Detection of Anomalous Diffusion with Deep Residual Networks.

Area of Science:

• Biophysics
• Computational Biology
• Machine Learning

Background:

• Understanding molecular diffusion in living cells is key to deciphering cellular mechanisms.
• Current methods for diffusion analysis can be computationally intensive and may lack accuracy.

Purpose of the Study:

• To develop an efficient and accurate method for classifying molecular diffusion types using deep learning.
• To adapt existing deep residual network (ResNet) architectures for trajectory classification.

Main Methods:

• Utilized deep residual networks (ResNets), originally for image classification, to analyze molecular trajectories.
• Performed numerical experiments to optimize the ResNet architecture for diffusion mode detection.
• Developed a reduced-size model with fewer parameters compared to the initial ResNet.

Main Results:

• Achieved higher accuracy in classifying diffusion modes compared to the baseline ResNet.
• The optimized model demonstrated significantly reduced training time due to its smaller size.
• The resulting network exhibited improved generalization to unseen data and reduced overfitting.

Conclusions:

• Deep residual networks offer a powerful and efficient approach for classifying molecular diffusion patterns.
• Optimized, smaller ResNet models provide a practical solution for analyzing cellular dynamics.
• This method enhances our ability to infer molecular driving forces and cellular characteristics.