1. DNA Microarray Data Analysis using Artificial Neural Network Models. by Venkatanand Venkatachalapathy (‘Venkat’) ECE/ CS/ ME 539 Course Project

2. Genetic information flow Genes {DNA} RNA intermediate Protein GENE EXPRESSION (Gene expression refers to both transcription and translation.)  Genes (information molecules) – code for RNA & Proteins (functional molecules- properties of cell).  “Gene Expression Level” - amount of Prot./ RNA produced per gene. Expression varies dynamically with time depending on environment, stage of development of cell etc.  When expression level is “high” or “low” with respect to a reference condition (‘normal state’), GENE is said to be switched ‘ON’ or ‘OFF’. TranscriptionTranslation

3. Microarray experiments and data  Measures Gene Exp. level of 1000’s of genes in a single experiment.  For a single experiment, each gene has a data point expressed as a ratio of current state expression to reference state expression. Eg. Exp. level for Genes [A B C] = [ 3000/10 10/30 1/1] (Conventionally, these ratios are normalized on a log scale)  ‘N’ such experiments for M genes give rise to GENE EXPRESSION MATRIX ( M x N) G(i,j) = expression level of i th gene in j th experiment. (Collection of Gene expression row vectors)  Enormous Significance in Biotech. & Medicine! WHY? Genome projects completed, => KNOW GENETIC CODE, MUST FIND FUNCTION?

4. Project Problem & Methodology OBJECTIVE:  Classify “unknown” genes to functional classes based on: - Microarray gene expression data & Knowledge about function of “well known” genes.  A Graphical User Interface for the analysis. SOLUTION STRATEGY:  Functionally related genes have similar expression level! Two step: 1.For “Well known genes” - correlate their gene expression vector & functional class. This correlation can be encoded in a Neural Network! 2. Using this Neural Network, classify of unknown genes using its gene expression vector!

5. ANN Models & Program Features  Models chosen  MLP (used bp.m), SVM (linear kernel, polynomial kernel, radial basis kernel – svmdemo.m)  GUI Interface accepting comma limited gene expression data files (.csv).

6. Data Source: Stanford Microarray Database. Classification of 2467 genes into “TCA” Class and “Non-TCA” Class (Tested by 3- way cross validation) Brown et al used SVM Radial Basis : 99.5% MLP Results SVM results ARCHITECTURETRAIN C-RATE TEST C-RATE 80 – 15 – 199.3 % 96.5 % 80 – 15 – 5 –199.6%97% KERNEL ORDER TRAIN C-RATE TEST C-RATE Linear100%99.1% Poly – 2 And Poly –3 To be done Preliminary Results