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Symmetry
Special Issues
Biological Network and Its Symmetric Applications in Biomedicine
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Biological Network and Its Symmetric Applications in Biomedicine

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Life Sciences".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 4252

Special Issue Editors

Dr. Wenying Yan

E-Mail Website
Guest Editor
1. Suzhou Medical College of Soochow University, Suzhou, China
2. Center for Systems Biology, Soochow University, Suzhou, China
Interests: bioinformatics; systems biology; biomedical informatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guang Hu

E-Mail Website
Guest Editor
Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215006, Jiangsu, China
Interests: protein science; network analysis

Special Issue Information

Dear Colleagues,

Symmetry is one of the most common properties in biological systems and biological network to keep the systems stability. However, some biological events especially the symmetry breaking events such as gene mutations, gene abnormal expression, protein miss-folding, etc. will lead to the perturbation to the systems, for example, human disease occurrence and development.

In recently decades, network methods including centrality-based method and perturbation-based method have been used to investigate the effects of symmetry and symmetry breaking on the biological systems. The aim of this Special Issue is to highlight and overview the recent advances in different level biological network methods and applications in different multidisciplinary areas. We are soliciting contributions (comprehensive reviews on general areas, mini reviews on specialized subjects, research work, short communications, technical notes) covering a broad range of topics on biological network and its application, including (though not limited to) the following:

  • Network-based methods for omics data or omics data integration.
  • Network-based methods for biological molecules structural symmetry and asymmetry.
  • Network-based methods for studying biological molecules related with human diseases.
  • Novel software tools and efficient algorithms for network models construction, analysis and visualization.

Submit your paper and select the Journal “Symmetry” and the Special Issue “Biological Network and Its Symmetric Applications in Biomedicine” via: MDPI submission system. Please contact the journal editor Ida Li. Our papers will be published on a rolling basis and we will be pleased to receive your submission once you have finished it.

Prof. Dr. Wenying Yan
Prof. Dr. Guang Hu
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 submissions that pass pre-check are 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. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). 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.

Published Papers (2 papers)

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Research

14 pages, 4081 KiB  
Article
Identification of Novel Drugs Targeting Cell Cycle Regulators for the Treatment of High-Grade Serous Ovarian Cancer via Integrated Bioinformatics Analysis
by Yuanchun Zhao, Jiachen Zuo, Yiming Shen, Donghui Yan, Jiajia Chen and Xin Qi
Symmetry 2022, 14(7), 1403; https://doi.org/10.3390/sym14071403 - 08 Jul 2022
Cited by 1 | Viewed by 1499
Abstract
High-grade serous ovarian carcinoma (HGSC), the most common and aggressive histological type of ovarian cancer, remains the leading cause of cancer-related deaths among females. It is important to develop novel drugs to improve the therapeutic outcomes of HGSC patients, thereby reducing their mortality. [...] Read more.
High-grade serous ovarian carcinoma (HGSC), the most common and aggressive histological type of ovarian cancer, remains the leading cause of cancer-related deaths among females. It is important to develop novel drugs to improve the therapeutic outcomes of HGSC patients, thereby reducing their mortality. Symmetry is one of the most important properties of the biological network, which determines the stability of a biological system. As aberrant gene expression is a critical symmetry-breaking event that perturbs the stability of biological networks and triggers tumor progression, we aim in this study to discover new candidate drugs and predict their targets for HGSC therapy based on differentially expressed genes involved in HGSC pathogenesis. Firstly, 98 up-regulated genes and 108 down-regulated genes were identified from three independent transcriptome datasets. Then, the small-molecule compounds PHA-793887, pidorubicine and lestaurtinib, which target cell-cycle-related processes, were identified as novel candidate drugs for HGSC treatment by adopting the connectivity map (CMap)-based drug repositioning approach. Furthermore, through a topological analysis of the protein–protein interaction network, cell cycle regulators CDK1, TOP2A and AURKA were identified as bottleneck nodes, and their expression patterns were validated at the mRNA and protein expression levels. Moreover, the results of molecular docking analysis showed that PHA-793887, pidorubicine and lestaurtinib had a strong binding affinity for CDK1, TOP2A and AURKA, respectively. Therefore, our study repositioned PHA-793887, pidorubicine and lestaurtinib, which can inhibit cell cycle regulators, as novel agents for HGSC treatment, thereby helping to optimize the therapeutic strategy for HGSC. Full article
(This article belongs to the Special Issue Biological Network and Its Symmetric Applications in Biomedicine)
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14 pages, 2464 KiB  
Article
Automated Detection of Sudden Cardiac Death by Discrete Wavelet Transform of Electrocardiogram Signal
by Manhong Shi, Hongjie Yu and Hongjie Wang
Symmetry 2022, 14(3), 571; https://doi.org/10.3390/sym14030571 - 14 Mar 2022
Cited by 4 | Viewed by 2015
Abstract
Sudden cardiac death (SCD) results in millions of deaths annually; as it is a fatal heart abnormality, early prediction of SCD could save peoples’ lives to the greatest extent. Symmetry and asymmetry play an important role in many fields. Electrocardiograms (ECG) as a [...] Read more.
Sudden cardiac death (SCD) results in millions of deaths annually; as it is a fatal heart abnormality, early prediction of SCD could save peoples’ lives to the greatest extent. Symmetry and asymmetry play an important role in many fields. Electrocardiograms (ECG) as a noninvasive process for acquiring the electrical activity of the heart, has both asymmetric and non-stationary characteristics; it is frequently employed to diagnose and evaluate the heart’s condition. In this work, we have detected SCD 14 min (separately for each one-minute interval) prior to its occurrence by analyzing ECG signals using discrete wavelet transform (DWT) and locality preserving projection (LPP). In the experiment, we have performed DWT on ECG signals to obtain coefficients, then LPP as a reduction methodology was used to cut down these obtained coefficients. Then, the acquired LPP features were ranked using various methods, including the T-test, Bhattacharyya, Wilcoxon, and entropy. At last, the highly ranked LPP features were subjected to decision tree, k-nearest neighbor (KNN), and support vector machine classifiers for distinguishing normal from SCD ECG signals. Our proposed technique has achieved a highest accuracy of 97.6% for the detection of SCD 14 min prior using the KNN classifier, compared to the existing works. Our proposed method is capable of predicting the people at risk of developing SCD 14 min before its onset, and, hence, clinicians would have enough time to provide treatment in intensive care units (ICU) for a subject at risk of SCD. Thus, this proposed technique as a useful tool can increase the survival rate of many cardiac patients. Full article
(This article belongs to the Special Issue Biological Network and Its Symmetric Applications in Biomedicine)
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