1. A computer program product for constructing a gene interaction network, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code executable by a processor to: for each pair of genes in a first set of gene expression data, compute a first gene interaction measure between a pair of genes, based upon the gene expression data, the computation comprising program code to: cluster genes from the first set based on the expression data, the clustering comprising program code to: separate 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 select genes with known annotation in each cluster as training data, and further cluster the annotated genes using perceptual clustering; capture a shape of the resulting clusters using an interpolation function, a kernel density function, and an associated bandwidth; and continuously formulate the kernel density function to provide a dense shape description of the clusters, and store the kernel density function as a representation of the clusters in the computer readable storage medium; generate a multi-dimensional image stack via RNAi screening for each pair of genes in a second set having image data depicting groups of cells or intra-cellular structures associated with the second set of genes; compute 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 the morphological features on paired cellular images; and activate a construction manager in a remote server, the activated construction manager programmed to: construct an initial graph based on the gene interaction and the gene relationship measures by 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 draw edges between nodes, the edges based on membership of gene clusters or morphological distance for a corresponding gene knockdown image stack; construct a final graph by expanding connectivity among the nodes in the graph using network propagation, the expanded connectivity forming the gene interaction network; and output the formed gene interaction network to the computer readable storage medium.