A new method to measure the semantic similarity of GO terms

Wang, James; Du, Zhidian; Payattakool, Rapeeporn; Yu, Philip S.; Chen, Chin‐Fu

doi:10.1093/bioinformatics/btm087

Cited by 1,083 publications

(1,049 citation statements)

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“…GO term similarity [14,15] in biological process were used to represent functional relationship between genes. We found significant co-relation between GO similarity and Hi-C interaction for all the 273,458 gene pairs (Figure.…”

Section: Resultsmentioning

confidence: 99%

“…All gene pairs within a same chromatin were divided into groups of equal size according to their Hi-C interactions, and we ploted their Hi-C interactions against the GO term similarity calculated by Wang's method[14]. Error bars represent the standard error.…”

Section: Resultsmentioning

confidence: 99%

“…For the gene function similarity analysis, we used Wang's method for measuring the semantic similarity of GO terms[14], and used an R package, GOSemSim[15] to achieve it.…”

Section: Methodsmentioning

confidence: 99%

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Human transcriptional interactome of chromatin contribute to gene co-expression

Dong

Chen

et al. 2010

BMC Genomics

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BackgroundTranscriptional interactome of chromatin is one of the important mechanisms in gene transcription regulation. By chromatin conformation capture and 3D FISH experiments, several chromatin interactions cases among sequence-distant genes or even inter-chromatin genes were reported. However, on genomics level, there is still little evidence to support these mechanisms. Recently based on Hi-C experiment, a genome-wide picture of chromatin interactions in human cells was presented. It provides a useful material for analysing whether the mechanism of transcriptional interactome is common.ResultsThe main work here is to demonstrate whether the effects of transcriptional interactome on gene co-expression exist on genomic level. While controlling the effects of transcription factors control similarities (TCS), we tested the correlation between Hi-C interaction and the mutual ranks of gene co-expression rates (provided by COXPRESdb) of intra-chromatin gene pairs. We used 6,084 genes with both TF annotation and co-expression information, and matched them into 273,458 pairs with similar Hi-C interaction ranks in different cell types. The results illustrate that co-expression is strongly associated with chromatin interaction. Further analysis using GO annotation reveals potential correlation between gene function similarity, Hi-C interaction and their co-expression.ConclusionsAccording to the results in this research, the intra-chromatin interactome may have relation to gene function and associate with co-expression. This study provides evidence for illustrating the effect of transcriptional interactome on transcription regulation.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Human transcriptional interactome of chromatin contribute to gene co-expression

Dong

Chen

et al. 2010

BMC Genomics

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“…The "molecular function" (MF) category describes fundamental biochemical activities (including specific binding to ligands or structures of a gene product) at the molecular level [2]. As a popular resource used for functional annotation, MF provides rich information and a convenient way to study gene functional similarity by comparing terms with which the genes are annotated [3-7], which subsequently supports a wide variety of applications, such as assessing target gene functions [8], predicting gene functional associations [9], inferring protein nomenclature [10], predicting sub-cellular localization [11], discovering new pathways [12], etc .…”

Section: Introductionmentioning

confidence: 99%

“…These approaches can be classified into two distinct categories: 1) group-wise, meaning calculating gene-to-gene similarity directly based on a statistical framework considering all the terms annotated to the target genes [13-15], and 2) pair-wise, i.e ., indirectly computing gene-to-gene similarity using term-to-term similarities computed with GO semantic measures [12,16-21]. Each of the aforementioned measurements adopts one or a few kinds of knowledge in the GO efficiently.…”

Section: Introductionmentioning

confidence: 99%

Towards integrative gene functional similarity measurement

2014

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BackgroundIn Gene Ontology, the "Molecular Function" (MF) categorization is a widely used knowledge framework for gene function comparison and prediction. Its structure and annotation provide a convenient way to compare gene functional similarities at the molecular level. The existing gene similarity measures, however, solely rely on one or few aspects of MF without utilizing all the rich information available including structure, annotation, common terms, lowest common parents.ResultsWe introduce a rank-based gene semantic similarity measure called InteGO by synergistically integrating the state-of-the-art gene-to-gene similarity measures. By integrating three GO based seed measures, InteGO significantly improves the performance by about two-fold in all the three species studied (yeast, Arabidopsis and human).ConclusionsInteGO is a systematic and novel method to study gene functional associations. The software and description are available at http://www.msu.edu/~jinchen/InteGO.

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Multi‐network logistic matrix factorization for metabolite–disease interaction prediction

Jiang

2020

FEBS Letters

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Edited by Qinghua CuiIdentifying disease-related metabolites is of great significance for the diagnosis, prevention, and treatment of disease. In this study, we propose a novel computational model of multiple-network logistic matrix factorization (MN-LMF) for predicting metabolite-disease interactions, which is especially relevant for new diseases and new metabolites. First, MN-LMF builds disease (or metabolite) similarity network by integrating heterogeneous omics data. Second, it combines these similarities with known metabolite-disease interaction networks, using modified logistic matrix factorization to predict potential metabolite-disease interactions. Experimental results show that MN-LMF accurately predicts metabolite-disease interactions, and outperforms other state-of-the-art methods. Moreover, case studies also demonstrated the effectiveness of the model to infer unknown metabolite-disease interactions for novel diseases without any known associations.

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A new method to measure the semantic similarity of GO terms

Abstract: http://bioinformatics.clemson.edu/Publication/Supplement/gsp.htm.

Cited by 1,083 publications

References 8 publications

Human transcriptional interactome of chromatin contribute to gene co-expression

Human transcriptional interactome of chromatin contribute to gene co-expression

Towards integrative gene functional similarity measurement

Multi‐network logistic matrix factorization for metabolite–disease interaction prediction

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