From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations

Du, Pan; Feng, Gang; Flatow, Jared; Song, Jie‐Young; Holko, Michelle; Kibbe, Warren A.; Lin, Simon

doi:10.1093/bioinformatics/btp193

Cited by 104 publications

(96 citation statements)

References 17 publications

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“…We use the hypergeometric test to measure the statistical significance of the enrichment of a gene set S in a GO [2] or DO term t [8]. Now, E is the set of genes from StatNetExpression that are annotated by any GO/DO term, A is the subset of genes from S that are present in E, G is the subset of genes from E that are annotated by t, and O is the set of genes that are present in both A and G [9].…”

Section: Validating Aging-related Predictionsmentioning

confidence: 99%

Improving identification of key players in aging via network de-noising

Yoo

Chen

Faisal

et al. 2014

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

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Since human aging is hard to study experimentally due to long lifespan and ethical constraints, current "ground truth" knowledge about human aging has mostly been predicted computationally or statistically by transferring the knowledge from well-studied model species via sequence homology or by studying human gene expression data. Since genes, i.e., their protein products, function by interacting with each other, analysis of protein-protein interaction (PPI) network data in the context of aging is promising. Existing studies of aging from PPI networks have typically relied on static network representations. But because cellular functioning is dynamic, and because different biological data types capture complementary slices of the cell, we recently integrated the static human PPI network with aging-related gene expression data to form dynamic, age-specific networks. Then, we predicted as key players in aging those proteins whose network topologies significantly changed with age.Since current PPI networks are noisy, here, we apply stateof-the-art link prediction methods to the human PPI network to de-noise it. After we show that de-noising improves biological quality of the network, we integrate the de-noised network with the aging-related expression data to construct de-noised age-specific networks and predict novel (hopefully improved) key players in aging from the de-noised data. Indeed, PPI network de-noising improves the quality of the predictions: it results in more significant overlap between the predicted aging-related data and the "ground truth"aging-related data. Yet, we obtain many novel predictions. Thus, we produce new knowledge about human aging from de-noised dynamic networks encompassing multiple biological data types, complementing in this way the existing knowledge obtained from sequence or expression data.

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Section: Validating Aging-related Predictionsmentioning

confidence: 99%

Improving identification of key players in aging via network de-noising

Yoo

Chen

Faisal

et al. 2014

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

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“…pizzas or human diseases (Horridge et al, 2004;Du et al, 2009). In many research disciplines outside of taxonomy, it is epistemologically permissible to maintain multiple alternative hierarchies.…”

Section: Sections I To Iii -Epistemological Constraintsmentioning

confidence: 99%

Biological Taxonomy and Ontology Development: Scope and Limitations

Franz

2011

Biodiv. Inf.

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Abstract.  The prospects of integrating full-blown biological taxonomies into an ontological reasoning framework are reviewed. Traditionally ontological representations of taxonomy have adopted the model of a single and static hierarchy. This model is contrasted with a more realistic situation involving dynamic revisions of particular groups and alignments among alternative taxonomic perspectives. Taxonomic practice is bound by a range of epistemological constraints and linguistic conventions that run orthogonal to the logical background from which ontological entities and relationships originate, resulting in severe challenges for ontological representation and reasoning. In particular, the purported existence of a single hierarchy in nature forces taxonomists to gradually approximate this hierarchy and make frequent rearrangements in light of new evidence. The evolvability of taxa implies that taxon-defining features may be lost in subordinate members or independently gained across multiple sections of the tree of life. As a result, many terms for phenotypic properties are phylogenetically underdetermined and have limited hierarchical transitivity. The standard approach of defining taxa both in reference to properties (intensional) and members (ostensive) undermines the individual/class dichotomy that sustains conventional ontologies, and compromises the reasoning capabilities associated with this distinction. Neither the use of Linnaean ranks in taxonomy nor the 250-year legacy of nomenclatural adjustments have obvious analogues in the ontological realm. Lastly, the piece-meal appearance of full-blown taxonomic information makes it necessary to use expert alignments to obtain a comprehensive static perspective. In light of these limitations, research along the taxonomy/ontology interface should focus either on strictly nomenclatural entities and relationships or on ontology-driven strategies for aligning multiple taxonomies, but not on building static networks for large portions of the tree of life. The prospects of using ontology-based services in taxonomy will largely depend on the ability of the taxonomic expert community to present its products in ways that are more compatible with ontological principles than concurrent practice.

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“…It is suggested that GeneRIF-Disease Ontology (DO) mapping performs better than OMIM when the prediction performances are compared [23,24]. Mapping process is illustrated in Figure 6, where the association between Gene ID: 7040 and DO ID: 2585 is shown.…”

Section: Disease Datamentioning

confidence: 99%

METU-SNP: An Integrated Software System for SNPComplex Disease Association Analysis

Üstünkar¹,

Son²

2011

Journal of Integrative Bioinformatics

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SummaryRecently, there has been increasing research to discover genomic biomarkers, haplotypes, and potentially other variables that together contribute to the development of diseases. Single Nucleotide Polymorphisms (SNPs) are the most common form of genomic variations and they can represent an individual's genetic variability in greatest detail. Genome-wide association studies (GWAS) of SNPs, high-dimensional case-control studies, are among the most promising approaches for identifying disease causing variants. METU-SNP software is a Java based integrated desktop application specifically designed for the prioritization of SNP biomarkers and the discovery of genes and pathways related to diseases via analysis of the GWAS case-control data. Outputs of METU-SNP can easily be utilized for the downstream biomarkers research to allow the prediction and the diagnosis of diseases and other personalized medical approaches. Here, we introduce and describe the system functionality and architecture of the METU-SNP. We believe that the METU-SNP will help researchers with the reliable identification of SNPs that are involved in the etiology of complex diseases, ultimately supporting the development of personalized medicine approaches and targeted drug discoveries.

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From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations

Cited by 104 publications

References 17 publications

Improving identification of key players in aging via network de-noising

Improving identification of key players in aging via network de-noising

Biological Taxonomy and Ontology Development: Scope and Limitations

METU-SNP: An Integrated Software System for SNPComplex Disease Association Analysis

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