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Network

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Vanashika Sharma

on 12 August 2014

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Transcript of Network

Vanashika Sharma
09101008
Objective
Procedure
Network
validation and analysis
References
Feature Selection
Prediction of protein-protein interactions
Data
Pre-processing
Co-Expression
[EXP]

Co-Essentiality
[ESS]
Domain Domain interaction [DDI]
Co-Functionality
[FUN]
Co-operon involvement
and gene clusters
[OPR]
Co-Pathway
[PAT]
PERL scripts
for
pairwise data generation
To Identify Potential
Drug Targets by constructing
Protein-Protein Interaction
(PPI) Network
Conventional Approach
One drug-One target
Solution
Identifying Multiple Drug Targets (Using Network Biology)
Construction of Protein-Protein
Interaction (PPI) Networks
GEO
ArrayExpress
Analyzing network structure & properties
Databases:
DEG
OGEE
EGGS
Examining the position of known drug targets
Identifying
experimental PPIs in PA
DEG
EGGS
OGEE
Co-localization
[LOC]
Proteome Analyst Specialized
Subcellular Localization (PASSL)
TMHMM Server
FEATURE COMPILATION
Functional Genomics & Network Biology of Pathogenic Microbes
Co-expression (EXP)
Ashish Tewari
09101069
Aman Gupta
09501830
Pairwise compilation of ESS, FUN, TRH, OPR,LOC, PAT using Perl Scripts: Output
ESS
FUN
OPR
PAT
TRH
Gene Ontology
database
KEGG
DOOR
ProOpDb
DOMINE
LOC
Trans-membrane helices [TRH]
Ambesi-Impiombato A., Bernardo D. (2006). Computational Biology and Drug Discovery: From Single-Target to Network Drugs. Current Bioinformatics. 1:3-13.

Barabasi A., Oltvai Z. (2004). Network Biology: Understanding the Cell’s Functional Organization. Nature. 5:101-113.

Chen X., Liu M. (2005). Prediction of protein–protein interactions using random decision forest framework. Bioinformatics. 21:4394–4400.

Csermely P., Pongor S., Agoston V. (2005). The efficiency of multi-target drugs: the network approach might help drug design. TRENDS in Pharmacological Sciences. 26: 178-182.

Deng J., Deng L., Su S., Zhang M., Lin X. (2011). Investigating the predictability of essential genes across distantly related organisms using an integrative approach. Nucleic Acids Res. 39: 795–807.

Pavlopoulos G., Secrier M., Moschopoulos C., Soldatos T.G., Kossida S., Aerts J., Schneide R.,Bagos P. (2011). Using graph theory to analyze biological networks. BioData Mining. 4:10.
Raman K. (2010).Construction and analysis of protein–protein interaction networks. BioMed Central. 2:2.

Turanalp M., Can T. (2008). Discovering functional interaction patterns in protein-protein interaction networks.BMC Bioinformatics. 9:276-294.

Yıldırım M.,Goh K., Cusick M., Barabsi A., Vidal M. (2007). Drug–target network. Nature Biotechnology . 25:1119-1126.

Zhang M.,Su S., Bhatnagar R., Hassett J., Lu L. (2012). Prediction and Analysis of the Protein Interactome in Pseudomonas aeruginosa to Enable Network-Based Drug Target Selection. Plos one. 7:1-13.
Compiled File
Clustering coefficient
Tendency of a graph to be divided into clusters.
Biological Networks have high
Clique
Subset of vertices in a network such that every two proteins are connected by an edge.
Identification of groups of consistently co-expressed genes.
Graph Density
Sparse graph vs. Dense Graph
Number of Connections per node set
Biological networks are sparsely connected
Source
Graph theory
Source
Source
Source
Source
Source
Source
Source
Vipul S kumar
09101021
Centralization

Shows whether a network has star-like topology or not.
Centralization approximately equal to one shows higher chances of star like topology.
Future work plan
Random Forest
Classifier
THANK YOU
Full transcript