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UW-Madison ECE 539 - DNA Microarray Data Analysis using Artificial Neural Network Models

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ECE 539: Introduction to Artificial Neural Networks and Fuzzy SystemsProject ReportByVenkatanand VenkatachalpathyIntroduction:Figure 1. Genetic Information flow in a cellStatistical Parameters for the Yeast Gene Expression Feature dataOptimization of MLP learning parameters:Optimization of number of hidden layersOptimization of number of hidden layersCrate results for SVMs of different kernelsECE 539: Introduction to Artificial Neural Networks andFuzzy SystemsProject ReportDNA Microarray Data Analysisusing Artificial Neural Network ModelsByVenkatanand Venkatachalpathy Email: [email protected] Student ID: 9016417330 Section # : 1 ( MWF – 11am)Introduction:Background:According to the Central Dogma of Molecular Biology, genes made ofDeoxyribo-Nucleic Acid (DNA) are the basic units of heredity [1]. The genes could alsobe called as the information molecules of life. This is because of the fact that the geneticcode represented as a sequence of chemical monomers is decoded in each living cell intothe functional molecular network of proteins and RNA molecules (RNA – RibonucleicAcid). The proteins and RNA are termed as the functional molecules of life. Thesefunctional molecules are responsible for the physical, chemical and biological propertiesof the cell, which in turn is manifested globally in the behavioral nature of a livingorganism like humans who are made of millions of cells. Figure 1.illustrates this principleof genetic information flow from DNA to proteins. Figure 1. Genetic Information flow in a cellGenes {DNA} RNA intermediate ProteinGENE EXPRESSION(Refers to both transcription and translation)Gene Expression:The biochemical process by which Genes are first transcribed into RNAmolecules (Transcription) and then converted to the Protein molecules (Translation) isreferred to as Gene expression. This process of gene to protein information translation isnot a static phenomenon. It varies dynamically with time depending on several factorslike the stage of development of the cell (or organism), environmental conditions etc. Forexample the heat shock genes are over expressed (that is more proteins are synthesized)as soon as the cell is subjected to an environment of high temperature. However the rateof this gene expression decreases as the cell settles down from the shock. Thus the geneexpression level of heat shock genes remains at a basal rate under normal or standardconditions and as soon as these conditions change, the expression level rises which thenfinally returns to the normal level of expression in time as the cell recovers from theshock. This simple example illustrates how gene expression level by varying at amolecular level represents the chemical (to some extent behavioral) response of the cellas the environmental conditions change, the environment being one of several factors thataffects gene expression rate. The state of gene expression under normal conditions ( or average conditions) isreferred to as the normal level or basal rate. When the expression level of a gene suddenlyincreases during an interval of time (such as the case that was illustrated earlier by theexample of heat shock genes), then the genes are said to be switched “ON” during thattime interval. Similarly a gene is switched off when its expression level decreases fromthe normal rate[2].Microarray Experiments:DNA microarray technology is a recent advancement in biotechnology. Itprovides biologists with the ability to measure the expression levels of thousands ofgenes in a single experiment. These arrays consist of large numbers of specificoligonucleotides or cDNA sequences, each corresponding to a different gene, affixed to asolid surface at very precise locations [3]. When an array chip is hybridized to labeledcDNA derived from a particular tissue of interest, it yields simultaneous measurements ofthe mRNA levels in the sample for each gene represented on the chip. Since mRNAlevels are expected to correlate roughly with the levels of their translation products, theactive molecules of interest, array results can be used as a crude approximation to theprotein content and thus the ‘state’ of the sample. Ideally, one would like in addition tomeasure the levels of proteins in a cell directly, and such technology is currently beingdeveloped. The intensity of the points in the array reflects the gene expression level of thegenes at the corresponding location. In short, DNA micro arrays yield a global view ofgene expression.Microarray Experimental Data:Each data point produced by a DNA microarray hybridization experimentrepresents the ratio of expression levels of a particular gene under two differentexperimental conditions. Typically the one of the experiment is carried out under standardconditions while the other is done under the varying conditions of interest. Thus the ratioreflects the increase or decrease in the level of gene expression when the cell is under theprobing conditions with respect to the basal expression level.The result data, from a single experiment with n genes on a single chip, is a series of nexpression-level ratios. The numerator of each ratio is the expression level of the gene inthe varying condition of interest, whereas the denominator is the expression level of thegene in the reference condition. The data from a series of m such experiments may berepresented as a gene expression matrix, in which each of the n rows consists of an m-element expression vector for a single gene. The values of the Gene expression vectorare normalized on a logarithmic scale with the total norm of the log values of the ratiosbeing 1.Motivation for the project:The pattern of gene expression in a cell characterizes its current state. Virtuallyall differences in cell state or type are correlated with changes in the mRNA levels ofmany genes. Expression patterns of many uncharacterized genes provide clues to theirpossible function by comparison [1]. This leads to great many potential applications inmedicine and molecular biology especially in identification of metabolic pathways,complex genetic diseases, drug discovery and toxicology analysis etc.One major application of microarray data is in the area of functional genomicswhere the


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UW-Madison ECE 539 - DNA Microarray Data Analysis using Artificial Neural Network Models

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