An Effective Approach for Predicting P-value using High-dimensional SNPs data with Small Sample Size

Jiayu Wang, Fengtao Nan, Po Yang, Yun Yang, Jun Qi

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

Alzheimer's disease has seriously affected the normal life of elder people. As an important biomarker of Alzheimer's disease, Single Nucleotide Polymorphisms (SNPs) is highly important for exploring the pathogenesis of Alzheimer's disease. Genome-Wide Association Studies are widely used for extracting important SNPs from extensive genetic data. It can provide a P-value to measure the significance of the association between SNPs and Alzheimer's disease the most significantly correlated SNPs are selected as features to determine the patient's status. However, these approaches suffer from some key limitations: (1) high dimensionality of genetic data requires feature selection or dimension reduction before it can be used. (2) genome-wide association studies require sufficiently genetic samples for ensuring the production of reliable P-values. In order to overcome above limits, we propose an automated framework for modeling P-values for high-dimensional and small-sample SNPs feature selection. Specifically, we transform the feature extraction problem into a regression problem and use a neural network to fit the change of P-value on a small number of samples. Secondly, we propose a new loss function to better measure the quality of model predictions. Extensive experimental results show that the proposed method outperforms Genome-Wide Association Studies methods on ADNI-1 dataset.

Original languageEnglish
Title of host publicationProceedings - 2021 20th International Conference on Ubiquitous Computing and Communications, 2021 20th International Conference on Computer and Information Technology, 2021 4th International Conference on Data Science and Computational Intelligence and 2021 11th International Conference on Smart Computing, Networking, and Services, IUCC/CIT/DSCI/SmartCNS 2021
EditorsJia Hu, Fei Hao, Haozhe Wang, Miaoqiong Wang, Xu Zhang, Zhiwei Zhao, Zi Wang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages339-344
Number of pages6
ISBN (Electronic)9781665466677
DOIs
Publication statusPublished - 2021
Event20th International Conference on Ubiquitous Computing and Communications, 20th International Conference on Computer and Information Technology, 4th International Conference on Data Science and Computational Intelligence and 11th International Conference on Smart Computing, Networking, and Services, IUCC/CIT/DSCI/SmartCNS 2021 - London, United Kingdom
Duration: 20 Dec 202122 Dec 2021

Publication series

NameProceedings - 2021 20th International Conference on Ubiquitous Computing and Communications, 2021 20th International Conference on Computer and Information Technology, 2021 4th International Conference on Data Science and Computational Intelligence and 2021 11th International Conference on Smart Computing, Networking, and Services, IUCC/CIT/DSCI/SmartCNS 2021

Conference

Conference20th International Conference on Ubiquitous Computing and Communications, 20th International Conference on Computer and Information Technology, 4th International Conference on Data Science and Computational Intelligence and 11th International Conference on Smart Computing, Networking, and Services, IUCC/CIT/DSCI/SmartCNS 2021
Country/TerritoryUnited Kingdom
CityLondon
Period20/12/2122/12/21

Keywords

  • Alzheimer's disease
  • feature selection
  • genetic
  • p-value
  • single nucleotide polymorphisms
  • small samples

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