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

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

Summary

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.


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

Published Papers
  • 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
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