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EnhPred: Deep Learning Model for Precise Prediction of Enhancer Positions
Jinseok Kim, Suyeon Wy, Jaebum Kim
http://doi.org/10.5626/JOK.2025.52.1.35
Enhancers are crucial regulatory elements that control gene expression in living organisms. Therefore, enhancer prediction is essential for a deeper understanding of gene regulation mechanisms. However, precise enhancer prediction is challenging due to their variable lengths and distant target genes. Existing artificial intelligence-based enhancer prediction methods often predict enhancers without identifying their boundaries accurately. In this study, we developed a new deep learning-based enhancer prediction method called EnhPred, which consisted of Convolutional Neural Networks (CNN) and bidirectional Gated Recurrent Units (GRU). To predict enhancer regions with a high resolution, we designed EnhPred to predict probabilities of enhancer presence within narrow segmented genomic regions. When evaluated with existing machine learning- and deep learning-based methods using data from three human cell lines, EnhPred demonstrated superior performances in terms of accuracy of enhancer prediction and resolution of enhancer boundaries.
A Time-Course Multi-Clustering Method for Single-Cell Trajectory Inference
http://doi.org/10.5626/JOK.2022.49.10.838
From time-series single-cell transcriptome data, gene expression information can be generated to observe the timing of significant cell differentiation changes while accounting for important biological phenomena in relation to experimental conditions. Due to recent surge of time-series single-cell transcriptome data, studies on various dynamic variation in cells such as cell cycle and cell differentiation have been actively conducted. Particularly, time series analysis at single-cell level for cell differentiation is advantageous for biological interpretation compared to a single time point as it is possible to observe changes in the time axis. In this paper, we proposed a multi-clustering method to infer cell trajectory by considering time information at the genetic-level of time-series single-cell transcriptome data. Analyses of gene expression data on the development of human neuron cell differentiation using this method showed similar results to biological results uncovered in a previous study.
Feature-based Gene Classification and Region Clustering using Gene Expression Grid Data in Mouse Hippocampal Region
Mi-Sun Kang, HyeRyun Kim, Sukchan Lee, Myoung-Hee Kim
Brain gene expression information is closely related to the structural and functional characteristics of the brain. Thus, extensive research has been carried out on the relationship between gene expression patterns and the brain’s structural organization. In this study, Principal Component Analysis was used to extract features of gene expression patterns, and genes were automatically classified by spatial distribution. Voxels were then clustered with classified specific region expressed genes. Finally, we visualized the clustering results for mouse hippocampal region gene expression with the Allen Brain Atlas. This experiment allowed us to classify the region-specific gene expression of the mouse hippocampal region and provided visualization of clustering results and a brain atlas in an integrated manner. This study has the potential to allow neuroscientists to search for experimental groups of genes more quickly and design an effective test according to the new form of data. It is also expected that it will enable the discovery of a more specific sub-region beyond the current known anatomical regions of the brain.
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