Link Prediction in Linked Data of Interspecies Interactions using

Hybrid Recommendation Approach

Hideaki TAKEDAProfessor

Chiang Mai, Thailand JIST 2014 November 10th, 2014

Tsuyoshi HOSOYAMycologist

Rathachai CHAWUTHAIrathachai.c@gmail.com

Linked Open Data for ACadamiaLODAC

“Salix pierotii”

lodac:Salix

species:hasSuperTaxon

lodac:Salix_ pierotii

National Museum of Nature and Science

30,000 Interactions4,000 Fungi7,000 Hosts

Let’s find the Missing Linksbetween speciesLPII

Link Prediction

on Interspecies Interactions

Objective:

To predict missing links between fungi and hosts

Agenda

•Dataset

• Introduction

•Hybrid Recommendation• Collaborative Filtering• Community Structure• Biological Classification

• Evaluation

• Summary

• Future work

lodac:Melampsora_yezoensis

rdfs:label “Melampsora yezoensis”@la ;

species:hasTaxonRank species:Species ;

species:hasSuperTaxon lodac:Melampsora .

lodac:Melampsora species:hasTaxonRank species:Genus.

lodac:Salix_pierotii

rdfs:label “Salix pierotii”@la ;

rdf:type species:ScientificName ;

species:hasSuperTaxon lodac:Salix .

lodac:Salix species:hasTaxonRank species:Genus.

lodac:Melampsora_yezoensis species:growsOn lodac:Salix_pierotii.

Dataset

6

Host

Fungus

Link

lodac:Melampsora

lodac:Salix

species:hasSuperTaxon

species:hasSuperTaxon

species:growsOn

lodac:Melampsora_

yezoensis

lodac:

Salix_pierotii

7

903 Rust Fungi 2,001 Hosts

2,966 Links

BiologicalClassification

of Fungi

BiologicalClassification

of Hosts

Selected

8

List of

Fungus-Host

interaction with

predictive scores

DATA PREPARATION LPII APPROACH

RESULT

transform data using

a Weight Function

BIOLOGIST

Making Observation

Collaborative

Filtering

Finding

Missing

Links

Combine

Score Score Score

1 2

3

4

Intr

od

uct

ion

9

Community

Structure

Biological

Classification

Fungus-Host

Interaction

Dataset

Generate Result

Collaborative Filtering

Some fungi found at the same host are common neighbors.

If some close neighbors of the fungus fare found at a host h,the fungus f may be found at the host h.

10

1

f1

f2

f3

f4

f5

h1

h2

h3

h4

h5

Fungi Hosts

11

f1

f2

h1

h2

PCF

( f1,h2 ) = ?

Collaborative Filtering for Link Prediction

Sum of similarities between fungi with common hosts

12

f1

f2

f3

f4

f5

h1

h2

h3

h4

h5

w = ?

Jaccard Index

13

f1

f2

f3

f4

f5

h1

h2

h3

h4

h5

w = 0.50

w = 0.33

14

Predictive Score usingCollaborative Filtering

PCF( f1,h2 ) = 0.50

PCF( f2,h3 ) = 0.33

PCF( f1,h3 ) = ???

PCF( f4,h3 ) = ???

f1

f2

f3

f4

f5

h1

h2

h3

h4

h5

w = 0.50

w = 0.33

PCF( f4,h5 ) = ???

etc.15

( Dash red lines are predicted links)

Community Structure

If a host h is commonly foundin the community of the fungus f, the fungus f may be found at the host h.

16

2

f1

f2

f3

f4

f5

h1

h2

h3

h4

h5

0.50

0.33

f4

f5

0.50

0.33

Bipartite GraphProjection of Fungi

f2

f1

f3

17

CommunityStructure

o f

Rust Fungi

18

Using Modularity with Random Walk

f4

f5

0.50

0.33

Projection of Fungi

f2

f1

f3

CommunityStructureh1

h2

h3

h4

h5

Community #1

Community #2

Community #3

PCS( f,h ) =

Number of links between

the community of the

fungus f and the host h

Number of all links

given by the community

of the fungus f

PCS( f3,h1 ) =2

5= 0.40

19

20

How to deal with

many very smal l

communit ies?

Biological Classification

If a host h is commonly foundin the biological classification of the fungus f, the fungus f may be found at the host h.

21

3

BIOLOGICAL CLASSIFICATION (TAXONOMY)

Domain e.g. Eukaryota

Kingdom e.g. Fungi

Phylum e.g. Basidiomycota

Class e.g. Urediniomycetes

Order e.g. Uredinales

Family e.g. Melampsoraceae

Genus e.g. Melampsora

Species e.g. Melampsora Yezoensis

Classification Example

22

f1

f2

f3

f4

f5

h1

h2

h3

h4

h5

with Biological Classification

G1

G2

Biological Classification

23

PBC( f,h ) =

Number of links between the

biological classification of the

fungus f and the host h

Number of all links given by

the biological classification of

the fungus f

PBC( f4,h2 ) =1

4= 0.25

PCF( f,h )

PII( f,h )

Hybrid Recommender Approach

PCS( f,h )

PBC( f,h )

CollaborativeFiltering

CommunityStructure

BiologicalClassification

24

Combination of

Evaluation

25

Training set(2,500 links)

Test set(500 links)

Candidates(400,000 links)

f1

f2

f3

f4

f5

h1

h2

h3

h4

h5

f1

f2

f3

f4

f5

h1

h2

h3

h4

h5

Learning and Testing

f1

f2

f3

f4

f5

h1

h2

h3

h4

h5

All PossibleLinks

Existent Links Missing Links

0.4210.8640.4660.4900.3660.5150.3130.0760.3620.9020.0690.5240.8760.4640.8390.504

26

AUC Area Under the receiver operating characteristic Curve

① PII( f1,h2 ) = 0.70

② PII( f2,h3 ) = 0.60

③ PII( f1,h3 ) = 0.50

④ PII( f4,h3 ) = 0.40

⑤ PII( f2,h2 ) = 0.30

⑥ PII( f3,h3 ) = 0.20

⑦ PII( f4,h3 ) = 0.10

① PII( f1,h2 ) = 0.70

② PII( f2,h2 ) = 0.60

③ PII( f3,h3 ) = 0.50

④ PII( f2,h3 ) = 0.50

⑤ PII( f1,h3 ) = 0.40

⑥ PII( f4,h3 ) = 0.30

⑦ PII( f4,h3 ) = 0.10

Predicted List #1

(sorted by predictive score)

Low AUCHigh AUC

For n comparisons,

• n' is number of times when

the test links have higher

score than the missing links.

• n" is number of times when

the test links have same

score as the missing links.

Predicted List #2

(sorted by predictive score)

27( Red scores are test links)

AUC Area Under the receiver operating characteristic Curve

Combination Scoring Function(s) AUC

Stand-alone functionPCF 0.859

PCS 0.823

PBC 0.680

Summation of functionsPCF + PCS 0.867

PCF + PBC 0.876

PCS + PBC 0.865

PCF + PCS + PBC 0.892

Multiplication of functionsPCF × PCS 0.817

PCF × PBC 0.862

PCS × PBC 0.827

PCF × PCS × PBC 0.818

28

RDF data of

Interspecies

Interactions

Projection

of Fungi

Collaborative

Filtering

Community

Structure

Biological

Classification

SPARQL

querying

being input of

Scoring Functions

ranking

predictions

in decreasing

order

Predicted Missing Links

of Fungus-Host together with

prediction scores

DATA PREPARATION LPII APPROACH

RESULT

Bipartite Graph

Missing

Links

Community

Detection Method

transform data using

a Weight Function

DOMAIN

EXPERT

found?yes

update

knowledgebase

NOTE

select

connected fungi

clustering using

Biological

Classification

make

observation

Data

Process

Third party method

Scoring Function

Input argument

Linear Operation

Decision

Dataflow

+

find

missing

linkssharing

LOD

Cloud

PII(f,h) +

PCF(f,h) PCS

(f,h) PBC(f,h)

1 2

3

4

29

Ove

rall

PCF( f,h )PII( f,h )

Hybrid Recommender Approach

PCS( f,h )

PBC( f,h )

α

β

γγ should be very

low as about 0.1 and 0.2.

30

Conclusion

Informatics Biology

• RDF Model for Interspecies Interaction• Improve the use of Collaborative filtering

with sparse dataset using• Community Structure• and Biological Classification

• It has been found that • In general case, PCF + PCS is enough.• But when a node

• having a few common neighbors• and locating in a small community,• PBC becomes a key player for

making link prediction.

• This model supports the view that most fungi under the same genus have similar parasite behavior.

• Some predicted links having high predictive score, such as,• Phragmidium mucronatum ハマナス• Phragmidium fusiforme ハマナス• Phragmidium potentillae イワキンバイ

have been discovered from other literatures.

• Next enhancement is to analyze fungal species into fungal spore types.

31

PCF( f,h )PII( f,h )

Future Work

PCS( f,h )

PBC( f,h )

α

β

γ

x1 (f,h)

x2 (f,h)

x3 (f,h)

32

RDF data of

Interspecies

Interactions

NFungi-Projection

or GProjFungi

Collaborative

Filtering

Community

Structure

Biological

Classification

SPARQL

querying

being input of

Scoring Functions

ranking

predictions

in decreasing

order

Predicted Missing Links

of Fungus-Host together with

prediction scores

DATA PREPARATION LPII APPROACH

RESULT

Bipartite Graph

GBipt

including

LExist

Missing

Links

Or

LMiss

clustering using

a Community

Detection Method

transform data using

a Weight Function

DOMAIN

EXPERT

found?yes

update

knowledgebase

NOTE

select

connected fungi

clustering using

Biological

Classification

make

observation

Data

Process

Third party method

Scoring Function

Input argument

Linear Operation

Decision

Dataflow

+

find

missing

linkssharing

LOD

Cloud

PII(f,h) +

PCF(f,h) PCS

(f,h) PBC(f,h)

1 2

3

4Ove

rall

α β γ

33

Any idea for improvement?