Information-theoretic analysis applied to biological data integration and network analysis, measuring entropy across DNA, RNA, single-cell, multi-omics, and ML pipeline outputs.
| File |
Purpose |
integration.py |
Domain-specific entropy analysis for DNA, RNA, single-cell, multi-omics, ML |
networks.py |
Network entropy, information flow, community detection, centrality measures |
| Function |
Description |
dna_integration() |
Compute entropy and information content of DNA sequence data |
rna_integration() |
Entropy analysis of RNA expression matrices |
singlecell_integration() |
Information metrics for single-cell expression profiles |
multiomics_integration() |
Cross-omics mutual information and integration scores |
ml_integration() |
Information-theoretic evaluation of ML feature sets |
network_entropy() |
Shannon or von Neumann entropy of a network graph |
information_flow() |
Random-walk or diffusion-based information flow between nodes |
information_community_detection() |
Community detection via infomap or map equation |
network_information_centrality() |
Entropy-based node centrality ranking |
network_motif_information() |
Information content of network motif patterns |
information_graph_distance() |
Entropy-based distance between two graphs |
from metainformant.information.integration.integration import dna_integration
from metainformant.information.integration.networks import network_entropy
results = dna_integration(sequences, method="entropy")
h = network_entropy(graph, method="shannon")