Mining influential genes based on deep learning

• Published in: BMC bioinformatics Vol. 22; no. 1; p. 27
• Main Authors: Kong, Lingpeng, Chen, Yuanyuan, Xu, Fengjiao, Xu, Mingmin, Li, Zutan, Fang, Jingya, Zhang, Liangyun, Pian, Cong
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 22.01.2021; BioMed Central; BMC
• Subjects: AutoEncoder; Binding sites; Cluster analysis; Computational biology; Computer applications; Correlation coefficient; Correlation coefficients; Data mining; Deep Learning; DeepLIFT; Gene expression; Gene Expression Profiling; Genes; Genetic research; Genome; Genomes; Genomics; Landmark genes; Landmarks; Lung cancer; Machine learning; Methodology; Methods; Neural networks; Performance evaluation; Perturbation; Physiology; Principal components analysis; Transcriptome; China; Deep learning; DeepLIFT; Landmark genes; AutoEncoder
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-021-03972-5

Currently, large-scale gene expression profiling has been successfully applied to the discovery of functional connections among diseases, genetic perturbation, and drug action. To address the cost of an ever-expanding gene expression profile, a new, low-cost, high-throughput reduced representation expression profiling method called L1000 was proposed, with which one million profiles were produced. Although a set of ~ 1000 carefully chosen landmark genes that can capture ~ 80% of information from the whole genome has been identified for use in L1000, the robustness of using these landmark genes to infer target genes is not satisfactory. Therefore, more efficient computational methods are still needed to deep mine the influential genes in the genome. Here, we propose a computational framework based on deep learning to mine a subset of genes that can cover more genomic information. Specifically, an AutoEncoder framework is first constructed to learn the non-linear relationship between genes, and then DeepLIFT is applied to calculate gene importance scores. Using this data-driven approach, we have re-obtained a landmark gene set. The result shows that our landmark genes can predict target genes more accurately and robustly than that of L1000 based on two metrics [mean absolute error (MAE) and Pearson correlation coefficient (PCC)]. This reveals that the landmark genes detected by our method contain more genomic information. We believe that our proposed framework is very suitable for the analysis of biological big data to reveal the mysteries of life. Furthermore, the landmark genes inferred from this study can be used for the explosive amplification of gene expression profiles to facilitate research into functional connections.