DLEB

DLEB provides deep learning model design, recommendation, data pre-processing, and automatic generation of executable Python code to support analysis and modeling of biological data.

Key Features:

• Model Design and Code Generation: Provides model design capabilities and automatically generates executable Python code corresponding to designed deep learning models.
• Recommendation System: Recommends appropriate deep learning models based on specific learning tasks and data types.
• Data Pre-processing Tools: Provides tools for pre-processing input biological data to prepare datasets for model training and analysis.
• Template Models and Example Datasets: Includes template deep learning models and example biological datasets for training and experimentation.

Scientific Applications:

• Genomic analysis: Applies deep learning approaches to genomic data analysis.
• Protein structure prediction: Applies deep learning approaches to protein structure prediction.
• Disease modeling: Applies deep learning approaches to disease modeling.
• Drug discovery: Applies deep learning approaches to drug discovery.

Methodology:

Model design, automatic generation of executable Python code, recommendation of models based on learning tasks and data types, data pre-processing, and provision of template models and example biological datasets.