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Mathematics
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Big Data and Bioinformatics
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Big Data and Bioinformatics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematical Biology".

Deadline for manuscript submissions: 26 May 2025 | Viewed by 22884

Special Issue Editors

Dr. Haolan Zhang

E-Mail Website
Guest Editor
Ningbo Institute of Technology, Zhejiang University, Ningbo, China
Interests: data analytics; artificial intelligence; cognitive analytics; brain informatics
Dr. Sanghyuk Lee

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Xi’an 215123, China
Interests: control theory; data analysis; fuzzy set theory; robust controller design; energy optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Improving data processing efficiency has attracted much attention from various research communities. Researchers in related fields are facing challenges linked to data explosion, which demands enormous manpower for data processing. Artificial intelligence and intelligent systems offer efficient mechanisms that can significantly reduce the costs of processing large volumes of data and improve data processing quality, such as intelligent agent networks. Practical applications have been developed in different areas, including health informatics, financial data analysis, geographic systems, automated manufacturing processes, etc.

This Special Issue aims to gather experts and scholars from related fields to present and share their recent research on big data and bioinformatics. This issue covers state-of-the-art technology in the data processing and bioinformatics field as well as future trends and related issues in this field. This Special Issue covers (but is not limited to) several interesting topics in the areas of big data analytics, data mining and visualization, genome bioinformatics and computational biology, intelligent knowledge mining, data analysis on bio-related areas, brain informatics, and machine learning for big data.

Dr. Haolan Zhang
Dr. Sanghyuk Lee
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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 analytics
  • data mining and visualization
  • genome bioinformatics and computational biology
  • intelligent knowledge mining
  • data analysis on bio-related areas
  • brain informatics
  • machine learning for big data

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Published Papers (4 papers)

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Research

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15 pages, 3489 KiB  
Article
Identification of Vital Genes for NSCLC Integrating Mutual Information and Synergy
by Xiaobo Yang, Zhilong Mi, Qingcai He, Binghui Guo and Zhiming Zheng
Mathematics 2023, 11(6), 1460; https://doi.org/10.3390/math11061460 - 17 Mar 2023
Viewed by 1760
Abstract
Lung cancer, amongst the fast growing malignant tumors, has become the leading cause of cancer death, which deserves attention. From a prevention and treatment perspective, advances in screening, diagnosis, and treatment have driven a reduction in non-small-cell lung cancer (NSCLC) incidence and improved [...] Read more.
Lung cancer, amongst the fast growing malignant tumors, has become the leading cause of cancer death, which deserves attention. From a prevention and treatment perspective, advances in screening, diagnosis, and treatment have driven a reduction in non-small-cell lung cancer (NSCLC) incidence and improved patient outcomes. It is of benefit that the identification of key genetic markers contributes to the understanding of disease initiation and progression. In this work, information theoretical measures are proposed to determine the collaboration between genes and specific NSCLC samples. Top mutual information observes genes of high sample classification accuracy, such as STX11, S1PR1, TACC1, LRKK2, and SRPK1. In particular, diversity exists in different gender, histology, and smoking situations. Furthermore, leading synergy detects a high-accuracy combination of two ordinary individual genes, bringing a significant gain in accuracy. We note a strong synergistic effect of genes between COL1A2 and DCN, DCN and MMP2, and PDS5B and B3GNT8. Apart from that, RHOG is revealed to have quite a few functions in coordination with other genes. The results provide evidence for gene-targeted therapy as well as combined diagnosis in the context of NSCLC. Our approach can also be extended to find synergistic biomarkers associated with different diseases. Full article
(This article belongs to the Special Issue Big Data and Bioinformatics)
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20 pages, 573 KiB  
Article
On the Regulated Nuclear Transport of Incompletely Spliced mRNAs by HIV-Rev Protein: A Minimal Dynamic Model
by Jeffrey J. Ishizuka, Delaney A. Soble, Tiffany Y. Chang and Enrique Peacock-López
Mathematics 2022, 10(21), 3922; https://doi.org/10.3390/math10213922 - 22 Oct 2022
Viewed by 1735
Abstract
A kinetic model for the HIV-1 Rev protein is developed by drawing upon mechanistic information from the literature to formulate a set of differential equations modeling the behavior of Rev and its various associated factors over time. A set of results demonstrates the [...] Read more.
A kinetic model for the HIV-1 Rev protein is developed by drawing upon mechanistic information from the literature to formulate a set of differential equations modeling the behavior of Rev and its various associated factors over time. A set of results demonstrates the possibility of oscillations in the concentration of these factors. Finally, the results are analyzed, and future directions are discussed. Full article
(This article belongs to the Special Issue Big Data and Bioinformatics)
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Review

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26 pages, 1696 KiB  
Review
Recent Advances in Generative Adversarial Networks for Gene Expression Data: A Comprehensive Review
by Minhyeok Lee
Mathematics 2023, 11(14), 3055; https://doi.org/10.3390/math11143055 - 10 Jul 2023
Cited by 17 | Viewed by 10142
Abstract
The evolving field of generative artificial intelligence (GenAI), particularly generative deep learning, is revolutionizing a host of scientific and technological sectors. One of the pivotal innovations within this domain is the emergence of generative adversarial networks (GANs). These unique models have shown remarkable [...] Read more.
The evolving field of generative artificial intelligence (GenAI), particularly generative deep learning, is revolutionizing a host of scientific and technological sectors. One of the pivotal innovations within this domain is the emergence of generative adversarial networks (GANs). These unique models have shown remarkable capabilities in crafting synthetic data, closely emulating real-world distributions. Notably, their application to gene expression data systems is a fascinating and rapidly growing focus area. Restrictions related to ethical and logistical issues often limit the size, diversity, and data-gathering speed of gene expression data. Herein lies the potential of GANs, as they are capable of producing synthetic gene expression data, offering a potential solution to these limitations. This review provides a thorough analysis of the most recent advancements at this innovative crossroads of GANs and gene expression data, specifically during the period from 2019 to 2023. In the context of the fast-paced progress in deep learning technologies, accurate and inclusive reviews of current practices are critical to guiding subsequent research efforts, sharing knowledge, and catalyzing continual growth in the discipline. This review, through highlighting recent studies and seminal works, serves as a key resource for academics and professionals alike, aiding their journey through the compelling confluence of GANs and gene expression data systems. Full article
(This article belongs to the Special Issue Big Data and Bioinformatics)
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28 pages, 4079 KiB  
Review
A Survey on Big Data Technologies and Their Applications to the Metaverse: Past, Current and Future
by Haolan Zhang, Sanghyuk Lee, Yifan Lu, Xin Yu and Huanda Lu
Mathematics 2023, 11(1), 96; https://doi.org/10.3390/math11010096 - 26 Dec 2022
Cited by 22 | Viewed by 8175
Abstract
The development of big data technologies, which have been applied extensively in various areas, has become one of the key factors affecting modern society, especially in the virtual reality environment. This paper provides a comprehensive survey of the recent developments in big data [...] Read more.
The development of big data technologies, which have been applied extensively in various areas, has become one of the key factors affecting modern society, especially in the virtual reality environment. This paper provides a comprehensive survey of the recent developments in big data technologies, and their applications to virtual reality worlds, such as the Metaverse, virtual humans, and digital twins. The purpose of this survey was to explore several cutting-edge big data and virtual human modelling technologies, and to raise the issue of future trends in big data technologies and the Metaverse. This survey investigated the applications of big data technologies in several key areas—including e-health, transportation, and business and finance—and the main technologies adopted in the fast-growing virtual world sector, i.e., the Metaverse. Full article
(This article belongs to the Special Issue Big Data and Bioinformatics)
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