Secondary structure prediction with support vector machines

By Jonathan J. Ward, Liam J. McGuffin, Bernard F. Buxton, David T. Jones. 2003

257 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Ocean Engineering

How researchers see this paper

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

In Computer Networks and Communications

1. Zhiyong Wang
2. David Baker

In Electrical and Electronic Engineering

1. Zhiyong Wang

In Ocean Engineering

1. Jack Hanson
2. Zhiyong Wang
3. Yaoqi Zhou
4. Kuldip K. Paliwal

Citations in the corpus

• A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction (2014, methodology): “Machine learning pattern recognition has proved more useful for such prediction, and many methods have utilized support vector machines[7-10] and neural networks[11-17].”
• Improving protein secondary structure prediction based on short subsequences with local structure similarity (2010, background): “In this category, Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Hidden Markov Models (HMMs) are the most widely used machine learning algorithms [15-21].”
• Classification of Nuclear Receptors Based on Amino Acid Composition and Dipeptide Composition* (2004, unknown): “The classifiers were developed using the support vector machines since it was shown in the past that SVM is better in classifying the biological data in comparison with artificial neural network (25-26).”
• Sixty-five years of the long march in protein secondary structure prediction: the final stretch? (2016, background): “Examples are support vector machines [76, 77], Bayesian or hidden semi-Markov network [78,”
• 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.”
• MUFOLD-SS: New Deep Inception-Inside-Inception Networks for Protein Secondary Structure Prediction (2018, background): “The earlier machine-learning methods for protein secondary structure prediction cannot effectively account for nonlocal interactions between residues that are close in the 3 D space but far from each other in their sequence positions.(9) Many existing techniques rely on sliding windows of size 10–20”
• 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).”
• 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].”
• Generalized Fragment Picking in Rosetta: Design, Protocols and Applications (2011, background): “Taking into account the shares of the three predictors defined as in quota.def file listed above, one would expect 200 * 0.1 = 20 helical fragments: 12 from PsiPred, 4 from SAM and 4 from Jufo.”