Log in
|
Help
|
English

English

Engish-Pirate

한국어

Search

Advanced Search

Find results with:

Add another field

Search by date

to

Searching collections:

CCSU Theses and Dissertations

Add or remove collections

Home CCSU Theses & Dissertations Knowledge Discovery in Microarray Data

Reference URL

Share

Add tags

Comment

Rate

To link to this object, paste this link in email, IM or document

To embed this object, paste this HTML in website

Knowledge Discovery in Microarray Data

View Description

Download

small (250x250 max)

medium (500x500 max)

large ( > 500x500)

Full Resolution

Print

you wish to report:

Your comment:

Your Name:

Submit

Cancel

...

Back to top

Powered by CONTENTdm®

Select the collections to add or remove from your search

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

T

U

V

W

X

Y

Z

Select All Collections

C

CCSU Student Publications

CCSU Theses and Dissertations

G

GLBTQ Archives

M

Modern Language Oral Histories

O

O'Neill Archives Oral Histories

P

Polish American Pamphlets

T

Treasures from the Special Collections

V

Veterans History Project

OK

Cancel

Identifier	Thesis 1760
Author	Islam, Rafiqul, 1973-
Title	Knowledge Discovery in Microarray Data
Publisher	Central Connecticut State University
Date	2004
Resource Type	Master's Thesis
Notes	During the last decade, new technologies created a deluge of potential drug targets. Sifting through thousands of potential drug targets is a major industry bottleneck. Pharmaceutical companies can save billions of dollars by identifying most promising drug candidates at the earlier stages of pre-clinical development, and eliminating the rest. Here we present a method of prioritizing potential drug targets based on their gene expression 'signature'. Primary human pre-cursor neuronal cells were treated with three classes (antidepressant, antipsychotic, opiod receptor agonist) of psychoactive drugs for 24 hours. Microarray technology was used to capture expression of ~11, 000 genes induced by these three categories of drugs. Clementine data mining software was used to build neural network and decision tree models that can categorize these three classes based on their gene expression profile. Then we tried to rank the predictions based on the confidence value generated by each model. These models can be used to classify and prioritize a portfolio of future novel drugs based on the gene expression induced by them. In addition, a small set of genes relevant to all three target disease classes were identified based on neural network model sensitivity analysis and C5.0 ruleset. Biological experiments can be designed to better understand these genes, their relationship to the target classes and the mechanism of action for each drug class. This paper is organized in two parts. The first part describes the biological techniques behind microarray technology and the second part describes how data mining techniques applied to microarray data. The data analysis part of this report is structured in CRISP-DM format, which is the cross-industry standard process for data mining.
Subject	DNA microarrays Data mining
Department	Department of Mathematical Sciences
Advisor	Larose, Daniel T
Type	Text
Digital Format	application/pdf
Language	eng
OCLC number	713734356
Rating

Find results with:

Searching collections:

Home CCSU Theses & Dissertations Knowledge Discovery in Microarray Data

Knowledge Discovery in Microarray Data

Description

Tags

Comments