1 / 6

DNA Microarray Data Analysis using Artificial Neural Network Models.

This project focuses on utilizing artificial neural network models for DNA microarray data analysis. Gene expression levels fluctuate dynamically based on environmental conditions and cell development stages. The research aims to classify unknown genes into functional classes using microarray gene expression data and information from well-known genes. The solution strategy involves correlating gene expression vectors and functional classes using a neural network. Selected models include Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) with various kernels. A user-friendly graphical interface accepts gene expression data files in CSV format and utilizes Stanford Microarray Database as a data source for classifying 2467 genes. Preliminary results show promising outcomes in gene classification.

cmclemore
Download Presentation

DNA Microarray Data Analysis using Artificial Neural Network Models.

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. 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’. Genetic information flow Transcription Translation

  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 ith gene in jth 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: • For “Well known genes” - correlate their gene expression vector & functional class. This correlation can be encoded in a Neural Network! • 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 Preliminary Results

More Related