Support vector machine approach for protein subcellular localization prediction

By Sujun Hua, Zhirong Sun. Bioinform., 2001

905 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e0f322c561f732366014e88191aa750e7036298d

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In Molecular Biology

Citations in the corpus

Better prediction of sub‐cellular localization by combining evolutionary and structural information (2003, methodology): “Two publicly available methods address a similar general-purpose prediction of localization without sequence motifs or homology, namely the neural network-based program NNPSL 31 and the support vector machine-based program SubLoc.”
Recent progress in protein subcellular location prediction. (2007, background): “During the past 15 years, a variety of predictors have been developed to deal with the challenge [7–39].”
Support Vector Machines with Applications (2006, methodology): “ing biological samples using their genetic expression 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 task”
Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs (2003, methodology): “Hua and Sun (2001a) constructed a subcellular localization prediction system using support vector machines (SVMs) based on amino acid compositions.”
An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange (2010, neutral): “…stock market prediction (Armano, Marchesi, & Murru, 2005; Avcı, 2007; Chu, Chen, Cheng, & Huang, 2009; Egeli, Ozturan, & Badur, 2003; Hiemstra, 1995; Hua & Sun, 2001; Huang & Tsai, 2009; Karaatli, Gungor, Demir, & Kalayci, 2005; Kim & Chun, 1998; Kim & Han, 2000; Kimoto, Asakawa, Yoda, & Takeoka,…”
Improved prediction of signal peptides: SignalP 3.0. (2004, background): “Ci¢dSi; ( 5 ) where ¢dSi is the difierence between the average S-score of d positions before and d positions after position i:”
ProFET: Feature engineering captures high-level protein functions (2015, background): “Examples for such predictions include protein–protein interactions (Bock and Gough, 2001; Cheng and Baldi, 2007), discriminating outer membrane proteins (Gromiha and Suwa, 2005), membrane topology (Nugent and Jones, 2009), subcellular localization (Hua and Sun, 2001) and more.”
Predicting subcellular localization of proteins for Gram‐negative bacteria by support vector machines based on n‐peptide compositions (2004, methodology): “On the other hand, a number of studies (Cedano et al. 1997; Andrade et al. 1998; Reinhardt and Hubbard 1998; Yuan 1999; Chou 2001; Hua and Sun 2001; Chou and Cai 2002) have shown that amino acid compositions are useful in discriminating protein subcellular localization sites.”
ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST (2004, methodology): “In the past, various methods have been developed to predict the subcellular location of proteins using different approaches (2).”
Non-classical protein secretion in bacteria (2005, result): “Several amino acid composition based features were selected for both predictors confirming previous results, which have demonstrated the importance of amino acid composition in relation to this problem [31,32,43-46].”
Discriminatively trained Markov model for sequence classification (2005, methodology): “[11].”