Artif Intell Med - An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods.

Tópicos

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Resumo

JECTIVE: In the context of "network medicine", gene prioritization methods represent one of the main tools to discover candidate disease genes by exploiting the large amount of data covering different types of functional relationships between genes. Several works proposed to integrate multiple sources of data to improve disease gene prioritization, but to our knowledge no systematic studies focused on the quantitative evaluation of the impact of network integration on gene prioritization. In this paper, we aim at providing an extensive analysis of gene-disease associations not limited to genetic disorders, and a systematic comparison of different network integration methods for gene prioritization.MATERIALS AND METHODS: We collected nine different functional networks representing different functional relationships between genes, and we combined them through both unweighted and weighted network integration methods. We then prioritized genes with respect to each of the considered 708 medical subject headings (MeSH) diseases by applying classical guilt-by-association, random walk and random walk with restart algorithms, and the recently proposed kernelized score functions.RESULTS: The results obtained with classical random walk algorithms and the best single network achieved an average area under the curve (AUC) across the 708 MeSH diseases of about 0.82, while kernelized score functions and network integration boosted the average AUC to about 0.89. Weighted integration, by exploiting the different "informativeness" embedded in different functional networks, outperforms unweighted integration at 0.01 significance level, according to the Wilcoxon signed rank sum test. For each MeSH disease we provide the top-ranked unannotated candidate genes, available for further bio-medical investigation.CONCLUSIONS: Network integration is necessary to boost the performances of gene prioritization methods. Moreover the methods based on kernelized score functions can further enhance disease gene ranking results, by adopting both local and global learning strategies, able to exploit the overall topology of the network.

Resumo Limpo

jectiv context network medicin gene priorit method repres one main tool discov candid diseas gene exploit larg amount data cover differ type function relationship gene sever work propos integr multipl sourc data improv diseas gene priorit knowledg systemat studi focus quantit evalu impact network integr gene priorit paper aim provid extens analysi genediseas associ limit genet disord systemat comparison differ network integr method gene prioritizationmateri method collect nine differ function network repres differ function relationship gene combin unweight weight network integr method priorit gene respect consid medic subject head mesh diseas appli classic guiltbyassoci random walk random walk restart algorithm recent propos kernel score functionsresult result obtain classic random walk algorithm best singl network achiev averag area curv auc across mesh diseas kernel score function network integr boost averag auc weight integr exploit differ inform embed differ function network outperform unweight integr signific level accord wilcoxon sign rank sum test mesh diseas provid toprank unannot candid gene avail biomed investigationconclus network integr necessari boost perform gene priorit method moreov method base kernel score function can enhanc diseas gene rank result adopt local global learn strategi abl exploit overal topolog network

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