Evaluation of secondary structure of proteins from UV circular dichroism spectra using an unsupervised learning neural network.

By Miguel Andrade, Pablo Chacón, J. Merelo, F. Morán. 1993

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Deep learning in neural networks: An overview (2014, background): “on et al., 1995; Dayan and Zemel, 1995; Amari et al., 1996; Deco and Parra, 1997), and to uncover and disentangle hidden underlying sources of signals (e.g., Jutten and Herault, 1991; Schuster, 1992; Andrade et al., 1993; Molgedey and Schuster, 1994; Comon, 1994; Cardoso, 1994; Bell and Sejnowski, 1”
DICHROWEB: an interactive website for the analysis of protein secondary structure from circular dichroism spectra (2002, unknown): “Data analyses can be done using a number of the most popular secondary structure determination algorithms, including SELCON3 (Sreerama and Woody, 1993; Sreerama et al., 1999), CONTIN (Provencher and Glockner, 1981; Van Stokkum et al., 1990), VARSLC (Compton and Johnson, 1986; Manavalan and Johnson, ”
DichroWeb, a website for calculating protein secondary structure from circular dichroism spectroscopic data (2021, methodology): “A neural network technique, K2D, 13 is also available at the site.”