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Showing 1 to 5 of 5 for “"Gene prioritization"”.

  1. Gene prioritization through hybrid distance-score rank aggregation

    … developing novel rank aggregation methods for gene prioritization. Gene prioritization refers to a family of computational techniques for inferring disease genes through a set of training genes and carefully chosen similarity criteria. Test genes are scored based on their average similarity to …

    uiuc Repository record for Gene prioritization through hybrid distance-score rank aggregation (opens in a new tab)

  2. Causal Gene Prioritization Across Diverse Diseases Through Multi-Omic Data Integration

    … loci, yet the underlying causal variants, genes and tissues of action are unknown for most of the reported associations. This limits our biological understanding of the mechanisms underlying diseases and presents a major bottleneck for experimental follow up and clinical translation of the …

    cambridge Repository record for Causal Gene Prioritization Across Diverse Diseases Through Multi-Omic Data Integration (opens in a new tab)

  3. A novel weighted rank aggregation algorithm with applications in gene prioritization

    … of the aggregation method on a set of test genes pertaining to the Bardet-Biedl syndrome, schizophrenia, and HIV and show that the combinatorial method matches or outperforms state-of-the art algorithms such as ToppGene.

    uiuc Repository record for A novel weighted rank aggregation algorithm with applications in gene prioritization (opens in a new tab)

  4. Algorithms for discovering disease genes by integrating 'omics data

    … “-omic” data, including genomic sequences, gene expression, and molecular interactions. Genome Wide Association Studies (GWAS) compare genomic sequences from healthy and affected populations to identify genetic variants that are potentially associated with diseases. Monitoring of gene

    ohiolink Repository record for Algorithms for discovering disease genes by integrating 'omics data (opens in a new tab)

  5. Integrative analysis of heterogeneous genomic datasets to discover genetic etiology of autism spectrum disorders

    Understanding the genetic background of complex diseases is crucial to medical research, with implications to diagnosis, treatment and drug development. As molecular approaches to this challenge are time consuming and costly, computational approaches offer an efficient alternative. Such approaches …

    mit Repository record for Integrative analysis of heterogeneous genomic datasets to discover genetic etiology of autism spectrum disorders (opens in a new tab)