Extensible neural network software : applications in gene expression analysis

Artificial Neural Networks have been increasingly utilized in the life sciences for analysis of large data sets. High-throughput technologies, such as gene expression microarrays, have challenged traditional statistical learning algorithms given their high dimensionality. This thesis describes GAINN, a neural network software package I created. GAINN was designed to be an extensible tool for both researches and students to use in neural network explorations. Several algorithms and features were implemented and tested on classification of various gene expression array data sets. The code design and user interface were implemented in such a manner that new algorithms and features would be trivial to incorporate into GAINN.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2005

Author dc:creator

• Jackson, Jonathan Lee

Advisor dc:contributor.advisor

• Lucila Ohno-Machado.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/33285

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/33285