353, no. 6301, pp. 790-794, 2016. 1

    [2] G. F. Cooper, C. F. Aliferis, R. Ambrosino, J. Aronis, B. G. Buchanan, R. Caruana, M. J. Fine, C. Glymour, G. Gordon, B. H. Hanusa, et al., “An evaluation of machine-learning methods for predicting pneumonia mortality,” Artificial intelligence in medicine, vol. 9, no. 2, pp. 107-138, 1997. 1

    [3] R. Caruana, Y. Lou, J. Gehrke, P. Koch, M. Sturm, and N. Elhadad, “Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1721-1730, ACM, 2015. 1

    [4] D. C. Cireşan, A. Giusti, L. M. Gambardella, and J. Schmidhuber, “Mitosis detection in breast cancer histology images with deep neural networks,” in International Conference on Medical Image Computing and Computerassisted Intervention, pp. 411-418, Springer, 2013. 1

    [5] A. Cruz-Roa, A. Basavanhally, F. González, H. Gilmore, M. Feldman, S. Ganesan, N. Shih, J. Tomaszewski, and A. Madabhushi, “Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks,” in Medical Imaging 2014: Digital Pathology, vol. 9041, p. 904103, International Society for Optics and Photonics, 2014. 1

    [6] J. Xu, X. Luo, G. Wang, H. Gilmore, and A. Madabhushi, “A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images,” Neurocomputing, vol. 191, pp. 214- 223, 2016. 1

    [7] P. Raccuglia, K. C. Elbert, P. D. Adler, C. Falk, M. B. Wenny, A. Mollo, M. Zeller, S. A. Friedler, J. Schrier, and A. J. Norquist, “Machine-learning-assisted materials discovery using failed experiments,” Nature, vol. 533, no. 7601, p. 73, 2016. 1

    [8] G. T. Richards, R. C. Nichol, A. G. Gray, R. J. Brunner, R. H. Lupton, D. E. V. Berk, S. S. Chong, M. A. Weinstein, D. P. Schneider, S. F. Anderson, et al., “Efficient photometric selection of quasars from the Sloan Digital Sky Survey: 100,000 z < 3 quasars from Data Release One,” The Astrophysical Journal Supplement Series, vol. 155, no. 2, p. 257, 2004. 1

    [9] P. R. Fiorentin, C. Bailer-Jones, Y. S. Lee, T. C. Beers, T. Sivarani, R. Wilhelm, C. A. Prieto, and J. Norris, “Estimation of stellar atmospheric parameters from SDSS/SEGUE spectra,” Astronomy & Astrophysics, vol. 467, no. 3, pp. 1373-1387, 2007. 1

    [10] P. Baldi, P. Sadowski, and D. Whiteson, “Searching for exotic particles in high-energy physics with deep learning,” Nature communications, vol. 5, p. 4308, 2014. 1

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