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Human Health Monitoring Using Emerging Technologies: Towards Proper Usage of Genomics and Epigenetics in Molecular and Bio-Signaling Data

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 7266

Special Issue Editors

Prof. Dr. Mohammad Amjad Kamal

grade E-Mail Website
Guest Editor
1. Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
2. King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
3. Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh
4. Enzymoics, 7 Peterlee Place, Hebersham, NSW 2770, Australia
5. Novel Global Community Educational Foundation, Hebersham, Australia
Interests: biochemistry; neuroscience; enzymology; toxicology; metabolomics; nanomedicines; manual lymph drainage and miRNA; leadership in managing staff performance and chaplaincy
Special Issues, Collections and Topics in MDPI journals
Dr. Md Belal Bin Heyat

E-Mail Website
Guest Editor
1. IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
2. Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
Interests: medical machine learning; biosignal processing; cardiac disease; neurological disorders
Prof. Dr. Atif Amin Baig

E-Mail Website
Guest Editor
Faculty of Medicine, Universiti Sultan Zainal Abidin, Medicine Campus, Kuala, Terengganu, Malaysia
Interests: human physiology; treatment; diabetic; yoga
Prof. Dr. M.A. Ansari

E-Mail Website
Guest Editor
School of Engineering, Gautam Buddha University, Gr. Noida, India
Interests: medical machine learning; biosignal processing; diabetes; neurological disorders

Special Issue Information

Dear Colleagues,

Health monitoring of a complex human body organization is necessary to deal with vital physiological and pathological changes. In the current era of the Industrial Revolution 4.0, where digitalization has been overtaking all fields of science and medicine globally, there is a growing challenge when it comes to monitoring human health under interconnected fabrics of anatomy, physiology, biochemistry, genomics, epigenetics, and artificial intelligence due to the many social implications of health monitoring devices. Current inventions and innovations focusing on wearable wireless sensors have been helping toward curation, collection, and association at a big data scale. However, our lack of understanding and data for precision medicine at the genomic and epigenetic level means that we need to integrate big data for predictions, estimates, and treatment of underlying causes of different disorders and diseases in humans. Complementing the current emerging techniques with machine learning, deep learning, internet sources, blockchain, and quantum technology aligned with genomics and epigenetics at the molecular level could help toward a specific, better understanding of biomolecular signaling involved in monitoring health metabolomics. The subtopics to be covered in this Special Issue include but are not limited to:

  • Health monitoring systems;
  • Wearable devices;
  • Molecular signaling;
  • Biosignal and image processing;
  • Psychoneurological human behavior such as stress, depression, anxiety, and fatigue;
  • Arrhythmia, stroke, blood pressure, congenital heart disease, heart muscle disease, and pericardial disease;
  • mental disorder, sleep disorder, motor imagery, Parkinson, Alzheimer, and schizophrenia;
  • Diabetes, addiction, COVID-19, and encephalitis;
  • Detection, localization, prediction, and treatment;
  • ECG, EKG, EEG, and EOG;
  • MRI and fMRI;
  • Machine learning, deep learning, blockchain, computer vision;
  • Quantum technology;
  • Yoga

Prof. Dr. Mohammad Amjad Kamal
Dr. Md Belal Bin Heyat
Prof. Dr. Atif Amin Baig
Prof. Dr. M.A. Ansari
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. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Published Papers (2 papers)

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Research

13 pages, 10192 KiB  
Article
Decoding the Impact of Genetic Variants in Gastric Cancer Patients Based on High-Dimensional Copy Number Variation Data Using Next-Generation Knowledge Discovery Methods
by Fehmida Bibi, Peter Natesan Pushparaj, Muhammad Imran Naseer, Muhammad Yasir and Esam Ibraheem Azhar
Appl. Sci. 2022, 12(19), 10053; https://doi.org/10.3390/app121910053 - 6 Oct 2022
Viewed by 1760
Abstract
Objectives: Despite a reduction in the incidence and mortality rates of gastric cancer (GC), it remains the fifth most frequently diagnosed malignancy globally. A better understanding of the regulatory mechanisms involved in the progression and development of GC is important for developing novel [...] Read more.
Objectives: Despite a reduction in the incidence and mortality rates of gastric cancer (GC), it remains the fifth most frequently diagnosed malignancy globally. A better understanding of the regulatory mechanisms involved in the progression and development of GC is important for developing novel targeted approaches for treatment. We aimed to identify a set of differentially regulated pathways and cellular, molecular, and physiological system development and functions in GC patients infected with H. pylori infection based on copy number variation (CNV) data using next-generation knowledge discovery (NGKD) methods. Methods: In this study, we used our previous CNV data derived from tissue samples from GC patients (n = 33) and normal gastric samples (n = 15) by the comparative genome hybridization (CGH) method using Illumina HumanOmni1-Quad v.1.0 BeadChip (Zenodo Accession No: 1346283). The variant effects analysis of genetic gain or loss of function in GC was conducted using Ingenuity Pathway Analysis (IPA) software. In addition, in silico validation was performed with iPathwayGuide software using high-throughput RNA sequencing (RNAseq) data (GSE83088) from GC patients. Results: We observed 213 unique CNVs in the control group, 420 unique CNVs in the GC group, and 225 common variants. We found that cancer, gastrointestinal diseases, and organismal injury and abnormalities were the three diseases or disorders that were most affected in the GC group. We also identified that the programmed cell death ligand 1 (PD-L1) cancer immunotherapy pathway, T-cell apoptosis, T-cell exhaustion, and Type 1 regulatory T-cell (Tr1 cells) specialization were dysregulated in GC patients. RNAseq data from GC patients showed that the PD-1/PD-L1 pathway was significantly upregulated in GC samples compared with controls. Conclusions: In conclusion, in the present study, we decoded differentially impacted GC-specific diseases and biological functions and pathways based on CNV data using NGKD methods that can be adopted to design personalized therapeutic approaches for patients with GC in a typical clinical milieu. Full article
(This article belongs to the Special Issue Human Health Monitoring Using Emerging Technologies: Towards Proper Usage of Genomics and Epigenetics in Molecular and Bio-Signaling Data)
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16 pages, 1768 KiB  
Article
Recognition of mRNA N4 Acetylcytidine (ac4C) by Using Non-Deep vs. Deep Learning
by Muhammad Shahid Iqbal, Rashid Abbasi, Md Belal Bin Heyat, Faijan Akhtar, Asmaa Sayed Abdelgeliel, Sarah Albogami, Eman Fayad and Muhammad Atif Iqbal
Appl. Sci. 2022, 12(3), 1344; https://doi.org/10.3390/app12031344 - 27 Jan 2022
Cited by 22 | Viewed by 3795
Abstract
Deep learning models have been successfully applied in a wide range of fields. The creation of a deep learning framework for analyzing high-performance sequence data have piqued the research community’s interest. N4 acetylcytidine (ac4C) is a post-transcriptional modification in mRNA, is an mRNA [...] Read more.
Deep learning models have been successfully applied in a wide range of fields. The creation of a deep learning framework for analyzing high-performance sequence data have piqued the research community’s interest. N4 acetylcytidine (ac4C) is a post-transcriptional modification in mRNA, is an mRNA component that plays an important role in mRNA stability control and translation. The ac4C method of mRNA changes is still not simple, time consuming, or cost effective for conventional laboratory experiments. As a result, we developed DL-ac4C, a CNN-based deep learning model for ac4C recognition. In the alternative scenario, the model families are well-suited to working in large datasets with a large number of available samples, especially in biological domains. In this study, the DL-ac4C method (deep learning) is compared to non-deep learning (machine learning) methods, regression, and support vector machine. The results show that DL-ac4C is more advanced than previously used approaches. The proposed model improves the accuracy recall area by 9.6 percent and 9.8 percent, respectively, for cross-validation and independent tests. More nuanced methods of incorporating prior bio-logical knowledge into the estimation procedure of deep learning models are required to achieve better results in terms of predictive efficiency and cost-effectiveness. Based on an experiment’s acetylated dataset, the DL-ac4C sequence-based predictor for acetylation sites in mRNA can predict whether query sequences have potential acetylation motifs. Full article
(This article belongs to the Special Issue Human Health Monitoring Using Emerging Technologies: Towards Proper Usage of Genomics and Epigenetics in Molecular and Bio-Signaling Data)
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