Special Issue "Data Science and Modeling in Biology, Health, and Medicine"

Submission Deadline: 31 March 2020 (closed)
Guest Editors
Prof. Ka-Chun Wong, City University of Hong Kong, Hong Kong SAR
Prof. Xiangtao Li, Northeast Normal University, China
Dr. Frederick Kin Hing Phoa, Academia Sinica, Taiwan


Since the 2010s, the high-throughput sequencing technologies such as Oxford Nanopore sequencing and other third-generation sequencing facilities have revolutionized the molecular biology research field. Such an advancement has propelled a multitude of downstream studies lead to significant impacts on biology, health, and medicine. However, such kind of new data is big, fast, and heterogeneous. It demands a new set of data science and modeling approaches in terms of computational scalability, complexity, and fault-tolerance. 

Therefore, we have initiated such a special issue on the data science and modeling in biology, health, and medicine in the hope that researchers can gather their works together in a single special issue for broad and deep impacts on multiple disciplines such as mathematical biology, bioinformatics, computational biology, health informatics, biomedical engineering, cancer informatics, translational medicine, and other related fields.

Bioinformatics; Computational Biology; Machine Learning; Data Science; Data Mining; Computational Intelligence; Natural Computing; Genetic Algorithm; Differential Evolution; Evolutionary Computation

Published Papers
  • Exploring the Abnormal Brain Regions and Abnormal Functional Connections in SZ by Multiple Hypothesis Testing Techniques
  • Abstract Schizophrenia (SZ) is one of the most common mental diseases. Its main characteristics are abnormal social behavior and inability to correctly understand real things. In recent years, the magnetic resonance imaging (MRI) technique has been popularly utilized to study SZ. However, it is still a great challenge to reveal the essential information contained in the MRI data. In this paper, we proposed a biomarker selection approach based on the multiple hypothesis testing techniques to explore the difference between SZ and healthy controls by using both functional and structural MRI data, in which biomarkers represent both abnormal brain functional connectivity and… More
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  • Fractional-Order Model for Multi-Drug Antimicrobial Resistance
  • Abstract Drug resistance is one of the most serious phenomena in financial, economic and medical terms. The present paper proposes and investigates a simple mathematical fractional-order model for the phenomenon of multi-drug antimicrobial resistance. The model describes the dynamics of the susceptible and three kinds of infected populations. The first class of the infected society responds to the first antimicrobial drug but resists to the second one. The second infected individuals react to the second antimicrobial drug but resist to the first one. The third class shows resistance to both of the two drugs. We formulate the model and associate it… More
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  • Effect of Absorption of Patch Antenna Signals on Increasing the Head Temperature
  • Abstract Every new generation of antennas is characterized by increased accuracy and faster transmission speeds. However, patch antennas have been known to damage human health. This type of antenna sends out electromagnetic waves that increase the temperature of the human head and prevent nerve strands from functioning properly. This paper examines the effect of the communication between the patch antenna and the brain on the head’s temperature by developing a hypothetical multi-input model that achieves more accurate results. These inputs are an individual’s blood and tissue, and the emission power of the antenna. These forces depend on the permeability and conductivity… More
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  • Discrete Circular Distributions with Applications to Shared Orthologs of Paired Circular Genomes
  • Abstract For structural comparisons of paired prokaryotic genomes, an important topic in synthetic and evolutionary biology, the locations of shared orthologous genes (henceforth orthologs) are observed as binned data. This and other data, e.g., wind directions recorded at monitoring sites and intensive care unit arrival times on the 24-hour clock, are counted in binned circular arcs, thus modeling them by discrete circular distributions (DCDs) is required. We propose a novel method to construct a DCD from a base continuous circular distribution (CCD). The probability mass function is defined to take the normalized values of the probability density function at some pre-fixed… More
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  • Growing and Pruning Based Deep Neural Networks Modeling for Effective Parkinson’s Disease Diagnosis
  • Abstract Parkinson’s disease is a serious disease that causes death. Recently, a new dataset has been introduced on this disease. The aim of this study is to improve the predictive performance of the model designed for Parkinson’s disease diagnosis. By and large, original DNN models were designed by using specific or random number of neurons and layers. This study analyzed the effects of parameters, i.e., neuron number and activation function on the model performance based on growing and pruning approach. In other words, this study addressed the optimum hidden layer and neuron numbers and ideal activation and optimization functions in order… More
  •   Views:831       Downloads:589        Download PDF