ArticleArtificial intelligence in medicine2014
An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods.
Article in Artificial intelligence in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
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- Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- An ontology-based knowledge graph for representing interactions involving RNA molecules.Scientific data · 2024Article
- A knowledge graph approach to predict and interpret disease-causing gene interactions.BMC bioinformatics · 2023Article
- Prioritization of New Candidate Genes for Rare Genetic Diseases by a Disease-Aware Evaluation of Heterogeneous Molecular Networks.International journal of molecular sciences · 2023Article
- NetSHy: network summarization via a hybrid approach leveraging topological properties.Bioinformatics (Oxford, England) · 2023Article
- Heterogeneous data integration methods for patient similarity networks.Briefings in bioinformatics · 2022Review
- Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.Molecular diversity · 2022Review
- Network Analysis of Microarray Data.Methods in molecular biology (Clifton, N.J.) · 2022Article
- The effect of statistical normalization on network propagation scores.Bioinformatics (Oxford, England) · 2021Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction.Scientific reports · 2020Article
- Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform.PLoS biology · 2020Article
- Benchmarking network propagation methods for disease gene identification.PLoS computational biology · 2019Article
- Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings.Nature communications · 2019Article
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- Searching the overlap between network modules with specific betweeness (S2B) and its application to cross-disease analysis.Scientific reports · 2018Article
- Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods.BMC bioinformatics · 2017Article
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Authors and funding
5 authors.
Funding
Abstract
objectiveIn 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
methodsWe 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.
resultsThe 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.
conclusionsNetwork 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.
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