LipGene: Lipschitz Continuity Guided Adaptive Learning Rates for Fast Convergence on Microarray Expression Data Sets.

Area of Science:

• Computational biology
• Machine learning
• Deep learning

Background:

• Hyperparameter tuning, particularly learning rate optimization, is a significant bottleneck in training machine learning models, especially for large datasets.
• Efficient training of neural networks for complex biological tasks like gene expression inference is crucial.

Purpose of the Study:

• To introduce a novel adaptive learning rate paradigm, LipGene, for gene expression inference.
• To reduce the time and computational resources required for hyperparameter tuning in neural network training.
• To improve the speed of convergence and generalizability of models used in gene expression inference.

Main Methods:

• Application of a novel adaptive learning rate paradigm (LipGene) guided by the Lipschitz continuity of loss functions.
• Utilizing shallow neural networks for gene expression inference.
• Employing Mean Absolute Error and Quantile loss functions during training.
• Dynamically computing the adaptive learning rate for each epoch based on Lipschitz constants.

Main Results:

• The proposed LipGene approach significantly surpasses conventional learning rate choices in both speed of convergence and model generalizability.
• The adaptive learning rate requires no manual tuning, streamlining the training process.
• The method supports Parsimonious Computing, enabling the use of smaller networks with minimal impact on prediction accuracy.

Conclusions:

• LipGene offers an efficient and effective solution for learning rate selection in gene expression inference.
• The method reduces the need for extensive hyperparameter search, saving computational resources.
• This adaptive learning rate strategy enhances model performance and promotes efficient deep learning practices in bioinformatics.