Selected Papers from the 15th International Symposium on Bioinformatics Research and Applications (ISBRA)

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (15 September 2019) | Viewed by 10021

Special Issue Editors


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Guest Editor
Institute of Computing Technology, Chinese Academy of Sciences Beijing, Beijing 100864, China
Interests: bioinformatics; protein structure prediction; electron tomography; molecules high-performance computing
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Guest Editor
Computer Science and Engineering Department, University of Connecticut, Storrs, CT 06269, USA
Interests: bioinformatics; next-generation sequencing data analysis; metagenomics and metatranscriptomics; computational molecular epidemiology; immunogenomics
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Department of Computer Science and Engineering, College of Engineering, University of North Texas, Denton, TX 76203, USA
Interests: machine learning; bioinformatics; cloud computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The International Symposium on Bioinformatics Research and Applications (ISBRA) provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and computational biology and their applications. Submissions presenting original research are solicited in all areas of bioinformatics and computational biology, including the development of experimental or commercial systems. It will be hold in Technical University of Catalonia, Barcelona, Spain; June 3-6, 2019.

ISBRA has been successfully held, 14 times, since 2005. Several famous researchers have joined the conference committees. Excellent speakers from different countries will present their results. For all details, please see http://alan.cs.gsu.edu/isbra19/, where the full list of presenters is available.

Prof. Fa Zhang
Prof. Ion Mandoiu
Dr. Xuan Guo
Guest Editors

Manuscript Submission Information

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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. Genes 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

  • Biomarker discovery
  • High-performance bio-computing
  • Biomedical databases and data integration
  • Metagenomics
  • Biomedical text mining and ontologies
  • Molecular evolution
  • Biomolecular imaging
  • Molecular modelling and simulation
  • Comparative genomics
  • Next-generation sequencing data analysis
  • Computational genetic epidemiology
  • Pattern discovery and classification
  • Computational proteomics
  • Population genetics
  • Data mining and visualization
  • Software tools and applications
  • Gene expression analysis
  • Structural biology
  • Genome analysis
  • Systems biology

Published Papers (3 papers)

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Research

19 pages, 2459 KiB  
Article
Network Embedding the Protein–Protein Interaction Network for Human Essential Genes Identification
by Wei Dai, Qi Chang, Wei Peng, Jiancheng Zhong and Yongjiang Li
Genes 2020, 11(2), 153; https://doi.org/10.3390/genes11020153 - 31 Jan 2020
Cited by 25 | Viewed by 3089
Abstract
Essential genes are a group of genes that are indispensable for cell survival and cell fertility. Studying human essential genes helps scientists reveal the underlying biological mechanisms of a human cell but also guides disease treatment. Recently, the publication of human essential gene [...] Read more.
Essential genes are a group of genes that are indispensable for cell survival and cell fertility. Studying human essential genes helps scientists reveal the underlying biological mechanisms of a human cell but also guides disease treatment. Recently, the publication of human essential gene data makes it possible for researchers to train a machine-learning classifier by using some features of the known human essential genes and to use the classifier to predict new human essential genes. Previous studies have found that the essentiality of genes closely relates to their properties in the protein–protein interaction (PPI) network. In this work, we propose a novel supervised method to predict human essential genes by network embedding the PPI network. Our approach implements a bias random walk on the network to get the node network context. Then, the node pairs are input into an artificial neural network to learn their representation vectors that maximally preserves network structure and the properties of the nodes in the network. Finally, the features are put into an SVM classifier to predict human essential genes. The prediction results on two human PPI networks show that our method achieves better performance than those that refer to either genes’ sequence information or genes’ centrality properties in the network as input features. Moreover, it also outperforms the methods that represent the PPI network by other previous approaches. Full article
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19 pages, 2549 KiB  
Article
Detection of Microaneurysms in Fundus Images Based on an Attention Mechanism
by Lizong Zhang, Shuxin Feng, Guiduo Duan, Ying Li and Guisong Liu
Genes 2019, 10(10), 817; https://doi.org/10.3390/genes10100817 - 17 Oct 2019
Cited by 28 | Viewed by 3224
Abstract
Microaneurysms (MAs) are the earliest detectable diabetic retinopathy (DR) lesions. Thus, the ability to automatically detect MAs is critical for the early diagnosis of DR. However, achieving the accurate and reliable detection of MAs remains a significant challenge due to the size and [...] Read more.
Microaneurysms (MAs) are the earliest detectable diabetic retinopathy (DR) lesions. Thus, the ability to automatically detect MAs is critical for the early diagnosis of DR. However, achieving the accurate and reliable detection of MAs remains a significant challenge due to the size and complexity of retinal fundus images. Therefore, this paper presents a novel MA detection method based on a deep neural network with a multilayer attention mechanism for retinal fundus images. First, a series of equalization operations are performed to improve the quality of the fundus images. Then, based on the attention mechanism, multiple feature layers with obvious target features are fused to achieve preliminary MA detection. Finally, the spatial relationships between MAs and blood vessels are utilized to perform a secondary screening of the preliminary test results to obtain the final MA detection results. We evaluated the method on the IDRiD_VOC dataset, which was collected from the open IDRiD dataset. The results show that our method effectively improves the average accuracy and sensitivity of MA detection. Full article
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15 pages, 1622 KiB  
Article
ZCMM: A Novel Method Using Z-Curve Theory- Based and Position Weight Matrix for Predicting Nucleosome Positioning
by Ying Cui, Zelong Xu and Jianzhong Li
Genes 2019, 10(10), 765; https://doi.org/10.3390/genes10100765 - 28 Sep 2019
Cited by 2 | Viewed by 2990
Abstract
Nucleosomes are the basic units of eukaryotes. The accurate positioning of nucleosomes plays a significant role in understanding many biological processes such as transcriptional regulation mechanisms and DNA replication and repair. Here, we describe the development of a novel method, termed ZCMM, based [...] Read more.
Nucleosomes are the basic units of eukaryotes. The accurate positioning of nucleosomes plays a significant role in understanding many biological processes such as transcriptional regulation mechanisms and DNA replication and repair. Here, we describe the development of a novel method, termed ZCMM, based on Z-curve theory and position weight matrix (PWM). The ZCMM was trained and tested using the nucleosomal and linker sequences determined by support vector machine (SVM) in Saccharomyces cerevisiae (S. cerevisiae), and experimental results showed that the sensitivity (Sn), specificity (Sp), accuracy (Acc), and Matthews correlation coefficient (MCC) values for ZCMM were 91.40%, 96.56%, 96.75%, and 0.88, respectively, and the average area under the receiver operating characteristic curve (AUC) value was 0.972. A ZCMM predictor was developed to predict nucleosome positioning in Homo sapiens (H. sapiens), Caenorhabditis elegans (C. elegans), and Drosophila melanogaster (D. melanogaster) genomes, and the accuracy (Acc) values were 77.72%, 85.34%, and 93.62%, respectively. The maximum AUC values of the four species were 0.982, 0.861, 0.912 and 0.911, respectively. Another independent dataset for S. cerevisiae was used to predict nucleosome positioning. Compared with the results of Wu’s method, it was found that the Sn, Sp, Acc, and MCC of ZCMM results for S. cerevisiae were all higher, reaching 96.72%, 96.54%, 94.10%, and 0.88. Compared with the Guo’s method ‘iNuc-PseKNC’, the results of ZCMM for D. melanogaster were better. Meanwhile, the ZCMM was compared with some experimental data in vitro and in vivo for S. cerevisiae, and the results showed that the nucleosomes predicted by ZCMM were highly consistent with those confirmed by these experiments. Therefore, it was further confirmed that the ZCMM method has good accuracy and reliability in predicting nucleosome positioning. Full article
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