Adapting Random Forests to Predict Obesity-Associated Gene Expression

Watts, Jeremy Lee; Allen, Elexis; Mitoubsi, Ahmad; Khojandi, Anahita; Eales, James; Jalalinajafabadi, Farideh; Papamarkou, Theodore

doi:10.1109/embc48229.2022.9871234

Cited by 2 publications

References 15 publications

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Towards Faster Gene Expression Prediction via Dimensionality Reduction and Feature Selection

Watts,

Allen,

Mitoubsi

et al. 2023

2023 45th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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The majority of genes have a genetic component to their expression. Elastic nets have been shown effective at predicting tissue-specific, individual-level gene expression from genotype data. We apply principal component analysis (PCA), linkage disequilibrium pruning, or the combination of the two to reduce, or generate, a lower-dimensional representation of the genetic variants used as inputs to the elastic net models for the prediction of gene expression. Our results show that, in general, elastic nets attain their best performance when all genetic variants are included as inputs; however, a relatively low number of principal components can effectively summarize the majority of genetic variation while reducing the overall computation time. Specifically, 100 principal components reduce the computational time of the models by over 80% with only an 8% loss in R 2 . Finally, linkage disequilibrium pruning does not effectively reduce the genetic variants for predicting gene expression. As predictive models are commonly made for over 27, 000 genes for more than 50 tissues, PCA may provide an effective method for reducing the computational burden of gene expression analysis.

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Towards Faster Gene Expression Prediction via Dimensionality Reduction and Feature Selection

Watts,

Allen,

Mitoubsi

et al. 2023

2023 45th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Evolutionary Mechanism Based Conserved Gene Expression Biclustering Module Analysis for Breast Cancer Genomics

Yuan,

Li,

Han

et al. 2024

Biomedicines

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The identification of significant gene biclusters with particular expression patterns and the elucidation of functionally related genes within gene expression data has become a critical concern due to the vast amount of gene expression data generated by RNA sequencing technology. In this paper, a Conserved Gene Expression Module based on Genetic Algorithm (CGEMGA) is proposed. Breast cancer data from the TCGA database is used as the subject of this study. The p-values from Fisher’s exact test are used as evaluation metrics to demonstrate the significance of different algorithms, including the Cheng and Church algorithm, CGEM algorithm, etc. In addition, the F-test is used to investigate the difference between our method and the CGEM algorithm. The computational cost of the different algorithms is further investigated by calculating the running time of each algorithm. Finally, the established driver genes and cancer-related pathways are used to validate the process. The results of 10 independent runs demonstrate that CGEMGA has a superior average p-value of 1.54 × 10−4 ± 3.06 × 10−5 compared to all other algorithms. Furthermore, our approach exhibits consistent performance across all methods. The F-test yields a p-value of 0.039, indicating a significant difference between our approach and the CGEM. Computational cost statistics also demonstrate that our approach has a significantly shorter average runtime of 5.22 × 100 ± 1.65 × 10−1 s compared to the other algorithms. Enrichment analysis indicates that the genes in our approach are significantly enriched for driver genes. Our algorithm is fast and robust, efficiently extracting co-expressed genes and associated co-expression condition biclusters from RNA-seq data.

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Adapting Random Forests to Predict Obesity-Associated Gene Expression

Cited by 2 publications

References 15 publications

Towards Faster Gene Expression Prediction via Dimensionality Reduction and Feature Selection

Towards Faster Gene Expression Prediction via Dimensionality Reduction and Feature Selection

Evolutionary Mechanism Based Conserved Gene Expression Biclustering Module Analysis for Breast Cancer Genomics

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