Predicting new gene-disease associations by a disease-aware evaluation of heterogeneous molecular networks
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Get paper here: de la Fuente et al., 2023
Cite us: de la Fuente L, Del Pozo-Valero M, Perea-Romero I, Blanco-Kelly F, Fernández-Caballero L, Cortón M, Ayuso C, Mínguez P. Prioritization of New Candidate Genes for Rare Genetic Diseases by a Disease-Aware Evaluation of Heterogeneous Molecular Networks. International Journal of Molecular Sciences. 2023; 24(2):1661.
A compilation of heterogeneous networks representing 13 different knowledge categories covering different aspects of cell regulation and knowledge generation
A compilation of heterogeneous networks representing 13 different knowledge categories covering different aspects of cell regulation and knowledge generation
Disease-specific evaluation of every knowledge category
Disease-specific evaluation of every knowledge category
Network-based algorithm using a random-walk with restart propagation model (RWWR) that
systematically evaluates and compares KCs ability to recover genes on a given list associated to the studied phenotype/disease
Disease-aware integration of evidence sources
Disease-aware integration of evidence sources
How to run?
How to run?
Download executable file from GitHub and install dependencies.
2. Download heterogeneous knowledge networks
3. Prepare your list of phenotype-related genes
4. Run GLOWgenes and find your candidates!!
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