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Selected Papers from the 15th International Conference on Bioinformatics (BIOINFO...
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Selected Papers from the 15th International Conference on Bioinformatics (BIOINFO 2019)

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 (27 September 2019) | Viewed by 25402

Special Issue Editors

Prof. Dr. Min Song

E-Mail Website
Guest Editor
Department of Library and Information Science and Jointly Appointed at College of Artificial Intelligence, Yonsei University, Incheon 21983, Republic of Korea
Interests: text mining; literature-based discovery; information extraction; informetrics; medical informatics
Dr. Insuk Lee

E-Mail
Guest Editor
Department of Biotechnology, Yonsei University, Seoul, Korea
Prof. Dr. Doheon Lee

E-Mail
Guest Editor
Department of Bio and Brain Engineering, KAIST/BSRC, Daejeon 305-701, Korea

Special Issue Information

Dear Colleagues,

The 15th International Conference on Bioinformatics (BIOINFO 2019), the official annual meeting of the Korean Society for Bioinformatics (KSBI), will be held in Seoul, Korea, from August 26–27, 2019. The event website can be accessed at: http://www.ksbi.or.kr/bioinfo2019/ .

The aim of the conference is to present the latest progress in a wide spectrum of data-driven biology and biomedical informatics, including big data biology, precision medicine, artificial intelligence in healthcare, and cutting-edge omics technology, in addition to fostering communication among researchers in shaping the future of the research field.

The current Special Issue invites submissions of unpublished original work describing recent advances on all aspects related to the computational biology, and key themes include:

  • Omics technology and data analysis
  • Systems biology
  • Medical informatics (including EMR and medical image analysis)
  • Chemical informatics and computational drug discovery
  • Artificial intelligence in biology and medicine
  • Theoretical biology
  • Database, web servers, software packages for biological research and medicine

We look forward to seeing you at the conference.

Prof. Min Song
Dr. Insuk Lee
Prof. Doheon Lee
Guest Editors

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. 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

  • Omics technology and data analysis
  • Systems biology
  • Medical informatics (including EMR and medical image analysis)
  • Chemical informatics and computational drug discovery
  • Artificial intelligence in biology and medicine
  • Theoretical biology
  • Databases, web servers, software packages for biological research and medicine

Published Papers (7 papers)

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Research

17 pages, 2268 KiB  
Article
HisCoM-PAGE: Hierarchical Structural Component Models for Pathway Analysis of Gene Expression Data
by Lydia Mok, Yongkang Kim, Sungyoung Lee, Sungkyoung Choi, Seungyeoun Lee, Jin-Young Jang and Taesung Park
Genes 2019, 10(11), 931; https://doi.org/10.3390/genes10110931 - 14 Nov 2019
Cited by 5 | Viewed by 3830
Abstract
Although there have been several analyses for identifying cancer-associated pathways, based on gene expression data, most of these are based on single pathway analyses, and thus do not consider correlations between pathways. In this paper, we propose a hierarchical structural component model for [...] Read more.
Although there have been several analyses for identifying cancer-associated pathways, based on gene expression data, most of these are based on single pathway analyses, and thus do not consider correlations between pathways. In this paper, we propose a hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE), which accounts for the hierarchical structure of genes and pathways, as well as the correlations among pathways. Specifically, HisCoM-PAGE focuses on the survival phenotype and identifies its associated pathways. Moreover, its application to real biological data analysis of pancreatic cancer data demonstrated that HisCoM-PAGE could successfully identify pathways associated with pancreatic cancer prognosis. Simulation studies comparing the performance of HisCoM-PAGE with other competing methods such as Gene Set Enrichment Analysis (GSEA), Global Test, and Wald-type Test showed HisCoM-PAGE to have the highest power to detect causal pathways in most simulation scenarios. Full article
(This article belongs to the Special Issue Selected Papers from the 15th International Conference on Bioinformatics (BIOINFO 2019))
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14 pages, 800 KiB  
Article
ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder
by Buru Chang, Yonghwa Choi, Minji Jeon, Junhyun Lee, Kyu-Man Han, Aram Kim, Byung-Joo Ham and Jaewoo Kang
Genes 2019, 10(11), 907; https://doi.org/10.3390/genes10110907 - 07 Nov 2019
Cited by 22 | Viewed by 3914
Abstract
Treating patients with major depressive disorder is challenging because it takes several months for antidepressants prescribed for the patients to take effect. This limitation may result in increased risks and treatment costs. To address this limitation, an accurate antidepressant response prediction model is [...] Read more.
Treating patients with major depressive disorder is challenging because it takes several months for antidepressants prescribed for the patients to take effect. This limitation may result in increased risks and treatment costs. To address this limitation, an accurate antidepressant response prediction model is needed. Recently, several studies have proposed models that extract useful features such as neuroimaging biomarkers and genetic variants from patient data, and use them as predictors for predicting the antidepressant responses of patients. However, it is impossible to utilize all the different types of predictors when making a clinical decision on what drugs to prescribe for a patient. Although a machine learning-based antidepressant response prediction model has been proposed to overcome this problem, the model cannot find the most effective antidepressant for a patient. Based on a neural network, we propose an Antidepressant Response Prediction Network (ARPNet) model capturing high-dimensional patterns from useful features. Based on a literature survey and data-driven feature selection, we extract useful features from patient data, and use the features as predictors. In ARPNet, the patient representation layer captures patient features and the antidepressant prescription representation layer captures antidepressant features. Utilizing the patient and antidepressant prescription representation vectors, ARPNet predicts the degree of antidepressant response. The experimental evaluation results demonstrate that our proposed ARPNet model outperforms machine learning-based models in predicting antidepressant response. Moreover, we demonstrate the applicability of ARPNet in downstream applications in use case scenarios. Full article
(This article belongs to the Special Issue Selected Papers from the 15th International Conference on Bioinformatics (BIOINFO 2019))
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16 pages, 3485 KiB  
Article
In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning
by Kyoungyeul Lee and Dongsup Kim
Genes 2019, 10(11), 906; https://doi.org/10.3390/genes10110906 - 07 Nov 2019
Cited by 30 | Viewed by 3501
Abstract
In in-silico prediction for molecular binding of human genomes, promising results have been demonstrated by deep neural multi-task learning due to its strength in training tasks with imbalanced data and its ability to avoid over-fitting. Although the interrelation between tasks is known to [...] Read more.
In in-silico prediction for molecular binding of human genomes, promising results have been demonstrated by deep neural multi-task learning due to its strength in training tasks with imbalanced data and its ability to avoid over-fitting. Although the interrelation between tasks is known to be important for successful multi-task learning, its adverse effect has been underestimated. In this study, we used molecular interaction data of human targets from ChEMBL to train and test various multi-task and single-task networks and examined the effectiveness of multi-task learning for different compositions of targets. Targets were clustered based on sequence similarity in their binding domains and various target sets from clusters were chosen. By comparing the performance of deep neural architectures for each target set, we found that similarity within a target set is highly important for reliable multi-task learning. For a diverse target set or overall human targets, the performance of multi-task learning was lower than single-task learning, but outperformed single-task for the target set containing similar targets. From this insight, we developed Multiple Partial Multi-Task learning, which is suitable for binding prediction for human drug targets. Full article
(This article belongs to the Special Issue Selected Papers from the 15th International Conference on Bioinformatics (BIOINFO 2019))
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18 pages, 2872 KiB  
Article
Development of Tissue-Specific Age Predictors Using DNA Methylation Data
by Heeyeon Choi, Soobok Joe and Hojung Nam
Genes 2019, 10(11), 888; https://doi.org/10.3390/genes10110888 - 04 Nov 2019
Cited by 16 | Viewed by 3587
Abstract
DNA methylation patterns have been shown to change throughout the normal aging process. Several studies have found epigenetic aging markers using age predictors, but these studies only focused on blood-specific or tissue-common methylation patterns. Here, we constructed nine tissue-specific age prediction models using [...] Read more.
DNA methylation patterns have been shown to change throughout the normal aging process. Several studies have found epigenetic aging markers using age predictors, but these studies only focused on blood-specific or tissue-common methylation patterns. Here, we constructed nine tissue-specific age prediction models using methylation array data from normal samples. The constructed models predict the chronological age with good performance (mean absolute error of 5.11 years on average) and show better performance in the independent test than previous multi-tissue age predictors. We also compared tissue-common and tissue-specific aging markers and found that they had different characteristics. Firstly, the tissue-common group tended to contain more positive aging markers with methylation values that increased during the aging process, whereas the tissue-specific group tended to contain more negative aging markers. Secondly, many of the tissue-common markers were located in Cytosine-phosphate-Guanine (CpG) island regions, whereas the tissue-specific markers were located in CpG shore regions. Lastly, the tissue-common CpG markers tended to be located in more evolutionarily conserved regions. In conclusion, our prediction models identified CpG markers that capture both tissue-common and tissue-specific characteristics during the aging process. Full article
(This article belongs to the Special Issue Selected Papers from the 15th International Conference on Bioinformatics (BIOINFO 2019))
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15 pages, 1637 KiB  
Article
Meta-Analysis of Polymyositis and Dermatomyositis Microarray Data Reveals Novel Genetic Biomarkers
by Jaeseung Song, Daeun Kim, Juyeon Hong, Go Woon Kim, Junghyun Jung, Sejin Park, Hee Jung Park, Jong Wha J. Joo and Wonhee Jang
Genes 2019, 10(11), 864; https://doi.org/10.3390/genes10110864 - 30 Oct 2019
Cited by 4 | Viewed by 4282
Abstract
Polymyositis (PM) and dermatomyositis (DM) are both classified as idiopathic inflammatory myopathies. They share a few common characteristics such as inflammation and muscle weakness. Previous studies have indicated that these diseases present aspects of an auto-immune disorder; however, their exact pathogenesis is still [...] Read more.
Polymyositis (PM) and dermatomyositis (DM) are both classified as idiopathic inflammatory myopathies. They share a few common characteristics such as inflammation and muscle weakness. Previous studies have indicated that these diseases present aspects of an auto-immune disorder; however, their exact pathogenesis is still unclear. In this study, three gene expression datasets (PM: 7, DM: 50, Control: 13) available in public databases were used to conduct meta-analysis. We then conducted expression quantitative trait loci analysis to detect the variant sites that may contribute to the pathogenesis of PM and DM. Six-hundred differentially expressed genes were identified in the meta-analysis (false discovery rate (FDR) < 0.01), among which 317 genes were up-regulated and 283 were down-regulated in the disease group compared with those in the healthy control group. The up-regulated genes were significantly enriched in interferon-signaling pathways in protein secretion, and/or in unfolded-protein response. We detected 10 single nucleotide polymorphisms (SNPs) which could potentially play key roles in driving the PM and DM. Along with previously reported genes, we identified 4 novel genes and 10 SNP-variant regions which could be used as candidates for potential drug targets or biomarkers for PM and DM. Full article
(This article belongs to the Special Issue Selected Papers from the 15th International Conference on Bioinformatics (BIOINFO 2019))
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10 pages, 2903 KiB  
Article
Identification of Key Genes for the Precise Classification between Solenopsis invicta and S. geminata Facilitating the Quarantine Process
by Kil-Hyun Kim, Ji-Su Kim, Hyun-Ji Cho, Jong-Ho Lee, Tae-Hwan Jun and Yang Jae Kang
Genes 2019, 10(10), 812; https://doi.org/10.3390/genes10100812 - 15 Oct 2019
Cited by 2 | Viewed by 3421
Abstract
One of the 100 worst invasive exotic species, Solenopsis invicta (red imported fire ant), has the possibility to induce an allergic reaction that may eventually cause death from its aggressive stinging. In 2017, S. invicta was found at a container yard in Gamman [...] Read more.
One of the 100 worst invasive exotic species, Solenopsis invicta (red imported fire ant), has the possibility to induce an allergic reaction that may eventually cause death from its aggressive stinging. In 2017, S. invicta was found at a container yard in Gamman Port, Busan, South Korea for the first time. It may result in an infestation of fire ants in the Korean environment. After this incident, sensitive quarantine procedures are required to detect possible contamination of fire ants in imported containers. However, currently, fire ant identification relies on phenotypic characteristics. This requires highly trained experts for identification and there are not enough to cover all imported containers. Here, we develop a key molecular marker to distinguish S. invicta from others using the whole genome sequence (WGS) of collected S. invicta from Gamman Port and NCBI-deposited WGS data of S.invicta and S. geminata. The consolidated genotypes of Solenopsis genus successfully indicate the distinguishable gene. The gel-based experimental validation confirmed expected classification and the developed cleaved amplified polymorphic sequences (CAPS) marker also gave a consistent result. Using the CAPS marker derived from our consolidated genotypes, the samples collected from containers in several ports can be easily tested by PCR in a few hours. The quick and easy test would increase not only the labor efficiency but also the environmental safety from fire ants. Full article
(This article belongs to the Special Issue Selected Papers from the 15th International Conference on Bioinformatics (BIOINFO 2019))
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9 pages, 1505 KiB  
Article
miR2Diabetes: A Literature-Curated Database of microRNA Expression Patterns, in Diabetic Microvascular Complications
by Sungjin Park, SeongRyeol Moon, Kiyoung Lee, Ie Byung Park, Dae Ho Lee and Seungyoon Nam
Genes 2019, 10(10), 784; https://doi.org/10.3390/genes10100784 - 09 Oct 2019
Cited by 3 | Viewed by 2335
Abstract
microRNAs (miRNAs) have been established as critical regulators of the pathogenesis of diabetes mellitus (DM), and diabetes microvascular complications (DMCs). However, manually curated databases for miRNAs, and DM (including DMCs) association studies, have yet to be established. Here, we constructed a user-friendly database, [...] Read more.
microRNAs (miRNAs) have been established as critical regulators of the pathogenesis of diabetes mellitus (DM), and diabetes microvascular complications (DMCs). However, manually curated databases for miRNAs, and DM (including DMCs) association studies, have yet to be established. Here, we constructed a user-friendly database, “miR2Diabetes,” equipped with a graphical web interface for simple browsing or searching manually curated annotations. The annotations in our database cover 14 DM and DMC phenotypes, involving 156 miRNAs, by browsing diverse sample origins (e.g., blood, kidney, liver, and other tissues). Additionally, we provide miRNA annotations for disease-model organisms (including rats and mice), of DM and DMCs, for the purpose of improving knowledge of the biological complexity of these pathologies. We assert that our database will be a comprehensive resource for miRNA biomarker studies, as well as for prioritizing miRNAs for functional validation, in DM and DMCs, with likely extension to other diseases. Full article
(This article belongs to the Special Issue Selected Papers from the 15th International Conference on Bioinformatics (BIOINFO 2019))
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