Topic Editors

Prof. Dr. Zhu-Hong You
School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
Dr. Yangming Li
Department of Electrical and Computer Engineering Technology, Rochester Institute of Technology, Rochester, NY 14623, USA
Dr. Haicheng Yi
School of Computer Science and Engineering, Nangyang Technological University, Singapore, Singapore

Intelligent Computing Unlocks the Molecular Code of Complex Diseases

Abstract submission deadline
31 March 2023
Manuscript submission deadline
30 April 2023
Viewed by
3960

Topic Information

Dear Colleagues,

A key aim of post-genomic biomedical research is to systematically unlock the molecular codes of complex diseases. Various molecules and their coordination comprise the basis of life activities, and the perturbations and or disorders of these associations lead to complex diseases. For instance, proteins directly undertake fundamental life activities, chemical compounds regulate metabolism, and multiple non-coding RNA also play a role in cellular development and tumorigenesis. Intelligence computing, represented by artificial intelligence, deep learning, and data analysis, etc., is very promising for accelerating related research. The aim of this Topic is to report on cutting-edge advances in intelligent computing approaches for the discovery of molecular mechanisms and the development of drugs for complex diseases. We are especially interested in research on molecular feature representation, genomics, proteomics data generation or integration, intelligence analysis algorithms for multi-omics data, single-cell RNA sequencing data analysis, drug combination, drug repositioning, evolutionary algorithms, parallel and cloud computing, the prediction of molecular function and structure, and applications of intelligence computing for personalized medicine. Potential topics include, but are not limited to: Sequence analysis; Multi-omics data modeling and analysis; scRNA-seq data analysis; Drug combination and repositioning; Molecular feature learning; ncRNA function annotation; Graph learning and reasoning; Molecular structure and function prediction; Systems biology and computational biology.

Prof. Dr. Zhu-Hong You
Dr. Yangming Li
Dr. Haicheng Yi
Topic Editors

Keywords

  • bioinformatics and computational biology
  • intelligence computing for healthcare
  • multi-omics data analysis
  • drug discovery
  • non-coding RNA–disease association

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Biomolecules
biomolecules
6.064 5.7 2011 15.8 Days 2100 CHF Submit
BioTech
biotech
- 5.2 2012 20.9 Days 1400 CHF Submit
Genes
genes
4.141 5.0 2010 17.1 Days 2400 CHF Submit
Non-Coding RNA
ncrna
- 10.1 2015 14.6 Days 1600 CHF Submit
Proteomes
proteomes
- 5.9 2013 20.7 Days 1600 CHF Submit

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Published Papers (3 papers)

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Article
Identification of Two Exosomal miRNAs in Circulating Blood of Cancer Patients by Using Integrative Transcriptome and Network Analysis
Non-Coding RNA 2022, 8(3), 33; https://doi.org/10.3390/ncrna8030033 - 12 May 2022
Abstract
Exosomes carry molecules of great biological and clinical interest, such as miRNAs. The contents of exosomes vary between healthy controls and cancer patients. Therefore, miRNAs and other molecules transported in exosomes are considered a potential source of diagnostic and prognostic biomarkers in cancer. [...] Read more.
Exosomes carry molecules of great biological and clinical interest, such as miRNAs. The contents of exosomes vary between healthy controls and cancer patients. Therefore, miRNAs and other molecules transported in exosomes are considered a potential source of diagnostic and prognostic biomarkers in cancer. Many miRNAs have been detected in recent years. Consequently, a substantial amount of miRNA-related data comparing patients and healthy individuals is available, which contributes to a better understanding of the initiation, development, malignancy, and metastasis of cancer using non-invasive sampling procedures. However, a re-analysis of available ncRNA data is rare. This study used available data about miRNAs in exosomes comparing healthy individuals and cancer patients to identify possible global changes related to the presence of cancer. A robust transcriptomic analysis identified two common miRNAs (miR-495-3p and miR-543) deregulated in five cancer datasets. They had already been implicated in different cancers but not reported in exosomes circulating in blood. The study also examined their target genes and the implications of these genes for functional processes. Full article
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Article
Network Theoretical Approach to Explore Factors Affecting Signal Propagation and Stability in Dementia’s Protein-Protein Interaction Network
Biomolecules 2022, 12(3), 451; https://doi.org/10.3390/biom12030451 - 15 Mar 2022
Abstract
Dementia—a syndrome affecting human cognition—is a major public health concern given to its rising prevalence worldwide. Though multiple research studies have analyzed disorders such as Alzheimer’s disease and Frontotemporal dementia using a systems biology approach, a similar approach to dementia syndrome as a [...] Read more.
Dementia—a syndrome affecting human cognition—is a major public health concern given to its rising prevalence worldwide. Though multiple research studies have analyzed disorders such as Alzheimer’s disease and Frontotemporal dementia using a systems biology approach, a similar approach to dementia syndrome as a whole is required. In this study, we try to find the high-impact core regulating processes and factors involved in dementia’s protein–protein interaction network. We also explore various aspects related to its stability and signal propagation. Using gene interaction databases such as STRING and GeneMANIA, a principal dementia network (PDN) consisting of 881 genes and 59,085 interactions was achieved. It was assortative in nature with hierarchical, scale-free topology enriched in various gene ontology (GO) categories and KEGG pathways, such as negative and positive regulation of apoptotic processes, macroautophagy, aging, response to drug, protein binding, etc. Using a clustering algorithm (Louvain method of modularity maximization) iteratively, we found a number of communities at different levels of hierarchy in PDN consisting of 95 “motif-localized hubs”, out of which, 7 were present at deepest level and hence were key regulators (KRs) of PDN (HSP90AA1, HSP90AB1, EGFR, FYN, JUN, CELF2 and CTNNA3). In order to explore aspects of network’s resilience, a knockout (of motif-localized hubs) experiment was carried out. It changed the network’s topology from a hierarchal scale-free topology to scale-free, where independent clusters exhibited greater control. Additionally, network experiments on interaction of druggable genome and motif-localized hubs were carried out where UBC, EGFR, APP, CTNNB1, NTRK1, FN1, HSP90AA1, MDM2, VCP, CTNNA1 and GRB2 were identified as hubs in the resultant network (RN). We finally concluded that stability and resilience of PDN highly relies on motif-localized hubs (especially those present at deeper levels), making them important therapeutic intervention candidates. HSP90AA1, involved in heat shock response (and its master regulator, i.e., HSF1), and EGFR are most important genes in pathology of dementia apart from KRs, given their presence as KRs as well as hubs in RN. Full article
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Article
RRM2 Alleviates Doxorubicin-Induced Cardiotoxicity through the AKT/mTOR Signaling Pathway
Biomolecules 2022, 12(2), 299; https://doi.org/10.3390/biom12020299 - 12 Feb 2022
Cited by 1
Abstract
Doxorubicin (DOX) is an effective chemotherapeutic agent that plays an unparalleled role in cancer treatment. However, its serious dose-dependent cardiotoxicity, which eventually contributes to irreversible heart failure, has greatly limited the widespread clinical application of DOX. A previous study has demonstrated that the [...] Read more.
Doxorubicin (DOX) is an effective chemotherapeutic agent that plays an unparalleled role in cancer treatment. However, its serious dose-dependent cardiotoxicity, which eventually contributes to irreversible heart failure, has greatly limited the widespread clinical application of DOX. A previous study has demonstrated that the ribonucleotide reductase M2 subunit (RRM2) exerts salutary effects on promoting proliferation and inhibiting apoptosis and autophagy. However, the specific function of RRM2 in DOX-induced cardiotoxicity is yet to be determined. This study aimed to elucidate the role and potential mechanism of RRM2 on DOX-induced cardiotoxicity by investigating neonatal primary cardiomyocytes and mice treated with DOX. Subsequently, the results indicated that RRM2 expression was significantly reduced in mice hearts and primary cardiomyocytes. Apoptosis and autophagy-related proteins, such as cleaved-Caspase3 (C-Caspase3), LC3B, and beclin1, were distinctly upregulated. Additionally, RRM2 deficiency led to increased autophagy and apoptosis in cells. RRM2 overexpression, on the contrary, alleviated DOX-induced cardiotoxicity in vivo and in vitro. Consistently, DIDOX, an inhibitor of RRM2, attenuated the protective effect of RRM2. Mechanistically, we found that AKT/mTOR inhibitors could reverse the function of RRM2 overexpression on DOX-induced autophagy and apoptosis, which means that RRM2 could have regulated DOX-induced cardiotoxicity through the AKT/mTOR signaling pathway. In conclusion, our experiment established that RRM2 could be a potential treatment in reversing DOX-induced cardiac dysfunction. Full article
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