ArticlePloS one2018
A complex network approach reveals a pivotal substructure of genes linked to schizophrenia.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- New alcohol-related genes suggest shared genetic mechanisms with neuropsychiatric disorders.Nature human behaviour · 2019Pooled it
- TIRESIA and TISBE: Explainable Artificial Intelligence Based Web Platforms for the Transparent Assessment of the Developmental Toxicity of Chemicals and Drugs.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Machine learning and XAI approaches highlight the strong connection betweenScientific reports · 2024Article
- Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions.Science advances · 2023Article
- Editorial: Mapping the pathophysiology of schizophrenia: interactions between multiple cellular pathways, volume II.Frontiers in cellular neuroscience · 2023Article
- A Transcriptome Community-and-Module Approach of the Human Mesoconnectome.Entropy (Basel, Switzerland) · 2021Article
- The interaction between cannabis use and a CB1-related polygenic co-expression index modulates dorsolateral prefrontal activity during working memory processing.Brain imaging and behavior · 2021Article
- Potential energy of complex networks: a quantum mechanical perspective.Scientific reports · 2020Article
- Article
- Transcriptomic context ofProceedings of the National Academy of Sciences of the United States of America · 2018Article
- The research domain criteria framework in drug discovery for neuropsychiatric diseases: focus on negative valence.Brain and neuroscience advancesReview
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10 authors.
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Abstract
Research on brain disorders with a strong genetic component and complex heritability, such as schizophrenia, has led to the development of brain transcriptomics. This field seeks to gain a deeper understanding of gene expression, a key factor in exploring further research issues. Our study focused on how genes are associated amongst each other. In this perspective, we have developed a novel data-driven strategy for characterizing genetic modules, i.e., clusters of strongly interacting genes. The aim was to uncover a pivotal community of genes linked to a target gene for schizophrenia. Our approach combined network topological properties with information theory to highlight the presence of a pivotal community, for a specific gene, and to simultaneously assess the information content of partitions with the Shannon's entropy based on betweenness. We analyzed the publicly available BrainCloud dataset containing post-mortem gene expression data and focused on the Dopamine D2 receptor, encoded by the DRD2 gene. We used four different community detection algorithms to evaluate the consistence of our approach. A pivotal DRD2 community emerged for all the procedures applied, with a considerable reduction in size, compared to the initial network. The stability of the results was confirmed by a Dice index ≥80% within a range of tested parameters. The detected community was also the most informative, as it represented an optimization of the Shannon entropy. Lastly, we verified the strength of connection of the DRD2 community, which was stronger than any other randomly selected community and even more so than the Weighted Gene Co-expression Network Analysis module, commonly considered the standard approach for such studies. This finding substantiates the conclusion that the detected community represents a more connected and informative cluster of genes for the DRD2 community, and therefore better elucidates the behavior of this module of strongly related DRD2 genes. Because this gene plays a relevant role in Schizophrenia, this finding of a more specific DRD2 community will improve the understanding of the genetic factors related with this disorder.
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