SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence

By C. Cai, Lianyi Han, Zhi Liang Ji, X. Chen, Yu Zong Chen. Nucleic Acids Res., 2003

591 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6091eb6a88ecfd7e429e38477ab7a017e894f1a5

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Citations in the corpus

BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions (2016, methodology): “These CTD features have been widely used for predicting protein folds [59], protein–protein interactions [60], and protein functional families [61] at accuracy levels of 74–100, 77–81, 67–99 %, respectively.”
Subsequence-based feature map for protein function classification (2008, methodology): “Automated prediction methds are successfully applied to enzyme classification according o the first (Lu et al., 2007) and second level of EC numbers Cai et al., 2003).”
DEEPre: sequence-based enzyme EC number prediction by deep learning (2017, methodology): “SVM-Prot was proposed in 2004 (Cai et al. , 2003, 2004) and updated in 2016 (Li et al. , 2016).”
mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning (2019, methodology): “…based on structure similarity (Dobson and Doig, 2005; Roy et al., 2012; Yang et al., 2015), sequence similarity (Tian et al., 2004; Quester and Schomburg, 2011), or machine learning techniques (des Jardins et al., 1997; Cai et al., 2003; Shen and Chou, 2007; Zhou et al., 2007; Li et al., 2018d).”
Support Vector Machines and Kernels for Computational Biology (2008, background): “A few examples of applying SVM to real-valued data include prediction of disease state from microarray data (see, e.g., [25]), and prediction of protein function from a set of features that include amino acid composition and various properties of the amino acids in the protein (see, e.g., [26]).”
Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence (2006, background): “From H1ði Þ and H2ði Þ defined in Equation (1) and (2), the hydrophobicity and hydrophilicity correlation functions are defined (32), respectively, as:”
Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics (2015, unknown): “The reported accuracies of a previous study on family classification have been in the range of 69.1 – 99.6% for 54 protein families [16].”