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Selected Papers from the 10th International Conference on Bioinformatics and...
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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: closed (15 December 2021) | Viewed by 17381

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, Collections and Topics 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: biomedical informatics; functional genomics; immunoinformatics; medical imaging analysis; medical data analysis; biomarker detection; systems biology; bio-signal analysis; pattern recognition
Special Issues, Collections and Topics in MDPI journals

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The current Special Issue invites submissions on unpublished original work describing recent advances in all aspects of bioinformatics and computational biology, multi-omics, systems biology, biomedical engineering,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 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

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

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Published Papers (5 papers)

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Research

12 pages, 2989 KiB  
Article
An Integrative Segmentation Framework for Cell Nucleus of Fluorescence Microscopy
by Weihao Pan, Zhe Liu, Weichen Song, Xuyang Zhen, Kai Yuan, Fei Xu and Guan Ning Lin
Genes 2022, 13(3), 431; https://doi.org/10.3390/genes13030431 - 26 Feb 2022
Cited by 3 | Viewed by 2457
Abstract
Nucleus segmentation of fluorescence microscopy is a critical step in quantifying measurements in cell biology. Automatic and accurate nucleus segmentation has powerful applications in analyzing intrinsic characterization in nucleus morphology. However, existing methods have limited capacity to perform accurate segmentation in challenging samples, [...] Read more.
Nucleus segmentation of fluorescence microscopy is a critical step in quantifying measurements in cell biology. Automatic and accurate nucleus segmentation has powerful applications in analyzing intrinsic characterization in nucleus morphology. However, existing methods have limited capacity to perform accurate segmentation in challenging samples, such as noisy images and clumped nuclei. In this paper, inspired by the idea of cascaded U-Net (or W-Net) and its remarkable performance improvement in medical image segmentation, we proposed a novel framework called Attention-enhanced Simplified W-Net (ASW-Net), in which a cascade-like structure with between-net connections was used. Results showed that this lightweight model could reach remarkable segmentation performance in the BBBC039 testing set (aggregated Jaccard index, 0.90). In addition, our proposed framework performed better than the state-of-the-art methods in terms of segmentation performance. Moreover, we further explored the effectiveness of our designed network by visualizing the deep features from the network. Notably, our proposed framework is open source. Full article
(This article belongs to the Special Issue Selected Papers from the 10th International Conference on Bioinformatics and Biomedical Science (ICBBS2021))
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0 pages, 1310 KiB  
Article
RETRACTED: Using Comorbidity Pattern Analysis to Detect Reliable Methylated Genes in Colorectal Cancer Verified by Stool DNA Test
by Yi-Chiao Cheng, Po-Hsien Wu, Yen-Ju Chen, Cing-Han Yang, Jhen-Li Huang, Yu-Ching Chou, Pi-Kai Chang, Chia-Cheng Wen, Shu-Wen Jao, Hsin-Hui Huang, Yi-Hsuan Tsai and Tun-Wen Pai
Genes 2021, 12(10), 1539; https://doi.org/10.3390/genes12101539 - 28 Sep 2021
Cited by 9 | Viewed by 3503 | Retraction
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
(This article belongs to the Special Issue Selected Papers from the 10th International Conference on Bioinformatics and Biomedical Science (ICBBS2021))
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12 pages, 2954 KiB  
Article
Improved Large-Scale Homology Search by Two-Step Seed Search Using Multiple Reduced Amino Acid Alphabets
by Kazuki Takabatake, Kazuki Izawa, Motohiro Akikawa, Keisuke Yanagisawa, Masahito Ohue and Yutaka Akiyama
Genes 2021, 12(9), 1455; https://doi.org/10.3390/genes12091455 - 21 Sep 2021
Cited by 1 | Viewed by 2953
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
(This article belongs to the Special Issue Selected Papers from the 10th International Conference on Bioinformatics and Biomedical Science (ICBBS2021))
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11 pages, 431 KiB  
Article
Tensor-Decomposition-Based Unsupervised Feature Extraction in Single-Cell Multiomics Data Analysis
by Y-h. Taguchi and Turki Turki
Genes 2021, 12(9), 1442; https://doi.org/10.3390/genes12091442 - 18 Sep 2021
Cited by 4 | Viewed by 3633
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
(This article belongs to the Special Issue Selected Papers from the 10th International Conference on Bioinformatics and Biomedical Science (ICBBS2021))
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13 pages, 1749 KiB  
Article
EPIsHilbert: Prediction of Enhancer-Promoter Interactions via Hilbert Curve Encoding and Transfer Learning
by Mingyang Zhang, Yujia Hu and Min Zhu
Genes 2021, 12(9), 1385; https://doi.org/10.3390/genes12091385 - 6 Sep 2021
Cited by 7 | Viewed by 3271
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
(This article belongs to the Special Issue Selected Papers from the 10th International Conference on Bioinformatics and Biomedical Science (ICBBS2021))
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