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Marginalized Kernels & Graph Kernels. Max Planck Institute for Biological Cybernetics Koji Tsuda. Kernels and Learning. In Kernel-based learning algorithms, problem solving is now decoupled into: A general purpose learning algorithm (e.g. SVM, PCA, … ) – Often linear algorithm

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marginalized kernels graph kernels

Marginalized Kernels & Graph Kernels

Max Planck Institute for Biological Cybernetics

Koji Tsuda

kernels and learning
Kernels and Learning
  • In Kernel-based learning algorithms, problem solving is now decoupled into:
    • A general purpose learning algorithm (e.g. SVM, PCA, …) – Often linear algorithm
    • A problem specific kernel

Simple (linear) learning algorithm

Complex Learning Task

Specific Kernel function

current synthesis
Current Synthesis
  • Modularity and re-usability
    • Same kernel ,different learning algorithms
    • Different kernels, same learning algorithms

Data 1 (Sequence)

Learning Algo 1

Kernel 1

Gram Matrix

(not necessarily stored)

Data 2 (Network)

Learning Algo 2

Kernel 2

Gram Matrix

lectures so far
Lectures so far
  • Kernel represents the similarity between two objects, defined as the dot-product in thefeature space
  • Various String Kernels
  • Importance of Positive Definiteness
kernel methods the mapping
Kernel Methods : the mapping

f

f

f

Original Space

Feature (Vector) Space

overview of this lecture
Overview of this lecture
  • Marginalized kernels
    • General idea about defining kernels using latent variables
    • An example in string kernel
  • Marginalized Graph Kernels
    • Kernel for labeled graphs (~ several hundred nodes)
    • Similarity for chemical compounds (drug discovery)
  • Diffusion Kernels
    • Closeness between nodes of a network
    • Used for function prediction of proteins based on biological networks (protein-protein interaction nets)
marginalized kernels
Marginalized kernels

K. Tsuda, T. Kin, and K. Asai.

Marginalized kernels for biological sequences

Bioinformatics, 18(Suppl. 1):S268-S275, 2002.

biological sequences classification tasks
Biological Sequences:Classification Tasks
  • DNA sequences (A,C,G,T)
    • Gene Finding, Splice Sites
  • RNA sequences (A,C,G,U)
    • MicroRNA discovery, Classification into Rfam families
  • Amino Acid Sequences (20 symbols)
    • Remote Homolog Detection, Fold recognition
structures hidden in sequences i
Structures hidden in sequences (I)
  • Exon/intron of DNA (Gene)
structures hidden in sequences ii
Structures hidden in sequences (II)
  • It is crucial to infer hidden structures and exploit them for classification

RNA

Secondary

Structure

Protein

3D Structures

hidden markov models
Hidden Markov Models
  • Visible Variable : Symbol Sequence
  • Hidden Variable : Context
  • HMM has parameters
    • Transition Probability
    • Emission Probability
  • HMM models the joint probability
hmm for gene finding
HMM for gene finding

Engineered HMM:

Some parameters are set to constants a priori

Reflect prior knowledge about the sequence

training hidden markov models
Training Hidden Markov Models
  • Training examples consist of string-context pairs
    • E.g., Fragments of DNA sequences with known splice sites
  • Parameters are estimated by the maximizing likelihood
using trained hidden markov models to estimate the context
Using trained hidden Markov models to estimate the context
  • A trained HMM can compute the posterior probability
  • Given the sequence x, what is the probability of the context h?
  • You can never predict the context perfectly!

x: A C C T G T A A A

0.0003

h: 1 2 1 2 2 2 2 1 1

0.0006

h: 2 2 1 1 1 1 2 1 1

kernels for sequences
Kernels for Sequences
  • Similarity between sequences of different lengths
  • How do you use the trained HMM for computing the kernel?

ACGGTTCAA

ATATCGCGGGAA

count kernel
Count Kernel
  • Inner product between symbol counts
  • Extension: Spectrum kernels (Leslie et al., 2002)
    • Counts the number of k-mers (k-grams) efficiently
  • Not good for sequences with frequent context change
    • E.g., coding/non-coding regions in DNA
hidden markov models for estimating context
Hidden Markov Models for Estimating Context
  • Visible Variable : Symbol Sequence
  • Hidden Variable : Context
  • HMM can estimate the posterior probability of hidden variables from data
marginalized kernels1
Marginalized kernels
  • Design a joint kernel for combined
    • Hidden variable is not usually available
    • Take expectation with respect to the hidden variable
  • The marginalized kernel for visible variables
designing a joint kernel for sequences
Designing a joint kernel for sequences
  • Symbols are counted separately in each context
  • :count of a combined symbol (k,l)
  • Joint kernel: count kernel with context information
marginalization of the joint kernel
Marginalization of the joint kernel
  • Joint kernel
  • Marginalized count kernel
computing marginalized counts from hmm
Computing Marginalized Counts from HMM
  • Marginalized count is described as
  • Posterior probability of i-th hidden variable is efficiently computed by dynamic programming
2 nd order marginalized count kernel
2nd order marginalized count kernel
  • If adjacent relations between symbols have essential meanings,the count kernel is obviously not sufficient
  • 2nd order marginalized count kernel
    • 4 neighboring symbols (i.e. 2 visible and 2 hidden) are combined and counted
protein clustering experiment
Protein clustering experiment
  • 84 proteins containing five classes
    • gyrB proteins from five bacteria species
  • Clustering methods
    • HMM + {FK,MCK1,MCK2}+K-Means
  • Evaluation
    • Adjusted Rand Index (ARI)
applications since then
Applications since then..
  • Marginalized Graph Kernels (Kashima et al., ICML 2003)
  • Sensor networks (Nyugen et al., ICML 2004)
  • Labeling of structured data (Kashima et al., ICML 2004)
  • Robotics (Shimosaka et al., ICRA 2005)
  • Kernels for Promoter Regions (Vert et al., NIPS 2005)
  • Web data (Zhao et al., WWW 2006)
  • Multiple Instance Learning (Kwok et al., IJCAI 2007)
summary marginalized kernels
Summary (Marginalized Kernels)
  • General Framework for using generative model for defining kernels
  • Fisher kernel as a special case
  • Broad applications
  • Combination with CRFs and other advanced stuff?
2 marginalized graph kernels
2. Marginalized Graph Kernels

H. Kashima, K. Tsuda, and A. Inokuchi.

Marginalized kernels between labeled graphs.

ICML 2003,pages 321-328, 2003.

motivations for graph analysis

Serial Num

Name

Age

Sex

Address

0001

○○

40

Male

Tokyo

0002

××

31

Female

Osaka

Motivations for graph analysis
  • Existing methods assume ” tables”
  • Structured data beyond this framework

→ New methods for analysis

graph structures in biology

A

C

G

C

UA

CG

CG

U

U

U

U

Graph Structures in Biology
  • Compounds
  • DNA Sequence
  • RNA

H

C

C

C

H

H

O

C

C

H

C

H

H

marginalized graph kernels
Marginalized Graph Kernels

(Kashima, Tsuda, Inokuchi, ICML 2003)

  • Going to define the kernel function
  • Both vertex and edges are labeled
label path
Label path
  • Sequence of vertex and edge labels
  • Generated by random walking
  • Uniform initial, transition, terminal probabilities
kernel definition

A c D b E

B c D a A

Kernel definition
  • Kernels for paths
  • Take expectation over all possible paths!
  • Marginalized kernels for graphs
slide36

Transition probability :

Initial and terminal : omitted

  • : Set of paths ending at v
  • KV : Kernel computed from the paths ending at (v, v’)
  • KV is written recursively
  • Kernel computed by solving

linear equations

(polynomial time)

A(v’)

v

v’

A(v)

Computation

graph kernel applications
Graph Kernel Applications
  • Chemical Compounds (Mahe et al., 2005)
  • Protein 3D structures (Borgwardt et al, 2005)
  • RNA graphs (Karklin et al., 2005)
  • Pedestrian detection
  • Signal Processing
predicting mutagenicity
Predicting Mutagenicity
  • MUTAG benchmark dataset
    • Mutation of Salmonella typhimurium
    • 125 positive data (effective for mutations)
    • 63 negative data (not effective for mutations)

Mahe et al. J. Chem. Inf. Model., 2005

classification of protein 3d structures
Classification of Protein 3D structures
  • Graphs for protein 3D structures
    • Node: Secondary structure elements
    • Edge: Distance of two elements
  • Calculate the similarity by graph kernels

Borgwardt et al. “Protein function prediction via graph kernels”, ISMB2005

classification of proteins accuracy
Classification of proteins: Accuracy

Borgwardt et al. “Protein function prediction via graph kernels”, ISMB2005

strong points of mgk
Strong points of MGK
  • Polynomial time computation O(n^3)
  • Positive definite kernel
    • Support Vector Machines
    • Kernel PCA
    • Kernel CCA
    • And so on…
biological networks
Biological Networks
  • Protein-protein physical interaction
  • Metabolic networks
  • Gene regulatory networks
  • Network induced from sequence similarity
  • Thousands of nodes (genes/proteins)
  • 100000s of edges (interactions)
physical interaction network1
Physical Interaction Network
  • Undirected graphs of proteins
  • Edge exists if two proteins physically interact
    • Docking (Key – Keyhole)
  • Interacting proteins tend to have the same biological function
metabolic network1

Oxaloacetate

Metabolic Network
  • Node: Chemical compounds
  • Edge: Enzyme catalyzing the reaction (EC Number)
  • KEGG Database (Kyoto University)
  • Collection of pathways (subnetworks)
  • Can be converted as a network of enzymes (proteins)

(S)-Malate

Fumarate

4.2.1.2

1.1.1.37

protein function prediction
Protein Function Prediction
  • For some proteins, their functions are known
  • But still functions of many proteins are unknown
function prediction using a network
Function Prediction Using a Network
  • Determination of protein’s function is a central goal of molecular biology
  • It has to be determined by biological experiments, but accurate computational prediction helps
  • Proteins close to each other in the networks tend to share the same functional category
  • Use the network for function prediction!
  • (Combination with other information sources)
prediction of one functional category
Prediction of one functional category
  • +1/-1: Labeled proteins with/without a specific function
  • ?: Unlabeled proteins
diffusion kernels kondor and lafferty 2002
Diffusion kernels (Kondor and Lafferty, 2002)
  • Function prediction by SVM using a network
    • Kernels are needed !
  • Define closeness of two nodes
    • Has to be positive definite

How Close?

definition of diffusion kernel
Definition of Diffusion Kernel
  • A: Adjacency matrix,
  • D: Diagonal matrix of Degrees
  • L = D-A: Graph Laplacian Matrix
  • Diffusion kernel matrix
    • :Diffusion paramater
  • Matrix exponential, not elementwise exponential
computation of matrix exponential
Computation of Matrix Exponential
  • Definition
  • Eigen-decomposition
actual values of diffusion kernels
Actual Values of Diffusion Kernels

Closeness from the

“central node”

interpretation stochastic process
Interpretation: Stochastic Process
  • For each node ,consider random variable
  • Initial condition
    • Zero mean, Variance
    • Independent to each other (covariance zero).
  • Each variable sends a fraction to the neighbors
stochastic process 2
Stochastic Process (2)
  • Time Evolution Operator
  • Covariance
  • Reduce the time step 1 to
  • Diffusion parameter
  • Taking the limit
interpretation via random walking
Interpretation via random walking
  • Random walking according to transition probability
  • Transition probability is constant
  • Remaining probability = Self loop
  • is equal to the probability of the walk that started at i being at j after infinite time steps
experimental results by lee et al 2006
Experimental Results by Lee et al. (2006)
  • Yeast Proteins
  • 34 functional categories
    • Decomposed into binary classification problems
  • Physical Interaction Network only
  • Methods
    • Markov Random Field
    • Kernel Logistic Regression (Diffusion Kernel)
      • Use additional knowledge of correlated functions
    • Support Vector Machine (Diffusion Kernel)
  • ROC score
    • Higher is better
concluding remarks
Concluding Remarks
  • Kernel methods have been applied to many different objects
    • Marginalized Kernels: Latent variables
    • Marginalized Graph Kernels: Graphs
    • Diffusion Kernels: Networks
  • Still active field
    • Mining and Learning with Graphs (MLG) Workshop Series
    • Journal of Machine Learning Research Special Issue on Graphs (Paper due: 10.2.2008)
  • THANK YOU VERY MUCH!!
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