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(SubLoc) Support vector machine approach for protein subcelluar localization prediction (SubLoc) Kim Hye Jin Intelligent Multimedia Lab

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Contents Introduction Materials and Methods –Support vector machine –Design and implementation of the prediction system –Prediction system assessment Result Discussion and Conclusion

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Introduction (1) Motivation –A key functional charactristic of potential gene products such as proteins Traditional methods –Protein N-terminal sorting signals Nielsen et al.,(1999), von Heijne et al (1997) –Amino acid composition Nakashima and Inshikawa(1994), Nakai(2000) Andrade et al(1998), Cedano et al(1997), Reinhart and Hubbard(1998)

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Materials and Methods(1) Dataset - SWISSPROT release Essential sequences which complete and reliable localization annotations -No transmembrane proteins By Rost et al.,1996; Hirokawa et al.,1998;Lio and Vnnucci,2000 -Redundancy reduction -Effectiveness test - by Reinhardt and Hubbard (1998)

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Support vector machine(1) A quadratic optimization problem with boundary constraints and one linear equality constraints Basically for two classification problem input vector x =(x 1,.. x 20 ) ( x i : aa) output vector y {-1,1} Idea –Map input vectors into a high dimension feature space –Construct optimal separating hyperplane(OSH) –maximize the margin; the distance between hyperplane and the nearest data points of each class in the space H K(x i,x j ) –Mapping by a kernel function K(x i,x j )

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Support vector machine(2) Decision function Where the coefficient by solving convex quadratic programming

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Support vector machine(3) Constraints –In eq(2), C is regularization parameter => control the trade- off between margin and misclassification error Typical kernel functions Eq(3), polynomial with d parameter Eq(4), radial basic function (RBF) with r parameter

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Support vector machine(4) Benefits of SVM –Globally optimization –Handle large feature spaces –Effectively avoid over-fitting by controlling margin –Automatically identify a small subset made up of informative points

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Design and implementation of the prediction system Problem : Multi-class classification problem –Prokaryotic sequences 3 classes –Eukaryotic sequences 4 classes Solution –To reduce the multi-classification into binary classification –1-v-r SVM( one versus rest ) QP problem –LOQO algorithm (Vanderbei, 1994) SVM light Speed –Less than 10 min on a PC running at 500MHz

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Prediction system assessment Prediction quality test by jackknife test –Each protein was singled out in turn as a test protein with the remaining proteins used to train SVM

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Results (1) SubLoc prediction accuracy by jackknife test –Prokaryotic sequence case d=1and d=9 for polynomial kernel =5.0 for RBF C = 1000 for SVM constraints –Eukaryotic sequence case d =9 for polynomial kernel =16.0 for RBF C=500 for each SVM Test : 5 – fold cross validation ( since limited computational power)

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Comparison based on amino acid composition –Neural network Reinhardt and Hubbard, 1998 –Covariant discriminant algorithm Chou and Elrod, 1999 Based on the full sequence information in genome sequence –Markov model ( Yuan, 1999)

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Assigning a reliability index RI (reliability index) Diff between the highest and the second - highest output value of the 1-v-r SVM 78% of all sequence have RI 3 and 95.9% correct prediction

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Robustness to errors in the N-terminal sequence

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Discussion and Conclusion SVM information condensation –The number of SVs is quite small –The ratio of SVs to all training is 13-30%

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SVM parameter selection Little influence on the classification performance –Table8 shows with little difference between kernel functions –Robust characteristic of the dataset by Vapnik(1995)

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Improvement of the perfomance Combining with other methods –Sorting signal base method and amino acid composition Signal : sensitive to errors in N terminal Composition: weakness in similar aa Incorporate other informative features Bayesian system integrating in the whole genome expression data Fluorescence microscope images

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