Special Issue "Selected Papers from the 10th International Conference on Bioinformatics and Biomedical Science (ICBBS2021)"

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

Deadline for manuscript submissions: 15 December 2021.

Special Issue Editors

Prof. Dr. Kenta Nakai
E-Mail Website
Guest Editor
The Institute of Medical Sciences, the University of Tokyo, Tokyo 108-8639, Japan
Interests: sequence analysis in molecular biology; bioinformatics in genome analysis
Special Issues and Collections in MDPI journals
Prof. Dr. Tun-Wen Pai
E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: artificial intelligence; bioinformatics; functional genomics; systems biology; medical informatics; multiomics; biomarker discovery; biomedical imaging; precision healthcare; digital health

Special Issue Information

Dear Colleagues,

The 10th International Conference on Bioinformatics and Biomedical Science (ICBBS 2021) will be held on 29–31 October 2021 in Xiamen, China, and a virtual format simultaneously. The webpage for this event is http://www.icbbs.org/.

The aim of ICBBS is to provide a platform for researchers, engineers, academicians as well as industrial professionals from all over the world to present their research results and development activities in bioinformatics- and biomedical-science-related fields. Students are also cordially invited and encouraged to actively participate and present their research work. This conference provides opportunities for the delegates to exchange new ideas and application experience to establish business or research relations and to find global partners for future collaboration.

The current Special Issue invites submissions on unpublished original work describing recent advances in all aspects of bioinformatics and computational biology, multi-omics, systems biology, biomedical engineering, medical informatics, digital health, including but not restricted to the following topics:

  • Algorithms, models, software, and tools in bioinformatics;
  • Next generation sequencing in functional genomics;
  • Precision medicine, translational bioinformatics, and biomarker detection;
  • Biomedical image processing and digital pathology;
  • Biomedical devices, sensors, and smart healthcare systems;
  • Computational intelligence in healthcare and telehealth;
  • Any novel approaches to bioinformatics and biomedical informatics.

Prof. Dr. Kenta Nakai
Prof. Dr. Tun-Wen Pai
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 papers will be 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 2000 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

  • artificial intelligence
  • bioinformatics
  • functional genomics
  • systems biology
  • multiomics
  • biomarker discovery
  • medical informatics
  • biomedical imaging
  • precision healthcare
  • digital health

Published Papers (4 papers)

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Research

Article
Using Comorbidity Pattern Analysis to Detect Reliable Methylated Genes in Colorectal Cancer Verified by Stool DNA Test
Genes 2021, 12(10), 1539; https://0-doi-org.brum.beds.ac.uk/10.3390/genes12101539 - 28 Sep 2021
Viewed by 350
Abstract
Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide in 2020. Colonoscopy and the fecal immunochemical test (FIT) are commonly used as CRC screening tests, but both types of tests possess different limitations. Recently, liquid biopsy-based DNA methylation test has become [...] Read more.
Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide in 2020. Colonoscopy and the fecal immunochemical test (FIT) are commonly used as CRC screening tests, but both types of tests possess different limitations. Recently, liquid biopsy-based DNA methylation test has become a powerful tool for cancer screening, and the detection of abnormal DNA methylation in stool specimens is considered as an effective approach for CRC screening. The aim of this study was to develop a novel approach in biomarker selection based on integrating primary biomarkers from genome-wide methylation profiles and secondary biomarkers from CRC comorbidity analytics. A total of 125 differential methylated probes (DMPs) were identified as primary biomarkers from 352 genome-wide methylation profiles. Among them, 51 biomarkers, including 48 hypermethylated DMPs and 3 hypomethylated DMPs, were considered as suitable DMP candidates for CRC screening tests. After comparing with commercial kits, three genes (ADHFE1, SDC2, and PPP2R5C) were selected as candidate epigenetic biomarkers for CRC screening tests. Methylation levels of these three biomarkers were significantly higher for patients with CRC than normal subjects. The sensitivity and specificity of integrating methylated ADHFE1, SDC2, and PPP2R5C for CRC detection achieved 84.6% and 92.3%, respectively. Through an integrated approach using genome-wide DNA methylation profiles and electronic medical records, we could design a biomarker panel that allows for early and accurate noninvasive detection of CRC using stool samples. Full article
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Article
Improved Large-Scale Homology Search by Two-Step Seed Search Using Multiple Reduced Amino Acid Alphabets
Genes 2021, 12(9), 1455; https://0-doi-org.brum.beds.ac.uk/10.3390/genes12091455 - 21 Sep 2021
Viewed by 520
Abstract
Metagenomic analysis, a technique used to comprehensively analyze microorganisms present in the environment, requires performing high-precision homology searches on large amounts of sequencing data, the size of which has increased dramatically with the development of next-generation sequencing. NCBI BLAST is the most widely [...] Read more.
Metagenomic analysis, a technique used to comprehensively analyze microorganisms present in the environment, requires performing high-precision homology searches on large amounts of sequencing data, the size of which has increased dramatically with the development of next-generation sequencing. NCBI BLAST is the most widely used software for performing homology searches, but its speed is insufficient for the throughput of current DNA sequencers. In this paper, we propose a new, high-performance homology search algorithm that employs a two-step seed search strategy using multiple reduced amino acid alphabets to identify highly similar subsequences. Additionally, we evaluated the validity of the proposed method against several existing tools. Our method was faster than any other existing program for ≤120,000 queries, while DIAMOND, an existing tool, was the fastest method for >120,000 queries. Full article
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Article
Tensor-Decomposition-Based Unsupervised Feature Extraction in Single-Cell Multiomics Data Analysis
Genes 2021, 12(9), 1442; https://0-doi-org.brum.beds.ac.uk/10.3390/genes12091442 - 18 Sep 2021
Viewed by 652
Abstract
Analysis of single-cell multiomics datasets is a novel topic and is considerably challenging because such datasets contain a large number of features with numerous missing values. In this study, we implemented a recently proposed tensor-decomposition (TD)-based unsupervised feature extraction (FE) technique to address [...] Read more.
Analysis of single-cell multiomics datasets is a novel topic and is considerably challenging because such datasets contain a large number of features with numerous missing values. In this study, we implemented a recently proposed tensor-decomposition (TD)-based unsupervised feature extraction (FE) technique to address this difficult problem. The technique can successfully integrate single-cell multiomics data composed of gene expression, DNA methylation, and accessibility. Although the last two have large dimensions, as many as ten million, containing only a few percentage of nonzero values, TD-based unsupervised FE can integrate three omics datasets without filling in missing values. Together with UMAP, which is used frequently when embedding single-cell measurements into two-dimensional space, TD-based unsupervised FE can produce two-dimensional embedding coincident with classification when integrating single-cell omics datasets. Genes selected based on TD-based unsupervised FE are also significantly related to reasonable biological roles. Full article
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Article
EPIsHilbert: Prediction of Enhancer-Promoter Interactions via Hilbert Curve Encoding and Transfer Learning
Genes 2021, 12(9), 1385; https://0-doi-org.brum.beds.ac.uk/10.3390/genes12091385 - 06 Sep 2021
Viewed by 426
Abstract
Enhancer-promoter interactions (EPIs) play a significant role in the regulation of gene transcription. However, enhancers may not necessarily interact with the closest promoters, but with distant promoters via chromatin looping. Considering the spatial position relationship between enhancers and their target promoters is important [...] Read more.
Enhancer-promoter interactions (EPIs) play a significant role in the regulation of gene transcription. However, enhancers may not necessarily interact with the closest promoters, but with distant promoters via chromatin looping. Considering the spatial position relationship between enhancers and their target promoters is important for predicting EPIs. Most existing methods only consider sequence information regardless of spatial information. On the other hand, recent computational methods lack generalization capability across different cell line datasets. In this paper, we propose EPIsHilbert, which uses Hilbert curve encoding and two transfer learning approaches. Hilbert curve encoding can preserve the spatial position information between enhancers and promoters. Additionally, we use visualization techniques to explore important sequence fragments that have a high impact on EPIs and the spatial relationships between them. Transfer learning can improve prediction performance across cell lines. In order to further prove the effectiveness of transfer learning, we analyze the sequence coincidence of different cell lines. Experimental results demonstrate that EPIsHilbert is a state-of-the-art model that is superior to most of the existing methods both in specific cell lines and cross cell lines. Full article
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