ArticleChinese journal of cancer2013
GOMA: functional enrichment analysis tool based on GO modules.
Article in Chinese journal of cancer, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Transcriptome profile analysis of genes by RNA-sequencing in neonatal maternal separation rats with autistic-like behaviors.Translational pediatrics · 2026Article
- Identification and validation of mitochondria-related genes in panvascular diseases.Frontiers in medicine · 2025Article
- Prognostic model construction and immune microenvironment analysis of esophageal cancer based on gene expression data and microRNA target genes.Translational cancer research · 2023Article
- Identification of key genes and pathways in mild and severe nonalcoholic fatty liver disease by integrative analysis.Chronic diseases and translational medicine · 2021Article
- Integration of gene expression data identifies key genes and pathways in colorectal cancer.Medical oncology (Northwood, London, England) · 2021Article
- Effect of VX‑765 on the transcriptome profile of mice spinal cords with acute injury.Molecular medicine reports · 2020Article
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- Effect of M2 macrophage adoptive transfer on transcriptome profile of injured spinal cords in rats.Experimental biology and medicine (Maywood, N.J.) · 2019Article
- CEA: Combination-based gene set functional enrichment analysis.Scientific reports · 2018Article
- Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling.Chinese journal of cancer · 2015Article
- Cancer bioinformatics: detection of chromatin states, SNP-containing motifs, and functional enrichment modules.Chinese journal of cancer · 2013Article
- CoCiter: an efficient tool to infer gene function by assessing the significance of literature co-citation.PloS one · 2013Article
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4 authors.
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Abstract
Analyzing the function of gene sets is a critical step in interpreting the results of high-throughput experiments in systems biology. A variety of enrichment analysis tools have been developed in recent years, but most output a long list of significantly enriched terms that are often redundant, making it difficult to extract the most meaningful functions. In this paper, we present GOMA, a novel enrichment analysis method based on the new concept of enriched functional Gene Ontology (GO) modules. With this method, we systematically revealed functional GO modules, i.e., groups of functionally similar GO terms, via an optimization model and then ranked them by enrichment scores. Our new method simplifies enrichment analysis results by reducing redundancy, thereby preventing inconsistent enrichment results among functionally similar terms and providing more biologically meaningful results.
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