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Application of Information Theory in Biomedical Data Mining

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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".

Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 16991

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Dr. Ting Hu

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School of Computing, Queen's University, Kingston, ON K7L 2N8, Canada
Interests: evolutionary computing; bioinformatics; computational biology; artificial evolution; machine learning; complex networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

In this era of big data in biomedicine, we now have access to high-throughput, high-dimensional complex data collected to help to better understand the biology of living systems. The availability of more data does not, however, guarantee more knowledge, unless more advanced and powerful data analysis tools are developed to help us to mine the data and extract that knowledge. The high-dimensionality, heterogeneity, and complexity of biomedical big data renders many traditional statistical and computational methods obsolete and thus, the area of biomedical data mining calls for new algorithms and methods that embrace complexity.

Information theory originates from information science and was developed to quantify, store, and transmit information. Information theoretical measures have been used to quantify correlations and interactions of attributes in biomedical data mining and hold great potential.

In this Special Issue, we would like to feature a series of novel applications of information theoretical measures for biomedical data mining. We welcome any original articles relating to, but not limited to, the topics described herein.

Dr. Ting Hu
Guest Editor

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Keywords

information theory
entropy
mutual information
information gain
biomedicine
big data
data mining

Published Papers (5 papers)

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Research

24 pages, 2681 KiB

Open AccessArticle

Ensemble and Greedy Approach for the Reconstruction of Large Gene Co-Expression Networks

by Francisco Gómez-Vela, Fernando M. Delgado-Chaves, Domingo S. Rodríguez-Baena, Miguel García-Torres and Federico Divina

Entropy 2019, 21(12), 1139; https://doi.org/10.3390/e21121139 - 21 Nov 2019

Cited by 4 | Viewed by 3204

Abstract

Gene networks have become a powerful tool in the comprehensive analysis of gene expression. Due to the increasing amount of available data, computational methods for networks generation must deal with the so-called curse of dimensionality in the quest for the reliability of the obtained results. In this context, ensemble strategies have significantly improved the precision of results by combining different measures or methods. On the other hand, structure optimization techniques are also important in the reduction of the size of the networks, not only improving their topology but also keeping a positive prediction ratio. In this work, we present Ensemble and Greedy networks (EnGNet), a novel two-step method for gene networks inference. First, EnGNet uses an ensemble strategy for co-expression networks generation. Second, a greedy algorithm optimizes both the size and the topological features of the network. Not only do achieved results show that this method is able to obtain reliable networks, but also that it significantly improves topological features. Moreover, the usefulness of the method is proven by an application to a human dataset on post-traumatic stress disorder, revealing an innate immunity-mediated response to this pathology. These results are indicative of the method’s potential in the field of biomarkers discovery and characterization. Full article

(This article belongs to the Special Issue Application of Information Theory in Biomedical Data Mining)

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12 pages, 1934 KiB

Open AccessArticle

Identification of Denatured Biological Tissues Based on Time-Frequency Entropy and Refined Composite Multi-Scale Weighted Permutation Entropy during HIFU Treatment

by Bei Liu, Shengyou Qian and Weipeng Hu

Entropy 2019, 21(7), 666; https://doi.org/10.3390/e21070666 - 8 Jul 2019

Cited by 7 | Viewed by 2728

Abstract

Identification of denatured biological tissue is crucial to high intensity focused ultrasound (HIFU) treatment. It is not easy for intercepting ultrasonic scattered echo signals from HIFU treatment region. Therefore, this paper employed time-frequency entropy based on generalized S-transform (GST) to intercept ultrasonic echo signals. First, the time-frequency spectra of ultrasonic echo signal is obtained by GST, which is concentrated around the real instantaneous frequency of the signal. Then the time-frequency entropy is calculated based on time-frequency spectra. The experimental results indicate that the time-frequency entropy of ultrasonic echo signal will be abnormally high when ultrasonic signal travels across the boundary between normal region and treatment region in tissues. Ultrasonic scattered echo signals from treatment region can be intercepted by time-frequency entropy. In addition, the refined composite multi-scale weighted permutation entropy (RCMWPE) is proposed to evaluate the complexity of nonlinear time series. Comparing with multi-scale permutation entropy (MPE) and multi-scale weighted permutation entropy (MWPE), RCMWPE not only measures complexity of signal including amplitude information, but also improves the stability and reliability of multi-scale entropy. The RCMWPE and MPE are applied to 300 cases of actual ultrasonic scattered echo signals (including 150 cases in normal status and 150 cases in denatured status). It is found that the RCMWPE and MPE values of denatured tissues are higher than those of the normal tissues. Both RCMWPE and MPE can be used to distinguish normal tissues and denatured tissues. However, there are fewer feature points in the overlap region between RCMWPE of denatured tissues and normal tissues compared with MPE. The intra-class distance and the inter-class distance of RCMWPE are less and greater respectively than MPE. The difference between denatured tissues and normal tissues is more obvious when RCMWPE is used as the characteristic parameter. The results of this study will be helpful to guide doctors to obtain more accurate assessment of treatment effect during HIFU treatment. Full article

(This article belongs to the Special Issue Application of Information Theory in Biomedical Data Mining)

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12 pages, 2884 KiB

Open AccessArticle

Intra- and Inter-Modular Connectivity Alterations in the Brain Structural Network of Spinocerebellar Ataxia Type 3

by Chi-Wen Jao, Bing-Wen Soong, Tzu-Yun Wang, Hsiu-Mei Wu, Chia-Feng Lu, Po-Shan Wang and Yu-Te Wu

Entropy 2019, 21(3), 317; https://doi.org/10.3390/e21030317 - 23 Mar 2019

Cited by 9 | Viewed by 3312

Abstract

In addition to cerebellar degeneration symptoms, patients with spinocerebellar ataxia type 3 (SCA3) exhibit extensive involvements with damage in the prefrontal cortex. A network model has been proposed for investigating the structural organization and functional mechanisms of clinical brain disorders. For neural degenerative diseases, a cortical feature-based structural connectivity network can locate cortical atrophied regions and indicate how their connectivity and functions may change. The brain network of SCA3 has been minimally explored. In this study, we investigated this network by enrolling 48 patients with SCA3 and 48 healthy subjects. A novel three-dimensional fractal dimension-based network was proposed to detect differences in network parameters between the groups. Copula correlations and modular analysis were then employed to categorize and construct the structural networks. Patients with SCA3 exhibited significant lateralized atrophy in the left supratentorial regions and significantly lower modularity values. Their cerebellar regions were dissociated from higher-level brain networks, and demonstrated decreased intra-modular connectivity in all lobes, but increased inter-modular connectivity in the frontal and parietal lobes. Our results suggest that the brain networks of patients with SCA3 may be reorganized in these regions, with the introduction of certain compensatory mechanisms in the cerebral cortex to minimize their cognitive impairment syndrome. Full article

(This article belongs to the Special Issue Application of Information Theory in Biomedical Data Mining)

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17 pages, 14072 KiB

Open AccessArticle

A New Phylogenetic Inference Based on Genetic Attribute Reduction for Morphological Data

by Jun Feng, Zeyun Liu, Hongwei Feng, Richard F. E. Sutcliffe, Jianni Liu and Jian Han

Entropy 2019, 21(3), 313; https://doi.org/10.3390/e21030313 - 22 Mar 2019

Viewed by 3731

Abstract

To address the instability of phylogenetic trees in morphological datasets caused by missing values, we present a phylogenetic inference method based on a concept decision tree (CDT) in conjunction with attribute reduction. First, a reliable initial phylogenetic seed tree is created using a few species with relatively complete morphological information by using biologists’ prior knowledge or by applying existing tools such as MrBayes. Second, using a top-down data processing approach, we construct concept-sample templates by performing attribute reduction at each node in the initial phylogenetic seed tree. In this way, each node is turned into a decision point with multiple concept-sample templates, providing decision-making functions for grafting. Third, we apply a novel matching algorithm to evaluate the degree of similarity between the species’ attributes and their concept-sample templates and to determine the location of the species in the initial phylogenetic seed tree. In this manner, the phylogenetic tree is established step by step. We apply our algorithm to several datasets and compare it with the maximum parsimony, maximum likelihood, and Bayesian inference methods using the two evaluation criteria of accuracy and stability. The experimental results indicate that as the proportion of missing data increases, the accuracy of the CDT method remains at 86.5%, outperforming all other methods and producing a reliable phylogenetic tree. Full article

(This article belongs to the Special Issue Application of Information Theory in Biomedical Data Mining)

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12 pages, 1186 KiB

Open AccessArticle

Quality-Oriented Perceptual HEVC Based on the Spatiotemporal Saliency Detection Model

by Xiantao Jiang, Tian Song, Daqi Zhu, Takafumi Katayama and Lu Wang

Entropy 2019, 21(2), 165; https://doi.org/10.3390/e21020165 - 11 Feb 2019

Cited by 16 | Viewed by 3582

Abstract

Perceptual video coding (PVC) can provide a lower bitrate with the same visual quality compared with traditional H.265/high efficiency video coding (HEVC). In this work, a novel H.265/HEVC-compliant PVC framework is proposed based on the video saliency model. Firstly, both an effective and efficient spatiotemporal saliency model is used to generate a video saliency map. Secondly, a perceptual coding scheme is developed based on the saliency map. A saliency-based quantization control algorithm is proposed to reduce the bitrate. Finally, the simulation results demonstrate that the proposed perceptual coding scheme shows its superiority in objective and subjective tests, achieving up to a 9.46% bitrate reduction with negligible subjective and objective quality loss. The advantage of the proposed method is the high quality adapted for a high-definition video application. Full article

(This article belongs to the Special Issue Application of Information Theory in Biomedical Data Mining)

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