Objectives: Analysis of multi-molecular interactions and detection of combinatorial transcriptomic signatures are
emerging as important research topics in disease analytics. Currently, a combination of gene and miRNA expression
profiling in bioinformatic analysis enables us to comprehensively detect molecular changes in cancer and thereafter
to identify integrated signatures and pathways that exist in the miRNA and gene interaction networks. Although many
methodologies and applications have been suggested in recent literature, efficient techniques that can integrate the
complex gene as well as miRNA expression profiles, and identify the most relevant signatures are required.
Methods: In this article, we presented a new framework of multi-molecular data integration to identify combinatorial
transcriptomic signatures through the strategy of unsupervised learning and target detection. Later, we evaluated their
utility in survival analysis through a multi-variate Cox regression study. We used a cervical cancer data repository to
conduct our experiment. To construct the miRNA-mRNA interaction network, we selected the downregulated mRNAs
that were negatively correlated with the upregulated miRNAs. Thereafter, we identified dense modules by using an
unsupervised learning technique. The silhouette index value was computed for each cluster.
Results: By considering the network centrality of each molecule belonging to each cluster we identified top 3 combined signatures We also highlighted cluster-2 (hsa-mir-944, CFTR, GABRB2, HNF4G, TAC1, and C7orf57) for its high
cohesiveness and contained a combined signature. We then applied three well-known classifiers (viz., SVM, KNN, and
random forest) using 10-fold cross-validation, and obtained a high AUC score for cluster-2. Finally, we conducted a
survival study with each molecule of the same cluster.
Conclusion: Finally, we conducted a survival study with each molecule of the same cluster. Our proposed combined
signature detection strategy can determine the signature(s) for any microarray or RNA-Seq profile. The code is available