Semantic similarity measurement between gene ontology terms based on exclusively inherited shared information

Zhang, Shubo; Lai, Jianhuang

doi:10.1016/j.gene.2014.12.062

Cited by 12 publications

(12 citation statements)

References 37 publications

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“…Zhang and Lai introduced the exclusively inherited common ancestors set as an alternative to the DCA [21] that calculated a subset of the DCA in linear time. Our group developed an efficient implementation of the algorithm by Zhang and Lai, called the semantic frontier (SF) algorithm.…”

Section: Methodsmentioning

confidence: 99%

“…SF calculates the common ancestor set in O ( k ), each BFS visitor operates in O ( k ), and checking for frontier edges takes O ( k ) time. Zhang and Lai report the complexity of their algorithm as O ( n log n ) [21] . The greater complexity reported by Zhang and Lai may be the result of less efficient ancestor or IC access which GGTK provides in O ( k ) and O (1) time respectively.…”

Section: Methodsmentioning

confidence: 99%

“…The computational cost of path counting poses a significant challenge to the real-time calculation of GraSM. Zhang and Lai [21] proposed a faster GraSM alternative called exclusively inherited shared information that calculates a subset of the disjunctive common ancestors. The modular separation of shared information from term similarity leads to some interesting properties.…”

Section: Introductionmentioning

confidence: 99%

“…As no method can determine true semantic similarity between genes [13] , various methods for evaluation have been put forward to evaluate the performance of semantic similarity methods. Commonly used evaluations include correlation with sequence similarity [11] , [19] , correlation with domain set similarity [13] , [20] , [27] , correlations with gene expression [4] , [23] , [28] , clustering genes into known pathways [3] , [5] , [21] , and prediction of protein–protein interactions [3] , [6] , [29] . Based on these past works, we have constructed a new suite of six benchmarks to evaluate semantic similarity that provides evaluations for a broad range of use cases.…”

Section: Introductionmentioning

confidence: 99%

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The effects of shared information on semantic calculations in the gene ontology

Bible

Sun

Morasso

et al. 2017

Computational and Structural Biotechnology Journal

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The structured vocabulary that describes gene function, the gene ontology (GO), serves as a powerful tool in biological research. One application of GO in computational biology calculates semantic similarity between two concepts to make inferences about the functional similarity of genes. A class of term similarity algorithms explicitly calculates the shared information (SI) between concepts then substitutes this calculation into traditional term similarity measures such as Resnik, Lin, and Jiang-Conrath. Alternative SI approaches, when combined with ontology choice and term similarity type, lead to many gene-to-gene similarity measures. No thorough investigation has been made into the behavior, complexity, and performance of semantic methods derived from distinct SI approaches. We apply bootstrapping to compare the generalized performance of 57 gene-to-gene semantic measures across six benchmarks. Considering the number of measures, we additionally evaluate whether these methods can be leveraged through ensemble machine learning to improve prediction performance. Results showed that the choice of ontology type most strongly influenced performance across all evaluations. Combining measures into an ensemble classifier reduces cross-validation error beyond any individual measure for protein interaction prediction. This improvement resulted from information gained through the combination of ontology types as ensemble methods within each GO type offered no improvement. These results demonstrate that multiple SI measures can be leveraged for machine learning tasks such as automated gene function prediction by incorporating methods from across the ontologies. To facilitate future research in this area, we developed the GO Graph Tool Kit (GGTK), an open source C++ library with Python interface (github.com/paulbible/ggtk).

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The effects of shared information on semantic calculations in the gene ontology

Bible

Sun

Morasso

et al. 2017

Computational and Structural Biotechnology Journal

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“…The most commonly used node-based SSMs are Resnik [17], Lin [18], and Jiang & Conrath [20], which were initially developed for WordNet and subsequently applied to GO by Lord et al [46], [47]. Thereafter, a number of node-based SSMs have been proposed in order to improve the existing SSMs in different perspectives and applications [19], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66]. The major drawbacks of IC based SSMs are already pointed out in section 1.1.…”

Section: Go-based Ssmsmentioning

confidence: 99%

A New Family of Similarity Measures for Scoring Confidence of Protein Interactions using Gene Ontology

Paul

Anand

2018

Preprint

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The large-scale protein-protein interaction (PPI) data has the potential to play a significant role in the endeavor of understanding cellular processes. However, the presence of a considerable fraction of false positives is a bottleneck in realizing this potential. There have been continuous efforts to utilize complementary resources for scoring confidence of PPIs in a manner that false positive interactions get a low confidence score. Gene Ontology (GO), a taxonomy of biological terms to represent the properties of gene products and their relations, has been widely used for this purpose. We utilize GO to introduce a new set of specificity measures: Relative Depth Specificity (RDS), Relative Node-based Specificity (RNS), and Relative Edge-based Specificity (RES), leading to a new family of similarity measures. We use these similarity measures to obtain a confidence score for each PPI. We evaluate the new measures using four different benchmarks. We show that all the three measures are quite effective. Notably, RNS and RES more effectively distinguish true PPIs from false positives than the existing alternatives. RES also shows a robust set-discriminating power and can be useful for protein functional clustering as well.

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Semantic Similarity in the Gene Ontology

Pesquita

2016

Methods in Molecular Biology

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Gene Ontology-based semantic similarity (SS) allows the comparison of GO terms or entities annotated with GO terms, by leveraging on the ontology structure and properties and on annotation corpora. In the last decade the number and diversity of SS measures based on GO has grown considerably, and their application ranges from functional coherence evaluation, protein interaction prediction, and disease gene prioritization.Understanding how SS measures work, what issues can affect their performance and how they compare to each other in different evaluation settings is crucial to gain a comprehensive view of this area and choose the most appropriate approaches for a given application.In this chapter, we provide a guide to understanding and selecting SS measures for biomedical researchers. We present a straightforward categorization of SS measures and describe the main strategies they employ. We discuss the intrinsic and external issues that affect their performance, and how these can be addressed. We summarize comparative assessment studies, highlighting the top measures in different settings, and compare different implementation strategies and their use. Finally, we discuss some of the extant challenges and opportunities, namely the increased semantic complexity of GO and the need for fast and effi cient computation, pointing the way towards the future generation of SS measures.

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Semantic similarity measurement between gene ontology terms based on exclusively inherited shared information

Cited by 12 publications

References 37 publications

The effects of shared information on semantic calculations in the gene ontology

The effects of shared information on semantic calculations in the gene ontology

A New Family of Similarity Measures for Scoring Confidence of Protein Interactions using Gene Ontology

Semantic Similarity in the Gene Ontology

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