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Updated: Sep 11, 2026
Analysis of Cell Cycle Position in Mammalian Cells
Published on: January 21, 2012
Identification of Cell Cycle-Regulated Genes by Convolutional Neural Network
Chenglin Liu1, Peng Cui1, Tao Huang2
1School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Rd., Minhang, Shanghai 200240. China.
Background:
The cell cycle-regulated genes express periodically with the cell cycle stages, and the identification and study of these genes can provide a deep understanding of the cell cycle process. Large false positives and low overlaps are big problems in cell cycle-regulated gene detection.
Methods:
Here, a computational framework called DLGene was proposed for cell cycle-regulated gene detection. It is based on the convolutional neural network, a deep learning algorithm representing raw form of data pattern without assumption of their distribution. First, the expression data was transformed to categorical state data to denote the changing state of gene expression, and four different expression patterns were revealed for the reported cell cycle-regulated genes. Then, DLGene was applied to discriminate the non-cell cycle gene and the four subtypes of cell cycle genes. Its performances were compared with six traditional machine learning methods. At last, the biological functions of representative cell cycle genes for each subtype are analyzed.
Results:
Our method showed better and more balanced performance of sensitivity and specificity comparing to other machine learning algorithms. The cell cycle genes had very different expression pattern with non-cell cycle genes and among the cell-cycle genes, there were four subtypes. Our method not only detects the cell cycle genes, but also describes its expression pattern, such as when its highest expression level is reached and how it changes with time. For each type, we analyzed the biological functions of the representative genes and such results provided novel insight to the cell cycle mechanisms.
Insights
DLGene, a deep learning framework, accurately identifies cell cycle-regulated genes and their expression patterns, overcoming limitations of traditional methods for a deeper understanding of cell cycle mechanisms.
Area of Science:
- Genomics
- Computational Biology
- Molecular Biology
Background:
- Cell cycle-regulated genes exhibit periodic expression, crucial for understanding cell cycle processes.
- Challenges in cell cycle-regulated gene detection include high false positives and low overlap between methods.
- Accurate identification of these genes is vital for advancing cell cycle research.
Purpose of the Study:
- To introduce DLGene, a novel computational framework for enhanced cell cycle-regulated gene detection.
- To address limitations of existing methods in identifying cell cycle-regulated genes and their expression patterns.
- To analyze the biological functions of identified cell cycle gene subtypes.
Main Methods:
- Developed DLGene, a deep learning framework utilizing convolutional neural networks.
- Transformed gene expression data into categorical states to reveal distinct expression patterns.
- Compared DLGene's performance against six traditional machine learning algorithms.
Main Results:
- DLGene demonstrated superior and balanced sensitivity and specificity compared to other machine learning methods.
- Identified four distinct subtypes of cell cycle-regulated genes based on their expression patterns.
- Provided novel insights into cell cycle mechanisms through functional analysis of representative genes.
Conclusions:
- DLGene offers a robust approach for accurate cell cycle-regulated gene detection and expression pattern analysis.
- The framework successfully distinguishes cell cycle genes and their subtypes, improving upon existing methods.
- Functional analysis of identified gene subtypes offers new perspectives on cell cycle regulation.
