Gene expression clock: an unsupervised deep learning approach for predicting circadian rhythmicity from whole genome expression

• Published in: Neural computing & applications Vol. 36; no. 33; pp. 20653 - 20670
• Main Authors: Ansary Ogholbake, Aram, Cheng, Qiang
• Format: Journal Article
• Language: English
• Published: London Springer London 01.11.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Circadian rhythm; Circadian rhythms; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Datasets; Deep learning; Gene expression; Genes; Greedy algorithms; Image Processing and Computer Vision; Machine learning; Original Article; Oscillations; Phases; Predictive control; Probability and Statistics in Computer Science; Unsupervised deep learning; Greedy layer-wise pre-training; Whole genome gene expression; Circadian sample phase; Bioinformatics
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-024-10316-w

Circadian rhythms are driven by an internal molecular clock which controls physiological and behavioral processes. Disruptions in these rhythms have been associated with health issues. Therefore, studying circadian rhythms is crucial for understanding physiology, behavior, and pathophysiology. However, it is challenging to study circadian rhythms over gene expression data, due to a scarcity of time labels. In this paper, we propose a novel approach to predict the phases of un-timed samples based on a deep neural network (DNN) architecture. This approach addresses two challenges: (1) prediction of sample phases and reliable identification of cyclic genes from high-dimensional expression data without relying on conserved circadian genes and (2) handling small sample-sized datasets. Our algorithm begins with initial gene screening to select candidate cyclic genes using a Minimum Distortion Embedding framework. This stage is then followed by greedy layer-wise pre-training of our DNN. Pre-training accomplishes two critical objectives: First, it initializes the hidden layers of our DNN model, enabling them to effectively capture features from the gene profiles with limited samples. Second, it provides suitable initial values for essential aspects of gene periodic oscillations. Subsequently, we fine-tune the pre-trained network to achieve precise sample phase predictions. Extensive experiments on both animal and human datasets show accurate and robust prediction of both sample phases and cyclic genes. Moreover, based on an Alzheimer’s disease (AD) dataset, we identify a set of hub genes that show significant oscillations in cognitively normal subjects but had disruptions in AD, as well as their potential therapeutic targets.