iLearn

iLearn provides a Python-based toolkit to extract descriptors and engineer features from DNA, RNA, and protein sequences and to build predictive models for sequence-function characterization.

Key Features:

• Feature Engineering: Implements 16 algorithms for clustering, selection, normalization, and dimensionality reduction for sequence-derived features.
• Machine Learning Integration: Incorporates five commonly used machine-learning algorithms and supports ensemble learning for predictive model construction.
• Descriptor Support and Output Formats: Provides diverse descriptors for DNA, RNA, and proteins and exports features in four feature output formats.
• Visualization: Includes results visualization capabilities for interpreting data patterns and model outcomes.

Scientific Applications:

• High-throughput sequence characterization: Supports large-scale feature extraction and analysis of DNA, RNA, and protein sequences.
• Predictive modeling: Enables construction of models linking sequence-derived features to functional or structural outcomes using integrated machine-learning methods.

Methodology:

Descriptor calculation for DNA, RNA, and protein sequences; feature engineering using 16 algorithms for clustering, selection, normalization, and dimensionality reduction; model building with five machine-learning algorithms and ensemble learning; export in four feature output formats and results visualization.