Neural networks for secondary structure and structural class predictions

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Protein secondary structure prediction for a single-sequence using hidden semi-Markov models (2006, methodology): “The first rule is also known as the 'EHL' mapping [34,35], the second rule is the one used in PSIPRED [1] and earlier outlined by Rost and Sander [36], while, finally, the third rule is the common 'CK' mapping, which is the one used in BSPSS and other methods [17,37,38].”
Modeling the language of life – Deep Learning Protein Sequences (2019, background): “The concept was 19 quickly taken up [4-8], and it was shown how the improvement in prediction increased with 20 even larger families including more diverse evolutionary information [9, 10].”
Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes (2005, methodology): “…acid composition is a typical discrete form that has been widely used in predicting protein structural class (Bahar et al., 1997; Cai et al., 2000; Chandonia and Karplus, 1995; Chou and Zhang, 1993; Chou and Maggiora, 1998; Chou and Zhang, 1994; Chou, 1989; Deleage and Roux, 1987; Klein, 1986;…”