2026-09-19T00:25:48Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/332852022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Lucila Ohno-Machado.
Jackson, Jonathan Lee
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2006-07-13T15:12:29Z
2006-07-13T15:12:29Z
2005
2005
http://hdl.handle.net/1721.1/33285
62277185
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
Includes bibliographical references (leaves 75-78).
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.
by Jonathan Lee Jackson.
M.Eng.
78 leaves
4187872 bytes
4191157 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Extensible neural network software : applications in gene expression analysis
Thesis
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