cancer algorithm.pptx
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High-throughput measurement microarray Genes and pathways prediction - important area in genomic. Microarray technology makes it possible through the expression
levels. some of the genes or pathways couldntbe analysed.
researches on this type of gene identification have been maximized developed a number of computational approaches understanding the cellular and progressive actions at a molecular
level through biological networks.
types of biological networks: protein-protein interactions, metabolic interactions, genetic interaction,
transcription factor binding and protein phosphorylationnetworks(Zhu, et al., 2007).
INTRODUCTION
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Cancer has become a genetic syndrome
improve the life span of cancer patients
various types of cancer are:
i. Breast cancer
ii. Prostate cancer
iii. Colon cancer
iv. Cervical cancer
v. Liver cancer
LITERATURE REVIEW
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Network-based approaches network based approach -produced to identify molecular interaction
of gene expression.
Network based approaches outperformed than gene-based approachesin cancer metastasis prediction (Chuang, et al., 2007). networks models are simple and crucial for understanding of complex
networks and help maintain biological systems. three types of network model
i. random networks,ii. scale-free networks and
iii. hierarchical networks nodes/vertices :-Biomolecules such as genes, proteins and
metabolites are represented as in molecular network. edges/links of nodes:- physical or fucntional interactions of protein,
genetics
LITERATURE REVIEW
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Types of network
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Network based algorithms available areas follows in the table 1 below.
Algorithms Description Reference
Single protein analysis of
network (SPAN)
Single protein analysis of network (SPAN) methodology used to
identify cancer genes and additionally, they computationally identified
pathways among interactors across signatures and validated them
using a similarity metric and patient survival.
Chen, et al. 2013
Markov Clustering (MCL)
MCL isa semi-supervised algorithm used to cluster the weightednetwork into a series of gene interaction modules.
Wu & Stein2012
NetRank NetRank is a derivative of PageRank algorithm to predict gen
interaction networks.
Roy, et al. 2012
NetWalker A NEM signature-based Survival Support Vector Machine (SSVM)
prognostic model was trained using a microarray gene expression
dataset and genes were integrated using NetWalker algorithm.
Shi, et al. 2012
Network Guilt-by-association(GBA)
GBA is GooglesPageRank algorithm which is used to detect diseasegenes in a particular dataset.
Lee, et al. 2011
Gene network Inference with
Ensemble of Trees (GENIE3)
GENIE3, a new algorithm for the prediction of GRNs where tree-
based ensemble methods Random Forests or Extra-Trees used to
predict expression pattern of one of the genes (target gene).
Huynh-Thu, et al. 2010
Maximum-expectation Gene
Cover (MGC)
This approach is basically adopted by the greedy approximation
algorithm to Weighted Set Cover.
Karni, et al. 2009
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Partial Correlation and
Information Theory (PCIT)
PCIT, used for the reconstruction of gene co-expression networks
(GCN) to classify significant gene to gene associations defining
edges in the reconstruction of GCN.
Reverter & Chan 2008
Weighted Gene Co-
expression Network Analysis
(WGCNA)
Weighted correlation network analysis (WGCNA) can be used for
finding clusters (modules) of highly correlated genes.
Langfelder & Horvath
2008
Supervised Inference of
Regulatory Networks(SIRENE)
SIRENE (Supervised Inference of Regulatory Networks), a new
method for the prediction of gene regulatory networks and focus onseparating target genes from non-targets for each transcription factor.
Mordelet & Vert 2008
Markov random field (MRF) MRF used ICM (Iterative conditional mode algorithm) to identify
genes and its subnetworks that are related to diseases.
Wei & Li2007
Context Likelihood
Relatedness (CLR)
An unsupervised network inference method, context likelihood of
relatedness (CLR), which uses transcriptional profilesof an organism
across a diverse set of conditions to systematically determine
transcriptional regulatory interactions.
Faith, et al. 2007
The Algorithm for
Reconstrcution of Accurate
Cellular Networks
(ARACNE)
Fundamentally, designed for extent up to the complication of
regulatory networks and remove the majority of indirect interactions.
Margolin, et al. 2006
Dynamic bayesian network
(DBN)
DBN-based approach will increases accuracy and reduced
computational time compared with existing BN methods.
Zhou & Conzen 2005