Cancer algorithm.pptx

8/10/2019 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

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Cancer algorithm.pptx