GO-based Functional Dissimilarity of Gene Sets

Díaz–Díaz, Norberto; Aguilar–Ruiz, Jesús S.

doi:10.1186/1471-2105-12-360

Cited by 17 publications

(13 citation statements)

References 37 publications

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“…The more the two sets are different, the lower the GOscore BM is. Among all the most common semantic similarity measures available between two terms of an ontology (Jiang [22], Lin [23] and Resnik [24]), we decided to use the Resnik one because it is considered the most efficient rate in correlating gene sequence similarities [32] [33]. Since the Resnik similarity measure has no upper bound, the GOscore BM that uses it has no predefined upper bound; this does not influence our application, since we look for low values of the score, which we heuristically defined as GOscore BM < 1.…”

Section: Novelty Indicatormentioning

confidence: 99%

Novelty Indicator for Enhanced Prioritization of Predicted Gene Ontology Annotations

Chicco

Palluzzi

Masseroli

2018

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Biomolecular controlled annotations have become pivotal in computational biology, because they allow scientists to analyze large amounts of biological data to better understand test results, and to infer new knowledge. Yet, biomolecular annotation databases are incomplete by definition, like our knowledge of biology, and might contain errors and inconsistent information. In this context, machine-learning algorithms able to predict and prioritize new annotations are both effective and efficient, especially if compared with time-consuming trials of biological validation. To limit the possibility that these techniques predict obvious and trivial high-level features, and to help prioritize their results, we introduce a new element that can improve accuracy and relevance of the results of an annotation prediction and prioritization pipeline. We propose a novelty indicator able to state the level of "originality" of the annotations predicted for a specific gene to Gene Ontology (GO) terms. This indicator, joint with our previously introduced prediction steps, helps by prioritizing the most novel interesting annotations predicted. We performed an accurate biological functional analysis of the prioritized annotations predicted with high accuracy by our indicator and previously proposed methods. The relevance of our biological findings proves effectiveness and trustworthiness of our indicator and of its prioritization of predicted annotations.

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Section: Novelty Indicatormentioning

confidence: 99%

Novelty Indicator for Enhanced Prioritization of Predicted Gene Ontology Annotations

Chicco

Palluzzi

Masseroli

2018

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…Edge-based measures compute a distance between GO terms using the directed graph topology. This distance can be the shortest path between two compared terms [ 23 ] or the length of the path between the root of the ontology and the lowest common ancestor of the compared terms [ 24 – 28 ]. This root to ancestor distance makes terms with a deep common ancestor more similar than terms with a common ancestor close to the root.…”

Section: Metrics Backgroundmentioning

confidence: 99%

Optimal Threshold Determination for Interpreting Semantic Similarity and Particularity: Application to the Comparison of Gene Sets and Metabolic Pathways Using GO and ChEBI

2015

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BackgroundThe analysis of gene annotations referencing back to Gene Ontology plays an important role in the interpretation of high-throughput experiments results. This analysis typically involves semantic similarity and particularity measures that quantify the importance of the Gene Ontology annotations. However, there is currently no sound method supporting the interpretation of the similarity and particularity values in order to determine whether two genes are similar or whether one gene has some significant particular function. Interpretation is frequently based either on an implicit threshold, or an arbitrary one (typically 0.5). Here we investigate a method for determining thresholds supporting the interpretation of the results of a semantic comparison.ResultsWe propose a method for determining the optimal similarity threshold by minimizing the proportions of false-positive and false-negative similarity matches. We compared the distributions of the similarity values of pairs of similar genes and pairs of non-similar genes. These comparisons were performed separately for all three branches of the Gene Ontology. In all situations, we found overlap between the similar and the non-similar distributions, indicating that some similar genes had a similarity value lower than the similarity value of some non-similar genes. We then extend this method to the semantic particularity measure and to a similarity measure applied to the ChEBI ontology. Thresholds were evaluated over the whole HomoloGene database. For each group of homologous genes, we computed all the similarity and particularity values between pairs of genes. Finally, we focused on the PPAR multigene family to show that the similarity and particularity patterns obtained with our thresholds were better at discriminating orthologs and paralogs than those obtained using default thresholds.ConclusionWe developed a method for determining optimal semantic similarity and particularity thresholds. We applied this method on the GO and ChEBI ontologies. Qualitative analysis using the thresholds on the PPAR multigene family yielded biologically-relevant patterns.

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“…GFD [3] is a novel GO-based method for measuring gene set dissimilarity. The proposal singles out the most relevant function of the whole set to compute the dissimilarity value.…”

Section: Go-based Semantic Similarity Measurementioning

confidence: 99%

Genes functional coherence based on actual biological knowledge

Díaz–Díaz

2013

AI Communications

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This work proposes two new approaches to establish the quality of genetic model based on current biological knowledge. First, it is developed a KEGG-based tool that provides a friendly graphical environment to analyze gene-enrichment. Moreover, a novel GO-based dissimilarity measure is proposed for evaluating groups of genes based on the most relevant functions of the whole set. To found this function, an heuristic approach based on Voronoi diagram has been presented.

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GO-based Functional Dissimilarity of Gene Sets

Cited by 17 publications

References 37 publications

Novelty Indicator for Enhanced Prioritization of Predicted Gene Ontology Annotations

Novelty Indicator for Enhanced Prioritization of Predicted Gene Ontology Annotations

Optimal Threshold Determination for Interpreting Semantic Similarity and Particularity: Application to the Comparison of Gene Sets and Metabolic Pathways Using GO and ChEBI

Genes functional coherence based on actual biological knowledge

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