1. A computer implemented method for construction of a gene interaction network, comprising: for each pair of genes in a first set of gene expression data, computing a first gene interaction measure between a pair of genes based upon said gene expression data, the computation comprising: clustering genes from the first set based on the expression data, the clustering comprising: separating gene expression values of the set of genes, obtained under an experimental condition, into two clusters based on differential expression with respect to a control in a corresponding environmental condition of associated experiments; and selecting genes with known annotation in each cluster as training data, and further clustering the annotated genes using perceptual clustering; capturing a shape of the resulting clusters using an interpolation function, a kernel density function, and an associated bandwidth; continuously formulating the kernel density function to provide a dense shape description of the clusters; and storing the kernel density function as a representation of the clusters; generating a multi-dimensional image stack via RNAi screening for each pair of genes in a second set having image data from the stack depicting groups of cells or intra-cellular structures associated with the second set of genes; computing a gene relationship measure using a morphological similarity measure between corresponding groups of cells, wherein the morphological similarity measure is a value computed by comparison of morphological features on paired cellular images; and constructing an initial graph based on the gene interaction and gene relationship measures including: combining the first set of gene expression data with the image data of the second set, each node in the graph associated with one of the genes belonging to one of the first and second sets and drawing edges between nodes, the edges based on membership of gene clusters or morphological distance for corresponding gene knockdown image stacks; and constructing a final graph by expanding connectivity among the nodes in the graph using network propagation, the expanded connectivity forming the gene interaction network.