BioBayesNet

BioBayesNet models and classifies biological sequence data using Bayesian networks to analyze sequence-derived features and their probabilistic dependencies.

Key Features:

• Flexible Sequence Data Input: Accepts annotated FASTA sequences or precomputed feature vectors for Bayesian network modeling.
• Automated Feature Extraction and Selection: Extracts sequence and structural features from FASTA inputs and performs automatic feature selection to identify discriminative variables.
• Bayesian Network Construction: Learns probabilistic network structures representing dependencies among sequence-derived features.
• Network Evaluation and Visualization: Provides quality measures for learned networks and individual features and generates graphical representations of network structures.
• Sequence Classification: Applies trained Bayesian networks to classify new biological sequence datasets.

Scientific Applications:

• Transcription Factor Binding Site Prediction: Identifies regulatory binding sites within promoter sequences using probabilistic feature modeling.
• Biological Sequence Classification: Classifies sequence datasets based on probabilistic dependencies among extracted sequence features.
• Regulatory Sequence Analysis: Investigates sequence patterns and dependencies relevant to transcriptional regulation.

Methodology:

BioBayesNet extracts sequence and structural features from annotated FASTA sequences, applies automatic feature selection, constructs Bayesian network models representing feature dependencies, and evaluates predictive performance through probabilistic classification and cross-validation.