Support vector machine approach for protein subcelluar localization prediction (SubLoc)

1. Support vector machine approachfor protein subcelluar localization prediction(SubLoc) Kim Hye Jin Intelligent Multimedia Lab. 2001.09.07.

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

3. 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)

4. Materials and Methods(1) • Dataset - SWISSPROT release 33.0 • 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)

6. Support vector machine(1) • A quadratic optimization problem with boundary constraints and one linear equality constraints • Basically for two classification problem input vector x =(x1, .. x20) ( xi :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 • Mapping by a kernel function K(xi,xj)

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

9. 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

10. 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

11. 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) • SVMlight • Speed • Less than 10 min on a PC running at 500MHz

12. 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

13. 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)

16. 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)

19. 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

20. Robustness to errors in the N-terminal sequence

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

23. 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)

24. 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