Abstract
The identification of disease-related genes and disease mechanisms is an important research goal; many studies have approached this problem by analysing genetic networks based on gene expression profiles and interaction datasets. To construct a gene network, correlations or associations among pairs of genes must be obtained. However, when gene expression data are heterogeneous with high levels of noise for samples assigned to the same condition, it is difficult to accurately determine whether a gene pair represents a significant gene–gene interaction (GGI). In order to solve this problem, we proposed a random forest-based method to classify significant GGIs from gene expression data. To train the model, we defined novel feature sets and utilised various high-confidence interactome datasets to deduce the correct answer set from known disease-specific genes. Using Alzheimer’s disease data, the proposed method showed remarkable accuracy, and the GGIs established in the analysis can be used to build a meaningful genetic network that can explain the mechanisms underlying Alzheimer’s disease.
| Original language | English |
|---|---|
| Article number | e0201056 |
| Journal | PloS one |
| Volume | 13 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 2018 Jul |
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All Science Journal Classification (ASJC) codes
- Biochemistry, Genetics and Molecular Biology(all)
- Agricultural and Biological Sciences(all)
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Machine learning-based identification of genetic interactions from heterogeneous gene expression profiles. / Park, Chihyun; Kim, Jung Rim; Kim, Jeongwoo; Park, Sanghyun.
In: PloS one, Vol. 13, No. 7, e0201056, 07.2018.Research output: Contribution to journal › Article
TY - JOUR
T1 - Machine learning-based identification of genetic interactions from heterogeneous gene expression profiles
AU - Park, Chihyun
AU - Kim, Jung Rim
AU - Kim, Jeongwoo
AU - Park, Sanghyun
PY - 2018/7
Y1 - 2018/7
N2 - The identification of disease-related genes and disease mechanisms is an important research goal; many studies have approached this problem by analysing genetic networks based on gene expression profiles and interaction datasets. To construct a gene network, correlations or associations among pairs of genes must be obtained. However, when gene expression data are heterogeneous with high levels of noise for samples assigned to the same condition, it is difficult to accurately determine whether a gene pair represents a significant gene–gene interaction (GGI). In order to solve this problem, we proposed a random forest-based method to classify significant GGIs from gene expression data. To train the model, we defined novel feature sets and utilised various high-confidence interactome datasets to deduce the correct answer set from known disease-specific genes. Using Alzheimer’s disease data, the proposed method showed remarkable accuracy, and the GGIs established in the analysis can be used to build a meaningful genetic network that can explain the mechanisms underlying Alzheimer’s disease.
AB - The identification of disease-related genes and disease mechanisms is an important research goal; many studies have approached this problem by analysing genetic networks based on gene expression profiles and interaction datasets. To construct a gene network, correlations or associations among pairs of genes must be obtained. However, when gene expression data are heterogeneous with high levels of noise for samples assigned to the same condition, it is difficult to accurately determine whether a gene pair represents a significant gene–gene interaction (GGI). In order to solve this problem, we proposed a random forest-based method to classify significant GGIs from gene expression data. To train the model, we defined novel feature sets and utilised various high-confidence interactome datasets to deduce the correct answer set from known disease-specific genes. Using Alzheimer’s disease data, the proposed method showed remarkable accuracy, and the GGIs established in the analysis can be used to build a meaningful genetic network that can explain the mechanisms underlying Alzheimer’s disease.
UR - http://www.scopus.com/inward/record.url?scp=85050689713&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050689713&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0201056
DO - 10.1371/journal.pone.0201056
M3 - Article
C2 - 30048494
AN - SCOPUS:85050689713
VL - 13
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 7
M1 - e0201056
ER -