The Community for Technology Leaders
RSS Icon
Subscribe
Big Island, HI, USA
Jan. 6, 2003 to Jan. 9, 2003
ISBN: 0-7695-1874-5
pp: 276b
Jialin Zheng , University of Nebraska Medical Center
David Erichsen , University of Nebraska Medical Center
Clancy Williams , University of Nebraska Medical Center
Hui Peng , University of Nebraska Medical Center
Gang Kou , University of Nebraska at Omaha
Chris Shi , Millard North High School
Yong Shi , University of Nebraska at Omaha
ABSTRACT
The ability to identify neuronal damage resulting from HIV-1- associated dementia (HAD) is crucial for designing specific therapies for the treatment of HAD. This paper proposes a two-class model of multiple criteria linear programming (MCLP) to classify the HAD neural dendritic and synaptic damages. The damages are measured by a number of quantitative variables such as the change of neuritis, arbors, branch nodes, and cell bodies. Given certain classes, including brain derived neurotrophic factor (BDNF) treatment, non-treatment, glutamate treatment, and gp120 (HIV-1 envelop protein) from laboratory cell observations, we use the two-class MCLP model to learn the data patterns between two classes so that we can discover the knowledge about the HAD neural dendritic and synaptic damages under different treatments. This knowledge can be applied to design and study specific therapies for the prevention or reversal of the neuronal demise associated with HAD. In the paper, we first describe the technical background of the two-class models that includes concepts, modeling and computer algorithms. Then, we conduct a series of learning experimental tests on the data of laboratory cell observations. We also illustrate some significance and implications of learning results in the HAD research.
INDEX TERMS
null
CITATION
Jialin Zheng, David Erichsen, Clancy Williams, Hui Peng, Gang Kou, Chris Shi, Yong Shi, "Classifications of Neural Dendritic and Synaptic Damage Resulting from HIV-1-associated Dementia: A Multiple Criteria Linear Programming Approach", HICSS, 2003, 36th Hawaii International Conference on Systems Sciences, 36th Hawaii International Conference on Systems Sciences 2003, pp. 276b, doi:10.1109/HICSS.2003.1174806
22 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool