Computational tools for prioritizing candidate genes: boosting disease gene discovery

By Yves Moreau, Léon-Charles Tranchevent. 2012

431 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Next-generation diagnostics and disease-gene discovery with the Exomiser (2015, background): “…data sets, including expression data, genetic sequences, functional annotations, protein-protein interaction networks and information from the medical literature in order to derive a ranked list of genes that provide investigators clues about those genes that warrant closer investigation 14 .”
Graph Representation Learning in Biomedicine and Healthcare (2021, neutral): “Characterizing a protein’s function in specific biological contexts is a challenging and experimentally intensive task 119,120 .”
Systematic noise degrades gene co-expression signals but can be corrected (2015, neutral): “Gene prioritisation methods [29, 30] identify the most promising genes from a list of candidates lacking adequate support to corroborate their involvement in a specific disease pathogenesis.”
Data Fusion by Matrix Factorization (2013, methodology): “Data integration is an important aspect of bioinformatics and the subject of much recent research (Yu et al., 2010; Vaske et al., 2010; Moreau & Tranchevent, 2012; Mostafavi & Morris, 2012).”
Integrating predicted transcriptome from multiple tissues improves association detection (2018, background): “However, understanding the mechanism underlying these associations remains a challenging 38 problem, requiring follow-up studies and a wide array of techniques such as prioritization [4] and pathway 39 analysis [5].”
Human symptoms–disease network (2014, background): “Interestingly, the inclusion of disease phenotype similarities can substantially improve the performance of candidate gene prediction methods 21–24 .”
Biological interpretation of genome-wide association studies using predicted gene functions (2015, methodology): “and converting the adjusted z-score to a P value; and (3) repeating steps 1 and 2 twenty times to estimate experiment-wide FDRs similar to the method described above.”