Abstract: This disclosure relates generally to method and system for graph convolution based gene prioritization on heterogeneous networks. The method includes obtaining a set of entities for human rare diseases from one or more sources containing rare diseases, genes, phenotypes for rare diseases and biological pathways and constructing an initial heterogeneous network using each of an entity from the set of entities. the initial heterogeneous network applying Graph Convolution-based Association Scoring (GCAS) to the initial heterogeneous network to derive inferred associations and 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.

Abstract: This disclosure relates generally to method and system for graph convolution based gene prioritization on heterogeneous networks. The method includes obtaining a set of entities for human rare diseases from one or more sources containing rare diseases, genes, phenotypes for rare diseases and biological pathways and constructing an initial heterogeneous network using each of an entity from the set of entities. the initial heterogeneous network applying Graph Convolution-based Association Scoring (GCAS) to the initial heterogeneous network to derive inferred associations and 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.

Abstract: Systems and methods for automated creation of a dictionary of scientific terms are described herein. Initially, input data is filtered to obtain a primary file having a plurality of term-ID pairs with each term-ID pair having a unique term ID and a scientific term. Further, a remove-term file is generated based on one or more term-ID pairs identified from the primary file such that the scientific terms of each term-ID pair corresponds to one of additional terms, frequent scientific terms, and undesirable terms. At least one term-ID pair from among the one or more term-ID pairs is altered to obtain a modified term-ID pair based on modification rules. The modified term-ID pair is added to an add-term file and a modified file is obtained based on the remove-term file and the add-term file. Duplicate term-ID pairs present in the modified file are removed to obtain the dictionary of scientific terms.