Transcript Support vector machine approach for protein subcelluar

Support vector machine approach
for protein subcelluar localization prediction
(SubLoc)
Kim Hye Jin
Intelligent Multimedia Lab.
2001.09.07.
Contents
• Introduction
• Materials and Methods
– Support vector machine
– Design and implementation of the
prediction system
– Prediction system assessment
• Result
• Discussion and Conclusion
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)
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)
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)
Support vector machine(2)
• Decision function
• Where the coefficient
quadratic programming
by solving convex
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
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
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
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
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)
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)
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
Robustness
to errors in the N-terminal sequence
Discussion and Conclusion
• SVM information condensation
– The number of SVs is quite small
– The ratio of SVs to all training is 13-30%
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)
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