Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.8
5.6
Journals
IJMS
Special Issues
Network Medicine in Human Diseases
ijms-logo
Submit to IJMS Review for IJMS Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Network Medicine in Human Diseases

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: closed (20 December 2022) | Viewed by 8240

Special Issue Editors

Dr. Antonio Federico

E-Mail Website
Guest Editor
Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)–Faculty of Medicine and Health Technology, Tampere University, 33100 Tampere, Finland
Interests: network medicine; predictive pharmacology; human diseases; drug repositioning; systems biology; multi-omics data analysis; biomarker discovery; disease subtype discovery
Special Issues, Collections and Topics in MDPI journals
Dr. Giovanni Scala

E-Mail Website
Guest Editor
Department of Biology, University of Naples ‘Federico II’, 80126 Naples, Italy
Interests: systems biology; multi-omics data analysis; epigenetic; machine learning; biomarker discovery; disease subtype discovery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the post-genomic era, considerable efforts have been done to identify molecular alterations causing diseases. Advancements in omics technologies and the massive amounts of data currently available paved the way to the characterization of human diseases at a molecular resolution, leveraging the discovery of novel biomarkers, disease subtypes/endotypes and therapeutic targets. However, integrating these data into biologically meaningful results is still challenging.

Network science is nowadays at the forefront to meet this challenge and its potential has been widely demonstrated. The employment of network science principles to biomolecular applications enabled the full integration and exploitation of heterogeneous (multi-)omics datasets, uncovering key molecular mechanisms responsible for the onset of human diseases and facilitating the design of new and more effective treatments. In the context of human complex diseases, network medicine allows to simultaneously evaluate the molecular impairment underlying the disease under study and the mechanism-of-action of drugs, identifying novel therapeutic targets for a certain disease.

In this context, drug treatments on certain patients often show limited efficacy. The molecular heterogeneity in patients affected by complex diseases hampers the design of optimal treatment regimens, while some patients become unresponsive to existing therapies. Modelling drug response using network-based predictions can lead to accurate and robust predictions in terms of druggability of biological systems, enabling the design of innovative and effective treatments.

For these reasons, network medicine will arguably be the instrument to tackle the complexity behind human pathological phenotypes, improving current treatments for curable diseases and finding new therapies for untreatable ones.

Dr. Antonio Federico
Dr. Giovanni Scala
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. 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

  • network science
  • network medicine
  • systems biology
  • multi-omics data analysis
  • human diseases
  • drug repositioning
  • predictive pharmacology

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

16 pages, 41816 KiB  
Article
Finding miRNA–RNA Network Biomarkers for Predicting Metastasis and Prognosis in Cancer
by Seokwoo Lee, Myounghoon Cho, Byungkyu Park and Kyungsook Han
Int. J. Mol. Sci. 2023, 24(5), 5052; https://doi.org/10.3390/ijms24055052 - 6 Mar 2023
Viewed by 1511
Abstract
Despite remarkable progress in cancer research and treatment over the past decades, cancer ranks as a leading cause of death worldwide. In particular, metastasis is the major cause of cancer deaths. After an extensive analysis of miRNAs and RNAs in tumor tissue samples, [...] Read more.
Despite remarkable progress in cancer research and treatment over the past decades, cancer ranks as a leading cause of death worldwide. In particular, metastasis is the major cause of cancer deaths. After an extensive analysis of miRNAs and RNAs in tumor tissue samples, we derived miRNA–RNA pairs with substantially different correlations from those in normal tissue samples. Using the differential miRNA–RNA correlations, we constructed models for predicting metastasis. A comparison of our model to other models with the same data sets of solid cancer showed that our model is much better than the others in both lymph node metastasis and distant metastasis. The miRNA–RNA correlations were also used in finding prognostic network biomarkers in cancer patients. The results of our study showed that miRNA–RNA correlations and networks consisting of miRNA–RNA pairs were more powerful in predicting prognosis as well as metastasis. Our method and the biomarkers obtained using the method will be useful for predicting metastasis and prognosis, which in turn will help select treatment options for cancer patients and targets of anti-cancer drug discovery. Full article
(This article belongs to the Special Issue Network Medicine in Human Diseases)
►▼ Show Figures

Figure 1

22 pages, 1220 KiB  
Article
Integrated Data Analysis Uncovers New COVID-19 Related Genes and Potential Drug Re-Purposing Candidates
by Alexandros Xenos, Noël Malod-Dognin, Carme Zambrana and Nataša Pržulj
Int. J. Mol. Sci. 2023, 24(2), 1431; https://doi.org/10.3390/ijms24021431 - 11 Jan 2023
Cited by 2 | Viewed by 2034
Abstract
The COVID-19 pandemic is an acute and rapidly evolving global health crisis. To better understand this disease’s molecular basis and design therapeutic strategies, we built upon the recently proposed concept of an integrated cell, iCell, fusing three omics, tissue-specific human molecular interaction networks. [...] Read more.
The COVID-19 pandemic is an acute and rapidly evolving global health crisis. To better understand this disease’s molecular basis and design therapeutic strategies, we built upon the recently proposed concept of an integrated cell, iCell, fusing three omics, tissue-specific human molecular interaction networks. We applied this methodology to construct infected and control iCells using gene expression data from patient samples and three cell lines. We found large differences between patient-based and cell line-based iCells (both infected and control), suggesting that cell lines are ill-suited to studying this disease. We compared patient-based infected and control iCells and uncovered genes whose functioning (wiring patterns in iCells) is altered by the disease. We validated in the literature that 18 out of the top 20 of the most rewired genes are indeed COVID-19-related. Since only three of these genes are targets of approved drugs, we applied another data fusion step to predict drugs for re-purposing. We confirmed with molecular docking that the predicted drugs can bind to their predicted targets. Our most interesting prediction is artenimol, an antimalarial agent targeting ZFP62, one of our newly identified COVID-19-related genes. This drug is a derivative of artemisinin drugs that are already under clinical investigation for their potential role in the treatment of COVID-19. Our results demonstrate further applicability of the iCell framework for integrative comparative studies of human diseases. Full article
(This article belongs to the Special Issue Network Medicine in Human Diseases)
►▼ Show Figures

Figure 1

16 pages, 1189 KiB  
Article
A Graph Neural Network Approach for the Analysis of siRNA-Target Biological Networks
by Massimo La Rosa, Antonino Fiannaca, Laura La Paglia and Alfonso Urso
Int. J. Mol. Sci. 2022, 23(22), 14211; https://doi.org/10.3390/ijms232214211 - 17 Nov 2022
Cited by 6 | Viewed by 2306
Abstract
Many biological systems are characterised by biological entities, as well as their relationships. These interaction networks can be modelled as graphs, with nodes representing bio-entities, such as molecules, and edges representing relations among them, such as interactions. Due to the current availability of [...] Read more.
Many biological systems are characterised by biological entities, as well as their relationships. These interaction networks can be modelled as graphs, with nodes representing bio-entities, such as molecules, and edges representing relations among them, such as interactions. Due to the current availability of a huge amount of biological data, it is very important to consider in silico analysis methods based on, for example, machine learning, that could take advantage of the inner graph structure of the data in order to improve the quality of the results. In this scenario, graph neural networks (GNNs) are recent computational approaches that directly deal with graph-structured data. In this paper, we present a GNN network for the analysis of siRNA–mRNA interaction networks. siRNAs, in fact, are small RNA molecules that are able to bind to target genes and silence them. These events make siRNAs key molecules as RNA interference agents in many biological interaction networks related to severe diseases such as cancer. In particular, our GNN approach allows for the prediction of the siRNA efficacy, which measures the siRNA’s ability to bind and silence a gene target. Tested on benchmark datasets, our proposed method overcomes other machine learning algorithms, including the state-of-the-art predictor based on the convolutional neural network, reaching a Pearson correlation coefficient of approximately 73.6%. Finally, we proposed a case study where the efficacy of a set of siRNAs is predicted for a gene of interest. To the best of our knowledge, GNNs were used for the first time in this scenario. Full article
(This article belongs to the Special Issue Network Medicine in Human Diseases)
►▼ Show Figures

Figure 1

Review

Jump to: Research

12 pages, 1370 KiB  
Review
The Intertwined Role of 8-oxodG and G4 in Transcription Regulation
by Francesca Gorini, Susanna Ambrosio, Luigi Lania, Barbara Majello and Stefano Amente
Int. J. Mol. Sci. 2023, 24(3), 2031; https://doi.org/10.3390/ijms24032031 - 19 Jan 2023
Cited by 4 | Viewed by 1754
Abstract
The guanine base in nucleic acids is, among the other bases, the most susceptible to being converted into 8-Oxo-7,8-dihydro-2′-deoxyguanosine (8-oxodG) when exposed to reactive oxygen species. In double-helix DNA, 8-oxodG can pair with adenine; hence, it may cause a G > T (C [...] Read more.
The guanine base in nucleic acids is, among the other bases, the most susceptible to being converted into 8-Oxo-7,8-dihydro-2′-deoxyguanosine (8-oxodG) when exposed to reactive oxygen species. In double-helix DNA, 8-oxodG can pair with adenine; hence, it may cause a G > T (C > A) mutation; it is frequently referred to as a form of DNA damage and promptly corrected by DNA repair mechanisms. Moreover, 8-oxodG has recently been redefined as an epigenetic factor that impacts transcriptional regulatory elements and other epigenetic modifications. It has been proposed that 8-oxodG exerts epigenetic control through interplay with the G-quadruplex (G4), a non-canonical DNA structure, in transcription regulatory regions. In this review, we focused on the epigenetic roles of 8-oxodG and the G4 and explored their interplay at the genomic level. Full article
(This article belongs to the Special Issue Network Medicine in Human Diseases)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop