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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 (24 June 2022) | Viewed by 12966

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Special Issue Editor

Prof. Dr. Young-Rae Cho

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Guest Editor

Computer & Telecommunication Engineering Division,Yonsei University, Wonju 26493, Korea
Interests: bioinformatics; network biology; systems biology; data mining; ontologies

Special Issue Information

The systematic study of complex biological networks is a new paradigm for characterizing molecular functions at a large scale. Modeling and analysis of the inherent, dynamic, and structural behaviors of biological networks from a topological perspective is a primary issue in current research on bioinformatics and biomedical informatics. However, these tasks have been challenging because of the large scale and complex connectivity of the biological networks structured by big data. Typical examples include metabolic networks, gene regulatory networks, protein–protein interactions, drug–drug interactions, drug–target interactions, and drug–disease associations.

A wide-range of graph-theoretic computational techniques have been applied to the effective analysis of large-scale, complex biological networks. Moreover, integrative approaches with other big data would improve the reliability of network modelling and the accuracy of the network analysis. These studies will have great potential for various biomedical applications such as precision medicine and drug repositioning.

This Special Issue aims to provide a forum to discuss state-of-the-art approaches for biological or biomedical network analysis. Special attention will be placed on information-entropic approaches to complex systems. This Issue will also emphasize a comparison of the performance of alternative approaches. The topics of this issue are as follows.

Network modelling
Link prediction
Function prediction
Pathway discovery
Network dynamics and evolution
Entropy-based network analysis
Graph data mining algorithms
Biomedical applications of network analysis

This Special Issue will also include extended versions of high-quality papers on BIBM 2020 (IEEE International Conferences on Bioinformatics and Biomedicine).

Prof. Dr. Young-Rae Cho
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. Entropy 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 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

bioinformatics
biological networks
systems biology
network modelling
complex systems
dynamic systems

Published Papers (6 papers)

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Research

19 pages, 10977 KiB

Open AccessArticle

Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals

by Yingmei Qin, Ziyu Hu, Yi Chen, Jing Liu, Lijie Jiang, Yanqiu Che and Chunxiao Han

Entropy 2022, 24(8), 1093; https://doi.org/10.3390/e24081093 - 09 Aug 2022

Cited by 6 | Viewed by 1763

Abstract

Fatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver’s attention and vigilance, manifesting a fatigue effect. This paper proposes a means of revealing the effects of driving fatigue on the brain’s information processing abilities, from the aspect of a directed brain network based on electroencephalogram (EEG) source signals. Based on current source density (CSD) data derived from EEG signals using source analysis, a directed brain network for fatigue driving was constructed by using a directed transfer function. As driving time increased, the average clustering coefficient as well as the average path length gradually increased; meanwhile, global efficiency gradually decreased for most rhythms, suggesting that deep driving fatigue enhances the brain’s local information integration abilities while weakening its global abilities. Furthermore, causal flow analysis showed electrodes with significant differences between the awake state and the driving fatigue state, which were mainly distributed in several areas of the anterior and posterior regions, especially under the theta rhythm. It was also found that the ability of the anterior regions to receive information from the posterior regions became significantly worse in the driving fatigue state. These findings may provide a theoretical basis for revealing the underlying neural mechanisms of driving fatigue. Full article

(This article belongs to the Special Issue Networks and Systems in Bioinformatics)

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18 pages, 2143 KiB

Open AccessArticle

Inference of Molecular Regulatory Systems Using Statistical Path-Consistency Algorithm

by Yan Yan, Feng Jiang, Xinan Zhang and Tianhai Tian

Entropy 2022, 24(5), 693; https://doi.org/10.3390/e24050693 - 13 May 2022

Viewed by 1696

Abstract

One of the key challenges in systems biology and molecular sciences is how to infer regulatory relationships between genes and proteins using high-throughout omics datasets. Although a wide range of methods have been designed to reverse engineer the regulatory networks, recent studies show that the inferred network may depend on the variable order in the dataset. In this work, we develop a new algorithm, called the statistical path-consistency algorithm (SPCA), to solve the problem of the dependence of variable order. This method generates a number of different variable orders using random samples, and then infers a network by using the path-consistent algorithm based on each variable order. We propose measures to determine the edge weights using the corresponding edge weights in the inferred networks, and choose the edges with the largest weights as the putative regulations between genes or proteins. The developed method is rigorously assessed by the six benchmark networks in DREAM challenges, the mitogen-activated protein (MAP) kinase pathway, and a cancer-specific gene regulatory network. The inferred networks are compared with those obtained by using two up-to-date inference methods. The accuracy of the inferred networks shows that the developed method is effective for discovering molecular regulatory systems. Full article

(This article belongs to the Special Issue Networks and Systems in Bioinformatics)

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17 pages, 3244 KiB

Open AccessArticle

Local Integral Regression Network for Cell Nuclei Detection

by Xiao Zhou, Miao Gu and Zhen Cheng

Entropy 2021, 23(10), 1336; https://doi.org/10.3390/e23101336 - 14 Oct 2021

Cited by 2 | Viewed by 1513

Abstract

Nuclei detection is a fundamental task in the field of histopathology image analysis and remains challenging due to cellular heterogeneity. Recent studies explore convolutional neural networks to either isolate them with sophisticated boundaries (segmentation-based methods) or locate the centroids of the nuclei (counting-based approaches). Although these two methods have demonstrated superior success, their fully supervised training demands considerable and laborious pixel-wise annotations manually labeled by pathology experts. To alleviate such tedious effort and reduce the annotation cost, we propose a novel local integral regression network (LIRNet) that allows both fully and weakly supervised learning (FSL/WSL) frameworks for nuclei detection. Furthermore, the LIRNet can output an exquisite density map of nuclei, in which the localization of each nucleus is barely affected by the post-processing algorithms. The quantitative experimental results demonstrate that the FSL version of the LIRNet achieves a state-of-the-art performance compared to other counterparts. In addition, the WSL version has exhibited a competitive detection performance and an effortless data annotation that requires only 17.5% of the annotation effort. Full article

(This article belongs to the Special Issue Networks and Systems in Bioinformatics)

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21 pages, 4105 KiB

Open AccessArticle

Fatigue Detection with Spatial-Temporal Fusion Method on Covariance Manifolds of Electroencephalography

by Nan Zhao, Dawei Lu, Kechen Hou, Meifei Chen, Xiangyu Wei, Xiaowei Zhang and Bin Hu

Entropy 2021, 23(10), 1298; https://doi.org/10.3390/e23101298 - 30 Sep 2021

Cited by 3 | Viewed by 2034

Abstract

With the increasing pressure of current life, fatigue caused by high-pressure work has deeply affected people and even threatened their lives. In particular, fatigue driving has become a leading cause of traffic accidents and deaths. This paper investigates electroencephalography (EEG)-based fatigue detection for driving by mining the latent information through the spatial-temporal changes in the relations between EEG channels. First, EEG data are partitioned into several segments to calculate the covariance matrices of each segment, and then we feed these matrices into a recurrent neural network to obtain high-level temporal information. Second, the covariance matrices of whole signals are leveraged to extract two kinds of spatial features, which will be fused with temporal characteristics to obtain comprehensive spatial-temporal information. Experiments on an open benchmark showed that our method achieved an excellent classification accuracy of 93.834% and performed better than several novel methods. These experimental results indicate that our method enables better reliability and feasibility in the detection of fatigued driving. Full article

(This article belongs to the Special Issue Networks and Systems in Bioinformatics)

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14 pages, 339 KiB

Open AccessArticle

Entropy-Based Graph Clustering of PPI Networks for Predicting Overlapping Functional Modules of Proteins

by Hoyeon Jeong, Yoonbee Kim, Yi-Sue Jung, Dae Ryong Kang and Young-Rae Cho

Entropy 2021, 23(10), 1271; https://doi.org/10.3390/e23101271 - 28 Sep 2021

Cited by 1 | Viewed by 2230

Abstract

Functional modules can be predicted using genome-wide protein–protein interactions (PPIs) from a systematic perspective. Various graph clustering algorithms have been applied to PPI networks for this task. In particular, the detection of overlapping clusters is necessary because a protein is involved in multiple functions under different conditions. graph entropy (GE) is a novel metric to assess the quality of clusters in a large, complex network. In this study, the unweighted and weighted GE algorithm is evaluated to prove the validity of predicting function modules. To measure clustering accuracy, the clustering results are compared to protein complexes and Gene Ontology (GO) annotations as references. We demonstrate that the GE algorithm is more accurate in overlapping clusters than the other competitive methods. Moreover, we confirm the biological feasibility of the proteins that occur most frequently in the set of identified clusters. Finally, novel proteins for the additional annotation of GO terms are revealed. Full article

(This article belongs to the Special Issue Networks and Systems in Bioinformatics)

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18 pages, 2716 KiB

Open AccessArticle

Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression Data

by Hui Wen Nies, Mohd Saberi Mohamad, Zalmiyah Zakaria, Weng Howe Chan, Muhammad Akmal Remli and Yong Hui Nies

Entropy 2021, 23(9), 1232; https://doi.org/10.3390/e23091232 - 20 Sep 2021

Cited by 3 | Viewed by 2536

Abstract

Artificial intelligence in healthcare can potentially identify the probability of contracting a particular disease more accurately. There are five common molecular subtypes of breast cancer: luminal A, luminal B, basal, ERBB2, and normal-like. Previous investigations showed that pathway-based microarray analysis could help in the identification of prognostic markers from gene expressions. For example, directed random walk (DRW) can infer a greater reproducibility power of the pathway activity between two classes of samples with a higher classification accuracy. However, most of the existing methods (including DRW) ignored the characteristics of different cancer subtypes and considered all of the pathways to contribute equally to the analysis. Therefore, an enhanced DRW (eDRW+) is proposed to identify breast cancer prognostic markers from multiclass expression data. An improved weight strategy using one-way ANOVA (F-test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+. The experimental results show that the eDRW+ exceeds other methods in terms of AUC. Besides this, the eDRW+ identifies 294 gene markers and 45 pathway markers from the breast cancer datasets with better AUC. Therefore, the prognostic markers (pathway markers and gene markers) can identify drug targets and look for cancer subtypes with clinically distinct outcomes. Full article

(This article belongs to the Special Issue Networks and Systems in Bioinformatics)

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