TY - GEN
T1 - Classifications of neural dendritic and synaptic damage resulting from HIV-1-associated dementia
T2 - 36th Annual Hawaii International Conference on System Sciences, HICSS 2003
AU - Zheng, Jialin
AU - Erichsen, D.
AU - Williams, C.
AU - Peng, Hui
AU - Kou, Gang
AU - Shi, C.
AU - Shi, Yong
N1 - Publisher Copyright:
© 2003 IEEE.
PY - 2003
Y1 - 2003
N2 - 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 gpl20 (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.
AB - 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 gpl20 (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.
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U2 - 10.1109/HICSS.2003.1174806
DO - 10.1109/HICSS.2003.1174806
M3 - Conference contribution
AN - SCOPUS:84969556036
T3 - Proceedings of the 36th Annual Hawaii International Conference on System Sciences, HICSS 2003
SP - 8
EP - 15
BT - Proceedings of the 36th Annual Hawaii International Conference on System Sciences, HICSS 2003
A2 - Sprague, Ralph H.
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 January 2003 through 9 January 2003
ER -