MicroRNAs (miRNAs) are key regulators of cell-fate decisions in development and disease with a vast array of target interactions that can be investigated using computational approaches. For this study, we developed metaMIR, a combinatorial approach to identify miRNAs that co-regulate identified subsets of genes from a user-supplied list. We based metaMIR predictions on an improved dataset of human miRNA-target interactions, compiled using a machine-learning-based meta-analysis of established algorithms. Simultaneously, the inverse dataset of negative interactions not likely to occur was extracted to increase classifier performance, as measured using an expansive set of experimentally validated interactions from a variety of sources. In a second differential mode, candidate miRNAs are predicted by indicating genes to be targeted and others to be avoided to potentially increase specificity of results. As an example, we investigate the neural crest, a transient structure in vertebrate development where miRNAs play a pivotal role. Patterns of metaMIR-predicted miRNA regulation alone partially recapitulated functional relationships among genes, and separate differential analysis revealed miRNA candidates that would downregulate components implicated in cancer progression while not targeting tumour suppressors. Such an approach could aid in therapeutic application of miRNAs to reduce unintended effects. The utility is available at http://rna.informatik.uni-freiburg.de/metaMIR/.