Author
Listed:
- Alfonso Monaco
- Anna Monda
- Nicola Amoroso
- Alessandro Bertolino
- Giuseppe Blasi
- Pasquale Di Carlo
- Marco Papalino
- Giulio Pergola
- Sabina Tangaro
- Roberto Bellotti
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.
Suggested Citation
Alfonso Monaco & Anna Monda & Nicola Amoroso & Alessandro Bertolino & Giuseppe Blasi & Pasquale Di Carlo & Marco Papalino & Giulio Pergola & Sabina Tangaro & Roberto Bellotti, 2018.
"A complex network approach reveals a pivotal substructure of genes linked to schizophrenia,"
PLOS ONE, Public Library of Science, vol. 13(1), pages 1-18, January.
Handle:
RePEc:plo:pone00:0190110
DOI: 10.1371/journal.pone.0190110
Download full text from publisher
Citations
Citations are extracted by the
CitEc Project, subscribe to its
RSS feed for this item.
Cited by:
- Amulyashree Sridhar & Sharvani GS & AH Manjunatha Reddy & Biplab Bhattacharjee & Kalyan Nagaraj, 2019.
"The Eminence of Co-Expressed Ties in Schizophrenia Network Communities,"
Data, MDPI, vol. 4(4), pages 1-23, November.
- Alfonso Monaco & Nicola Amoroso & Loredana Bellantuono & Eufemia Lella & Angela Lombardi & Anna Monda & Andrea Tateo & Roberto Bellotti & Sabina Tangaro, 2019.
"Shannon entropy approach reveals relevant genes in Alzheimer’s disease,"
PLOS ONE, Public Library of Science, vol. 14(12), pages 1-29, December.
- Domenico Pomarico & Annarita Fanizzi & Nicola Amoroso & Roberto Bellotti & Albino Biafora & Samantha Bove & Vittorio Didonna & Daniele La Forgia & Maria Irene Pastena & Pasquale Tamborra & Alfredo Zit, 2021.
"A Proposal of Quantum-Inspired Machine Learning for Medical Purposes: An Application Case,"
Mathematics, MDPI, vol. 9(4), pages 1-15, February.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0190110. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.