Identifying Combinatorial Significance for Classification of Alzheimer's Disease Proteomics Expression with Logical Analysis of Data

Sunung Kim, Sangkyun Noh, Hong Seo Ryoo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper, we develop clinical Alzheimer's Disease pattern as a combination of protein expression quantity using logical analysis of data on ROSMAP brain samples [1]. As a result, 14 transcripts are selected as support markers and compose interpretable patterns. These patterns show far statistical significance than any individual transcripts. In addition, patterns also indicate novel combinations of transcripts that have a little relation on the STRING network. Our result demonstrates a possible novel approach on analyzing interconnected transcripts, expecting a full pathology of the Alzheimer's Disease.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
EditorsYufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1661-1663
Number of pages3
ISBN (Electronic)9781665401265
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, United States
Duration: 2021 Dec 92021 Dec 12

Publication series

NameProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Conference

Conference2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Country/TerritoryUnited States
CityVirtual, Online
Period21/12/921/12/12

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Alzheimer's Disease
  • Feature Selection
  • Logical Analysis of Data
  • Proteomics
  • Set Covering

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Biomedical Engineering
  • Health Informatics
  • Information Systems and Management

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