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Special Issue "Advances of Psychiatric Genetics"

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Genetics and Genomics".

Deadline for manuscript submissions: 28 February 2022.

Special Issue Editors

Dr. Judit Bene
E-Mail Website
Guest Editor
Department of Medical Genetics, Medical School, University of Pécs, Pecs, Hungary
Interests: molecular pathogenesis; next generation sequencing; rare disorders; autism spectrum disorders; genetic testing
Dr. Kinga Hadzsiev
E-Mail Website
Guest Editor
Department of Medical Genetics, Medical School, University of Pécs, Pecs, Hungary
Interests: autism spectrum disorders; genomic disorders; epilepsy

Special Issue Information

Dear Colleagues,

People with mental illness make up a large group of patients with increasing prevalence. Despite the extensive research, the etiology and molecular pathogenesis of psychiatric disorders are still poorly understood. It is clear from recent genomic studies that the genetic architecture of psychiatric disorders is highly polygenic and comprises all forms of genetic variations, such as common and rare SNVs, small insertions and deletions, de novo and inherited CNVs and chromosomal translocations. Moreover, epigenetic and environmental factors also play a crucial role in the development of these diseases.

Thanks to the new sophisticated technologies, such as whole-exome and whole-genome sequencing, more and more genomic data are available for psychiatric disorders. This gives promise to understand the biological mechanisms and develop novel therapeutic methods.

There are a number of rare disorders with psychiatric symptoms. The exploration of candidate genes and related biological pathways involved in the pathogenesis of these rare disorders is supposed to contribute to discovering the complex nature of mental disorders.

The aim of this Special Issue is to collect original and review articles which describe recent findings related to the genetic defects and pathomechanisms underlying mental illness. Pure clinical studies are out of the scope of this Special Issue; however, clinical submissions with biomolecular experiments are welcome.

Dr. Judit Bene
Dr. Hadzsiev Kinga
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mental disorders
  • schizophrenia
  • bipolar disorder
  • obsessive-compulsive disorder
  • major depression disorder
  • anxiety disorders
  • autism spectrum disorders
  • attention deficit hyperactivity disorder
  • rare disorders
  • WES
  • WGS
  • aCGH

Published Papers (1 paper)

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Research

Article
Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters
Int. J. Mol. Sci. 2021, 22(7), 3364; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22073364 - 25 Mar 2021
Cited by 1 | Viewed by 598
Abstract
RNA-seq has been a powerful method to detect the differentially expressed genes/long non-coding RNAs (lncRNAs) in schizophrenia (SCZ) patients; however, due to overfitting problems differentially expressed targets (DETs) cannot be used properly as biomarkers. This study used machine learning to reduce gene/non-coding RNA [...] Read more.
RNA-seq has been a powerful method to detect the differentially expressed genes/long non-coding RNAs (lncRNAs) in schizophrenia (SCZ) patients; however, due to overfitting problems differentially expressed targets (DETs) cannot be used properly as biomarkers. This study used machine learning to reduce gene/non-coding RNA features. Dorsolateral prefrontal cortex (dlpfc) RNA-seq data from 254 individuals was obtained from the CommonMind consortium. The average predictive accuracy for SCZ patients was 67% based on coding genes, and 96% based on long non-coding RNAs (lncRNAs). Machine learning is a powerful algorithm to reduce functional biomarkers in SCZ patients. The lncRNAs capture the characteristics of SCZ tissue more accurately than mRNA as the former regulate every level of gene expression, not limited to mRNA levels. Full article
(This article belongs to the Special Issue Advances of Psychiatric Genetics)
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