Identifying Extracellular Plant Proteins Based on

Frequent Subsequences of Amino Acids

Y. Wang, O. Zaiane, R. Goebel

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IntroductionProtein: linear sequence of amino acidsProtein subcellular localization Plant: nuclear, cytoplamic,

mitochondria, extracellular, …Intracellular vs. Extracellular Sequence information alone Class imbalance Transparency

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Related WordN-terminal sorting signalsAmino acid compositionLexical analysisIntegrative approachSubsequence methods

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Predicting Extracellular Proteins

Feature ExtractionSupport Vector MachineBoostingFrequent Pattern Method

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Feature ExtractionFrequent subsequences: subsequences that occur in more than a certain percentage of extracellular proteins Strong discriminative power Perform similar functions via

relationed biochemical mechanism Capture local similarity

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Generalized Suffix Tree

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Support Vector MachineInput data represented as feature vectorsFind a linear separator that separate the data and maximize the marginKernel function: nonlinear separator

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SVM for extracellular protein prediction

Data Transformation(sequencevector) Frequent subsequences as features Transform protein sequence as binary

vectorsKernel Functions Linear kernel Polynomial kernel RBF kernel

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BoostingIterative algorithms to improve weak classifierDifferent weighted distribution of examples in each iterationIncrease the weights of incorrectly classified examples, and decrease the weights of correctly classified ones

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AdaBoost

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Frequent Pattern MethodFrequent pattern: *X1*X2*…*Xn* extracellular X1,X2,…Xn are frequent

subsequences “*” can be substituted to zero or up to

MaxGap amino acids when matching a protein sequence

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FOIL algorithm

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Z-number

:accuracy of rule R:support of rule R

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ExperimentsDataset(PASub project at UofA) Plant: 3293 proteins, 171 extracellularFive-cross validation

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Evaluation MatrixOverall accuracy is not good enoughF-measure

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Result(SVM with subsequence)

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Result(Boosting with subsequence)

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Result(Frequent Pattern)

MinLen=3Min_gain=0.1

03.08.0

MinSup=5%MinConf=80%MaxGap=300

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Result(SVM with composition)

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Result(Boosting with composition)

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Cross Comparision

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SVM with combined features

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Boosting with combined features

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Effects of MinLen on SVM

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Effects of MinLen on boosting

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ConclusionPresented three methods for identifying extracellular proteins based on frequent subsequence of amino acidsSVM achieves the best resultFSP method provides easily interpretable rules

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Future WorkUse for information about proteins (e.g., structure, function, …)Integrating amino acid composition into FSP methodIncorporate more biological knowledge