A novel method for protein secondary structure prediction using dual‐layer SVM and profiles

By Jian Guo, Hu Chen, Zhirong Sun, Yuanlie Lin. 2004

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A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction (2014, methodology): “Machine learning pattern recognition has provedmore useful for such prediction, andmanymethods have utilized support vector machines [7], [8], [9], [10] and neural networks [11], [12], [13], [14], [15], [16], [17].”
Knowledge-based machine learning methods for macromolecular 3D structure prediction (2016, methodology): “Support vector machines (SVM) have also been applied for SS prediction (Hua and Sun, 2001; Kim and Park, 2003; Ward et al, 2003; Guo et al, 2004; Duan et al, 2008).”
The effect of long‐range interactions on the secondary structure formation of proteins (2005, background): “…Petersen et al. 2000), hidden Markov models (Karplus et al. 1998; Lin et al. 2005), and support vector machines (Hua and Sun 2001; Ward et al. 2003; Guo et al. 2004), which try to capture local sequence patterns of known examples of secondary structures in an input multiple sequence alignment.”
Protein secondary structure prediction for a single-sequence using hidden semi-Markov models (2006, background): “Algorithms of protein secondary structure prediction frequently employ neural networks [1-7], support vector machines [8-13] and hidden Markov models [14-16].”