High-throughput 'omic' methods reveal many aspects of cellular state and biological responses to perturbations. However, no single data type is capable of fully representing the cellular activity. Beyond the list of molecules from each data type, there is a necessity to consider multiple data jointly and reconstruct the relations between these molecules with personalized network-based approaches. We introduced the Omics Integrator software that integrates a variety of 'omic' data as input and identifies putative underlying molecular pathways. The approach applies an advanced network optimization algorithm to a network of thousands of molecular interactions to find high-confidence, interpretable subnetworks that best explain the data. Omics Integrator also provides an elegant framework to incorporate not only positive data, but also negative evidence. Incorporating negative evidence allows Omics Integrator to avoid unexpressed genes and avoid being biased toward highly-studied hub proteins, except when they are strongly implicated by the data. The tool has been applied in several examples, including data derived from Huntington's Disease and glioblastoma multiforme models, to identify novel pathways and nodes through which biological perturbations like disease cause changes in the cell.