Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
5.5
Journals
Biomolecules
Special Issues
Big Data Analysis in Human Disease
share announcement

Big Data Analysis in Human Disease

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 6625

Special Issue Editor

Dr. Ben-Chang Shia

E-Mail Website
Guest Editor
1. Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City 242, Taiwan
2. Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242, Taiwan
Interests: bioinformatics; medical/health informatics; machine learning; data mining; artificial intelligence and applications

Special Issue Information

Dear Colleagues,

Public health provides an extremely wide variety of problems that can be tackled using computational and machine learning techniques. Health informatics comprises a spectrum of multidisciplinary fields that includes the study of the design, development, and application of computational techniques to improve healthcare. This in turn may result in the development of new applications for omics, drug discovery, vaccine design, biomarkers discovery, neurosciences, biomedical engineering, etc.

However, most of these big data sets present certain features that make analysis difficult. In this context, complex computational or statistical analysis tools are required to capture the complexity of the data and multi-label machine learning (ML) algorithms are used to find predictive models for these data about systems with multiple biological properties and multiple labels (drugs, proteins, cell lines, tissues, brain regions, organisms, populations, etc.).

Therefore, the use of all these computational techniques to process biomolecular or medical data becomes even more important if computational biomedical systems for translational and personalized medicine can be developed. Consequently, ML algorithms have to merge data from preclinical assays (as in ChEMBL databases) with data from clinical assays with personal data information. Consequently, in this new Special Issue we aim to provide a forum for the publication of technical aspects and new applications or results (software, databases, cheminformatic models, machine learning algorithms, and complex network tools),and for the discussion of the ethical and legal implications of these tools.

This Special Issue is open to relevant subject areas of healthcare/medical informatics. The keywords listed below provide an outline of some of the possible areas of interest.

Dr. Ben-Chang Shia
Guest Editor

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. Biomolecules 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 2700 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.

Keywords

  • big data
  • health informatics
  • medical informatics
  • machine learning
  • human disease
  • public health
  • neuroinformatics
  • biostatistics
  • artificial intelligence and applications

Published Papers (3 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

13 pages, 3688 KiB  
Article
Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image
by Samer Kais Jameel, Sezgin Aydin, Nebras H. Ghaeb, Jafar Majidpour, Tarik A. Rashid, Sinan Q. Salih and Poh Soon JosephNg
Biomolecules 2022, 12(12), 1888; https://doi.org/10.3390/biom12121888 - 16 Dec 2022
Cited by 10 | Viewed by 2078
Abstract
Corneal diseases are the most common eye disorders. Deep learning techniques are used to perform automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical [...] Read more.
Corneal diseases are the most common eye disorders. Deep learning techniques are used to perform automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical images using conditional generative adversarial networks (CGANs), is presented. It also illustrates how produced medical images may be utilized to enrich medical data, improve clinical decisions, and boost the performance of the conventional neural network (CNN) for medical image diagnosis. The study includes using corneal topography captured using a Pentacam device from patients with corneal diseases. The dataset contained 3448 different corneal images. Furthermore, it shows how an unbalanced dataset affects the performance of classifiers, where the data are balanced using the resampling approach. Finally, the results obtained from CNN networks trained on the balanced dataset are compared to those obtained from CNN networks trained on the imbalanced dataset. For performance, the system estimated the diagnosis accuracy, precision, and F1-score metrics. Lastly, some generated images were shown to an expert for evaluation and to see how well experts could identify the type of image and its condition. The expert recognized the image as useful for medical diagnosis and for determining the severity class according to the shape and values, by generating images based on real cases that could be used as new different stages of illness between healthy and unhealthy patients. Full article
(This article belongs to the Special Issue Big Data Analysis in Human Disease)
►▼ Show Figures

Figure 1

14 pages, 1015 KiB  
Article
Developing an Improved Survival Prediction Model for Disease Prognosis
by Zhanbo Chen and Qiufeng Wei
Biomolecules 2022, 12(12), 1751; https://doi.org/10.3390/biom12121751 - 25 Nov 2022
Cited by 1 | Viewed by 1380
Abstract
Machine learning has become an important research field in genetics and molecular biology. Survival analysis using machine learning can provide an important computed-aid clinical research scheme for evaluating tumor treatment options. However, the genomic features are high-dimensional, which limits the prediction performance of [...] Read more.
Machine learning has become an important research field in genetics and molecular biology. Survival analysis using machine learning can provide an important computed-aid clinical research scheme for evaluating tumor treatment options. However, the genomic features are high-dimensional, which limits the prediction performance of the survival learning model. Therefore, in this paper, we propose an improved survival prediction model using a deep forest and self-supervised learning. It uses a deep survival forest to perform adaptive learning of high-dimensional genomic data and ensure robustness. In addition, self-supervised learning, as a semi-supervised learning style, is designed to utilize unlabeled samples to improve model performance. Based on four cancer datasets from The Cancer Genome Atlas (TCGA), the experimental results show that our proposed method outperforms four advanced survival analysis methods in terms of the C-index and brier score. The developed prediction model will help doctors rethink patient characteristics’ relevance to survival time and personalize treatment decisions. Full article
(This article belongs to the Special Issue Big Data Analysis in Human Disease)
►▼ Show Figures

Figure 1

25 pages, 14153 KiB  
Article
EZH2 as a Prognostic Factor and Its Immune Implication with Molecular Characterization in Prostate Cancer: An Integrated Multi-Omics in Silico Analysis
by Tian-Qi Du, Ruifeng Liu, Qiuning Zhang, Hongtao Luo, Zhiqiang Liu, Shilong Sun and Xiaohu Wang
Biomolecules 2022, 12(11), 1617; https://doi.org/10.3390/biom12111617 - 02 Nov 2022
Cited by 1 | Viewed by 2462
Abstract
Prostate cancer (PCa) is a type of potentially fatal malignant tumor. Immunotherapy has shown a lot of potential for various types of solid tumors, but the benefits have been less impressive in PCa. Enhancer of zeste homolog 2 (EZH2) is one of the [...] Read more.
Prostate cancer (PCa) is a type of potentially fatal malignant tumor. Immunotherapy has shown a lot of potential for various types of solid tumors, but the benefits have been less impressive in PCa. Enhancer of zeste homolog 2 (EZH2) is one of the three core subunits of the polycomb repressive complex 2 that has histone methyltransferase activity, and the immune effects of EZH2 in PCa are still unclear. The purpose of this study was to explore the potential of EZH2 as a prognostic factor and an immune therapeutic biomarker for PCa, as well as the expression pattern and biological functions. All analyses in this study were based on publicly available databases, mainly containing Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), UCSCXenaShiny, and TISIDB. We performed differential expression analysis, developed a prognostic model, and explored potential associations between EZH2 and DNA methylation modifications, tumor microenvironment (TME), immune-related genes, tumor mutation burden (TMB), tumor neoantigen burden (TNB), and representative mismatch repair (MMR) genes. We also investigated the molecular and immunological characterizations of EZH2. Finally, we predicted immunotherapeutic responses based on EZH2 expression levels. We found that EZH2 was highly expressed in PCa, was associated with a poor prognosis, and may serve as an independent prognostic factor. EZH2 expression in PCa was associated with DNA methylation modifications, TME, immune-related genes, TMB, TNB, and MMR. By gene set enrichment analysis and gene set variation analysis, we found that multiple functions and pathways related to tumorigenesis, progression, and immune activation were enriched. Finally, we inferred that immunotherapy may be more effective for PCa patients with low EZH2 expression. In conclusion, our study showed that EZH2 could be a potentially efficient predictor of prognosis and immune response in PCa patients. Full article
(This article belongs to the Special Issue Big Data Analysis in Human Disease)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop