Currently, our network modeling only considers undirected edges among genes. In future we would like to generalise the approach to identify directed and indirect interactions among genes. In network modeling, a combination of both direct and indirect relationships among gene-pairs was found to provide better insights into biological systems in our previous studies [68]. The rationale for combining these two types of gene-gene relationships in signaling networks is that EGFR/ErbB and IGF1R can both cross-talk (EGFR/IGF1R heterodimerization) directly at the receptor level, and indirectly mediated by GPCR signaling, as reported by Van der Veeken et al. [62]. Other high-throughput datasets such as miRNA expression data, copy number aberration data, and methylation data could also be incorporated into our framework to obtain a better understanding of gene dependencies. Note that our methodology exploits a fully data-driven approach for finding putative drug-resistant cross-talks, without incorporating other prior information regarding gene-gene relationships, such as Protein-Protein Interactions. Hence, although our data-driven approach may inherently yield some false-positive predictions, it may also provide the possibilities of finding novel cross-talks contributing to drug- resistance.