Prediction of protein subcellular locations by incorporating quasi-sequence-order effect.

By Kuo-Chen Chou. Biochemical and Biophysical Research Communications - BBRC, 2000

361 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/69116de77cf03e3475e3a1e756187ac5432dcf42

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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): “(i n), n 2,3,4) have been accounted for by using pseudoamino acid composition.(42,43) With the availability of many”
COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning (2020, background): “The physicochemical features included the compositions, transitions, and distributions (69), quasi-sequence-order (70), MoreauBroto auto-correlation (71, 72), and Geary auto-correlation (73) of various physicochemical properties such as charge, hydrophobicity, polarity, and solvent accessibility (74”
iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences (2018, neutral): “This figure is adapted from (Chou, 2000).”
Pseudo Amino Acid Composition and its Applications in Bioinformatics, Proteomics and System Biology (2009, methodology): “The others, such as the physicochemical distance mode [104] and amphiphilic pattern mode [105], can also be used to derive different types of Pse-AAC.”
REVIEW : Recent advances in developing web-servers for predicting protein attributes (2009, methodology): “…co-occurrence matrix factors extracted from the cellular automaton images were used to represent the samples of proteins through their pseudo amino acid composition [18,53], followed by utilizing the augmented covariant-discriminant classifier [12,164] to operate the prediction of GPCR-CA.”
Recent progress in protein subcellular location prediction. (2007, methodology): “The others, such as the physicochemical distance mode [59] and the amphiphilic pattern mode [60], can also be used to derive different types of PseAA composition.”
A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data (2012, background): “If the unrelated receptors do not possess similar ligands in the learning dataset, then the protein encoding (structural and physicochemical properties derived from their primary sequence) plays a key role for the unrelated targets identification, which have been well applied for the prediction of p”
Prediction of protein subcellular localization (2006, background): “In recent years, many efforts[1-21] have been made to predict protein subcellular locations based on the cell structure definition as described above.”
Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs (2003, background): “Furthermore, Chou (2000, 2001) observed improvements of prediction accuracy when correlations of residue pairs were considered in addition to the amino acid composition.”