This project implements a powerful algorithm for identifying biclusters and co-activation patterns within biological datasets. CCA can help uncover hidden relationships and functional insights in complex biological data.
Biclustering or simultaneous clustering of both genes and conditions as a new paradigm was introduced by Cheng and Church's Algorithm (CCA). The concept of bicluster refers to a subset of genes and a subset of conditions with a high similarity score, which measures the coherence of the genes and conditions in the bicluster. It also returns the list of biclusters for the given data set.
- Biclustering: Discover patterns of co-activated genes across subsets of conditions.
- Co-Activation Analysis: Identify relationships and interactions among genes.
- Evaluation Metrics: Evaluate the algorithm's performance using precision, recall, F1-score, accuracy, and purity metrics.
- Cross-Validation: Utilize cross-validation for robust evaluation.
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Clone the repository:
git clone [email protected]:ShokofehVS/REMBic-CCA.git cd REMBic-CCA python CCA_modified.py