Classification of HIV-1-mediated neuronal dendritic and synaptic damage using multiple criteria linear programming

Jialin Zheng, Wei Zhuang, Nian Yan, Gang Kou, Hui Peng, Clancy McNally, David Erichsen, Abby Cheloha, Shelley Herek, Chris Shi, Yong Shi

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

The ability to identify neuronal damage in the dendritic arbor during HIV-1-associated dementia (HAD) is crucial for designing specific therapies for the treatment of HAD. To study this process, we utilized a computer-based image analysis method to quantitatively assess HIV-1 viral protein gp120 and glutamate-mediated individual neuronal damage in cultured cortical neurons. Changes in the number of neurites, arbors, branch nodes, cell body area, and average arbor lengths were determined and a database was formed (http://dm.ist.unomaha. edu/database.htm). We further proposed a two-class model of multiple criteria linear programming (MCLP) to classify such HIV-1-mediated neuronal dendritic and synaptic damages. Given certain classes, including treatments with brain-derived neurotrophic factor (BDNF), glutamate, gp120 or non-treatment controls from our in vitro experimental systems, we used the two-class MCLP model to determine the data patterns between classes in order to gain insight about neuronal dendritic damages. This knowledge can be applied in principle to the design and study of specific therapies for the prevention or reversal of neuronal damage associated with HAD. Finally, the MCLP method was compared with a well-known artificial neural network algorithm to test for the relative potential of different data mining applications in HAD research.

Original languageEnglish (US)
Pages (from-to)303-326
Number of pages24
JournalNeuroinformatics
Volume2
Issue number3
DOIs
StatePublished - 2004

Keywords

  • Classification
  • Data mining
  • Dendritic morphology
  • HIV-1-associated dementia
  • Multi-criteria linear programming
  • Neuronal damage

ASJC Scopus subject areas

  • Software
  • Neuroscience(all)
  • Information Systems

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