Complex Networks and Data Mining on genetic databases

KnowledgeDiscovery inDatabases

throughComplex

Networks:application tophylodynamics

Luiz Max F.de Carvalho

ScientificComputingProgramme(PROCC),

FiocruzPan American

Center forFoot-and-

Mouth Disease(PAHO/WHO)

WaFiS 2012

KnowledgeDiscovery inDatabases(KDD)

ComplexNetworks

Example 1:Chitinpathwayphylogeny

Example 2:Foot-and-mouth diseasevirus in SouthAmerica

Who’s this guy?

Knowledge Discovery in Databases throughComplex Networks: application to

phylodynamics

Luiz Max F. de CarvalhoScientific Computing Programme (PROCC), FiocruzPan American Center for Foot-and-Mouth Disease

(PAHO/WHO)

WaFiS 2012

September 28, 2012

throughComplex

FiocruzPan American

Center forFoot-and-

WaFiS 2012

ComplexNetworks

Outline

1 Knowledge Discovery in Databases (KDD)

2 Complex Networks

3 Example 1: Chitin pathway phylogeny

4 Example 2: Foot-and-mouth disease virus in South America

throughComplex

FiocruzPan American

Center forFoot-and-

WaFiS 2012

ComplexNetworks

Knowledge Discovery in Databases (KDD)

Lots of data

human brain very limited processing capacity

Information → Knowledge

Increasing number of molecular data (sequences, 3Dstructures, antigenicity,. . . )

Is it possible to explore these databases to discover usefulstuff?

throughComplex

FiocruzPan American

Center forFoot-and-

WaFiS 2012

ComplexNetworks

Well. . . Let’s see

throughComplex

FiocruzPan American

Center forFoot-and-

WaFiS 2012

ComplexNetworks

[We may use] Complex Networks

Graphs → G = (V ,E )

throughComplex

FiocruzPan American

Center forFoot-and-

WaFiS 2012

ComplexNetworks

Yeah, but how?

We can explore the ”dynamic signature” of these ComplexNetworks, i.e., study and compare their structural properties.Some useful formulas:

Clustering Coefficient < c >: 3×#triangles#triples

Degree distribution PK =∑∞

K ′=K pK ′

Diameter: max(d(i , j))

throughComplex

FiocruzPan American

Center forFoot-and-

WaFiS 2012

ComplexNetworks

Ok, Let’s work then

1 Grab n sequences;

2 Create an n × n matrix using some kind of (normalized)distance (say, S);

3 For each σ ∈ [0, 1] build M(σ) such that:

mij(σ) =

{1 if Sij > σ,

0 if Sij < σ.

In a sense, we are transforming a single network in a family ofnetworks.

throughComplex

FiocruzPan American

Center forFoot-and-

WaFiS 2012

ComplexNetworks

Analysis

We shall explore the relationships between these networks:First, define a higher-order neighborhood indicator function,such that you binarize the adjacency matrix with regard thepath length `, obtaining a matrix M =

∑D`=1 `M(`). Then

δ(α, β) =1

N∑i=1

N∑j=1

(mij(α)

D(α)−

mij(β)

D(β)) (1)

Evaluating δ(σ, σ + ∆σ) can give some interesting insights.

throughComplex

FiocruzPan American

Center forFoot-and-

WaFiS 2012

ComplexNetworks

Example 1: Chitin pathway phylogeny

Proteins related to the chitin metabolic pathway from1605 complete genomes;

BLAST distances (which are asymmetric);

Search for phylogenetic relationships

throughComplex

FiocruzPan American

Center forFoot-and-

WaFiS 2012

ComplexNetworks

Example 1: Some results

throughComplex

FiocruzPan American

Center forFoot-and-

WaFiS 2012

ComplexNetworks

Example 1: Some more results

throughComplex

FiocruzPan American

Center forFoot-and-

WaFiS 2012

ComplexNetworks

Example 1: The expected Network(s)

throughComplex

FiocruzPan American

Center forFoot-and-

WaFiS 2012

ComplexNetworks

Example 2: Foot-and-mouth disease virus in SouthAmerica

S was built with phylogenetic (TN93) distances for NTand JTT distances for AA;

Try to make sense of a somewhat big data set (167 seqs);

Extract some nice patterns;

throughComplex

FiocruzPan American

Center forFoot-and-

WaFiS 2012

ComplexNetworks

Indexes × σ

(a) (b)

throughComplex

FiocruzPan American

Center forFoot-and-

WaFiS 2012

ComplexNetworks

A nice network

throughComplex

FiocruzPan American

Center forFoot-and-

WaFiS 2012

ComplexNetworks

Some more developments

throughComplex

FiocruzPan American

Center forFoot-and-

WaFiS 2012

ComplexNetworks

Related Work

Identify transmission clusters (HIV, HCV) (Lewis et al,2008,Plos Medicine)

Explore scale-free behavior in phylodynamics (Shiino,2012, Frontiers in Microbiology)

throughComplex

FiocruzPan American

Center forFoot-and-

WaFiS 2012

ComplexNetworks

Future Directions

Explore the spatial aspect in the construction of SMaybe S = µ+ S(G )α

Power law analysis

Implement assortativity

Suggestions. . .

throughComplex

FiocruzPan American

Center forFoot-and-

WaFiS 2012

ComplexNetworks

Thank You!

Complex Networks and Data Mining on genetic databases

Education

Transcript of Complex Networks and Data Mining on genetic databases

genetic databases ppt

Genetic algorithms in Data Mining

Indexing and Data Mining in Multimedia Databases

Data Mining of Protein Databases

Biomedical Literature Mining for Biological Databases Annotation

Phenotype Capture in Genetic Variant Databases

UNIT - I 1.Data mining, data warehousing, multimedia databases, and Web databases 2000s: Stream data management and mining, Data mining and its applications, Web technology (XML, data

Biomedical Literature Mining for Biological Databases ...malerba/publications/Chapter16.pdf · Biomedical Literature Mining for Biological Databases Annotation 269 difficulties encountered

Biomedical Literature Mining for Biological Databases ... · 16 Biomedical Literature Mining for Biological Databases Annotation Margherita Berardi 1,5, Donato Malerba 1, Roberta

Databases and Marketing Customer databases Data Warehouses Database Mining.

Text Databases. Outline Spatial Databases Temporal Databases Spatio-temporal Databases Data Mining Multimedia Databases Text databases Image and video.

Data Mining and Knowledge Discovery in Databases.

Modeling Storing and Mining Moving Object Databases

ASSOCIATION RULE MINING OVER MULTIPLE · PDF fileassociation rule mining over multiple databases: partitioned and ... association rule mining over multiple databases: partitioned and

Data Mining: Concepts and Techniques Mining sequence patterns in transactional databases.

Genetic Databases International Collaboration and Secondary Uses

15-826: Multimedia Databases and Data Mining

Query Optimization of Frequent Itemset Mining on Multiple Databases Mining on Multiple Databases David Fuhry Department of Computer Science Kent State.

OECD Guidelines on Human Biobanks and Genetic Research Databases

Mining Sequential Patterns in Dense Databases