A processor-implemented method comprising: obtaining a set of entities for human rare diseases from one or more sources, the set of entities comprising rare diseases, genes, phenotypes for rare diseases and biological pathways; constructing an initial heterogeneous network using each of an entity from the set of entities as a node and associations between a plurality of entity pairs as symmetric weighted edges forming pair-wise ontological associations and curated associations wherein the curated associations comprise disease-gene associations, gene-gene associations, and phenotype-disease associations using the weights assigned to each of a plurality of pair edges from the one or more sources and ontological associations comprises: determining a pair-wise association score based on Lin similarity of the defined ontological relationships between the phenotypes; and forming phenotype-phenotype edges for each of the phenotypes and assigning the determined pair-wise association score to obtain the pair-wise ontological associations; applying Graph Convolution-based Association Scoring (GCAS) to the initial heterogeneous network for deriving inferred associations for the plurality of entity pairs, wherein deriving the inferred associations comprises applying graph convolution to determine information propagated between neighborhood nodes of each of the plurality of entity pairs in the initial heterogeneous network using the curated associations and based on the convolution parameters comprising convolution operation, dampening factor (θ) and convolution depth; determining association scores, for each of the plurality of entity pairs having no direct links, based on the determined information propagated to obtain the pairwise association score matrix; and inferring associations between each of the plurality of entities based on the pairwise association score matrix to obtain the inferred associations; creating a Heterogeneous Association Network for Rare Diseases (HANRD) by adding the inferred associations to the initial heterogeneous network; and generating a prioritized set of genes for an input query being received in the HANRD, the input query comprising a plurality of phenotypes corresponding to a clinical case, and wherein generating the prioritized set of genes comprises sorting cumulative association score between each of the phenotype from the plurality of phenotypes and associated genes for each of the phenotype in the HANRD wherein the cumulative association score is sum of association scores for each of the plurality of phenotypes, and wherein the association score is based on the gene neighbors of each of the plurality of phenotypes in the HANRD. obtaining a set of entities for human rare diseases from one or more sources, the set of entities comprising rare diseases, genes, phenotypes for rare diseases and biological pathways; constructing an initial heterogeneous network using each of an entity from the set of entities as a node and associations between a plurality of entity pairs as symmetric weighted edges forming pair-wise ontological associations and curated associations wherein the curated associations comprise disease-gene associations, gene-gene associations, and phenotype-disease associations using the weights assigned to each of a plurality of pair edges from the one or more sources and ontological associations comprises: determining a pair-wise association score based on Lin similarity of the defined ontological relationships between the phenotypes; and forming phenotype-phenotype edges for each of the phenotypes and assigning the determined pair-wise association score to obtain the pair-wise ontological associations; applying Graph Convolution-based Association Scoring (GCAS) to the initial heterogeneous network for deriving inferred associations for the plurality of entity pairs, wherein deriving the inferred associations comprises applying graph convolution to determine information propagated between neighborhood nodes of each of the plurality of entity pairs in the initial heterogeneous network using the curated associations and based on the convolution parameters comprising convolution operation, dampening factor (θ) and convolution depth; determining association scores, for each of the plurality of entity pairs having no direct links, based on the determined information propagated to obtain the pairwise association score matrix; and inferring associations between each of the plurality of entities based on the pairwise association score matrix to obtain the inferred associations; creating a Heterogeneous Association Network for Rare Diseases (HANRD) by adding the inferred associations to the initial heterogeneous network; and generating a prioritized set of genes for an input query being received in the HANRD, the input query comprising a plurality of phenotypes corresponding to a clinical case, and wherein generating the prioritized set of genes comprises sorting cumulative association score between each of the phenotype from the plurality of phenotypes and associated genes for each of the phenotype in the HANRD wherein the cumulative association score is sum of association scores for each of the plurality of phenotypes, and wherein the association score is based on the gene neighbors of each of the plurality of phenotypes in the HANRD.