A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach.

By Sujun Hua, Zhirong Sun. 2001

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In Molecular Biology

Citations in the corpus

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].”
Secondary structure prediction with support vector machines (2003, methodology): “The highest quoted Q 3 score of 73.5% and the fairly modest improvement over PHD suggests that the SVM classifier, described in (Hua and Sun, 2001), is not competitive with the best modern methods.”
Protein 8-class secondary structure prediction using conditional neural fields (2010, methodology): “Support vector machines (SVM) have also been applied to secondary structure prediction [16]– [20].”
Prediction of protein subcellular localization (2006, background): “It is well known in the study of secondary structure prediction 22–24 that the homologous sequences are meticulously removed from the testing-training data sets.”
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].”
Improved prediction of protein-protein binding sites using a support vector machines approach. (2005, background): “…accessibility (Yuan et al., 2002), β-edge strands (Siepen et al., 2003), single nucleotide polymorphisms (Krishnan and Westhead, 2003), protein secondary structure (Hua and Sun, 2001; Kim and Park, 2003), protein quaternary structure (Zhang et al., 2003) and T-cell epitopes (Zhao et al., 2003).”
SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence (2003, methodology): “It has also been employed in a number of other protein studies including protein–protein interaction prediction (15), fold recognition (27), solvent accessibility (28) and structure prediction (29,30).”
The effect of long‐range interactions on the secondary structure formation of proteins (2005, background): “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.”
Support vector machines : theory and applications (2005, background): “Support Vector Machines (SVMs) [1, 2, 3] have been widely applied to pattern classification problems [4, 5, 6, 7, 8] and nonlinear regressions [9, 10, 11].”
Ranking predicted protein structures with support vector regression (2007, methodology): “SVMs have been used successfully in numerous bioinformatics applications,24 including remote protein homology detection,25 protein function classification,26 protein interaction prediction,27 protein secondary structure prediction,28 and microarray data analysis.”
Conditional Neural Fields (2009, methodology): “SVMpro [26] uses the position specific frequency as input feature.”
Support Vector Machines with Applications (2006, methodology): “pression proﬁles. The SVMs have been applied recently to tissue classiﬁcation [26], gene function prediction [59], protein subcellular location prediction [31], protein secondary structure prediction [32] and protein fold prediction [23], among other tasks. In almost all cases, SVMs outperformed oth”
Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks (2016, background): “, SVMs [Sujun and Zhirong, 2001], recurrent neural networks (RNNs) [Pollastri et al.”