Selected Papers from the 11th International Conference on Bioinformatics and Biomedical Science (ICBBS 2022)

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 5128

Special Issue Editors


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Guest Editor
1. School of Computer, Electronic and Information, Guangxi University, Nanning 530004, China
2. The State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning 530004, China
Interests: graph mining; knowledge graph; computational persuasion; molecular structure modeling; medical image analysis; disease–drug correlation

Special Issue Information

Dear Colleagues,

The 11th International Conference on Bioinformatics and Biomedical Science (ICBBS 2022) will be held on 28–30 October 2022 in Nanning, China, and it will be held in a virtual format simultaneously. The webpage for this event is http://www.icbbs.org/.

ICBBS 2022 is dedicated to bioinformatics and computational biology, as well as biomedical science and engineering. The topics expected to be included are, but are not limited to, microarray data analysis, computational proteomics, protein structure, function and sequence analysis, telemedicine, computer-assisted surgery, and computer-assisted intervention systems. We are looking forward to receiving valuable submissions from you.

Prof. Dr. Qingfeng Chen
Prof. Dr. Y-h. Taguchi
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.

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Keywords

  • next-generation sequencing and sequence analysis
  • DNA and RNA structure, function and sequence analysis
  • gene regulation, expression, identification and network
  • interactive 3D modelling
  • computational support for clinical decisions
  • health monitoring systems and wearable systems

Published Papers (2 papers)

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Research

18 pages, 2730 KiB  
Article
Uncovering the Relationship between Tissue-Specific TF-DNA Binding and Chromatin Features through a Transformer-Based Model
by Yongqing Zhang, Yuhang Liu, Zixuan Wang, Maocheng Wang, Shuwen Xiong, Guo Huang and Meiqin Gong
Genes 2022, 13(11), 1952; https://doi.org/10.3390/genes13111952 - 26 Oct 2022
Cited by 5 | Viewed by 1957
Abstract
Chromatin features can reveal tissue-specific TF-DNA binding, which leads to a better understanding of many critical physiological processes. Accurately identifying TF-DNA bindings and constructing their relationships with chromatin features is a long-standing goal in the bioinformatic field. However, this has remained elusive due [...] Read more.
Chromatin features can reveal tissue-specific TF-DNA binding, which leads to a better understanding of many critical physiological processes. Accurately identifying TF-DNA bindings and constructing their relationships with chromatin features is a long-standing goal in the bioinformatic field. However, this has remained elusive due to the complex binding mechanisms and heterogeneity among inputs. Here, we have developed the GHTNet (General Hybrid Transformer Network), a transformer-based model to predict TF-DNA binding specificity. The GHTNet decodes the relationship between tissue-specific TF-DNA binding and chromatin features via a specific input scheme of alternative inputs and reveals important gene regions and tissue-specific motifs. Our experiments show that the GHTNet has excellent performance, achieving about a 5% absolute improvement over existing methods. The TF-DNA binding mechanism analysis shows that the importance of TF-DNA binding features varies across tissues. The best predictor is based on the DNA sequence, followed by epigenomics and shape. In addition, cross-species studies address the limited data, thus providing new ideas in this case. Moreover, the GHTNet is applied to interpret the relationship among TFs, chromatin features, and diseases associated with AD46 tissue. This paper demonstrates that the GHTNet is an accurate and robust framework for deciphering tissue-specific TF-DNA binding and interpreting non-coding regions. Full article
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12 pages, 2947 KiB  
Article
A Novel Hypoxia Related Marker in Blood Link to Aid Diagnosis and Therapy in Osteoarthritis
by Shunhan Yao, Meiling Deng, Xiaojing Du, Rongzhi Huang and Qingfeng Chen
Genes 2022, 13(9), 1501; https://doi.org/10.3390/genes13091501 - 23 Aug 2022
Cited by 5 | Viewed by 2389
Abstract
Osteoarthritis (OA) is a common chronic degenerative arthritis. Its treatment options are very limited. At present, hypoxia is a prominent factor in OA. This study aimed to re-explore the mechanism between hypoxia and OA, which provides new insights into the diagnosis and therapy [...] Read more.
Osteoarthritis (OA) is a common chronic degenerative arthritis. Its treatment options are very limited. At present, hypoxia is a prominent factor in OA. This study aimed to re-explore the mechanism between hypoxia and OA, which provides new insights into the diagnosis and therapy of OA. We acquired the OA-related expression profiles of GSE48556, GSE55235, and GSE55457 for our analysis. Using gene set variation analysis (GSVA), we found significant differences in hypoxia. These differences result from multiple pathways, such as the p53 signaling pathway, cell senescence, the NF-kappa B signaling pathway, Ubiquitin-mediated proteolysis, and apoptosis. Meanwhile, the single-sample gene set enrichment analysis (ssGSEA) showed that hypoxia was significantly associated with the level of immune cell infiltration in the immune microenvironment. Thus, we believe that hypoxia is useful for the diagnosis and treatment of OA. We successfully constructed a novel hypoxia-related index (HRI) based on seven hypoxia-related genes (ADM, CDKN3, ENO1, NDRG1, PGAM1, SLC2A1, VEGFA) by least absolute shrinkage and binary logistic regression of the generalized linear regression. HRI showed potential for improving OA diagnosis through receiver operation characteristic (ROC) analysis (AUC training cohort = 0.919, AUC testing cohort = 0.985). Moreover, we found that celastrol, droxinostat, torin-2, and narciclasine may be potential therapeutic compounds for OA based on the Connectivity Map (CMap). In conclusion, hypoxia is involved in the development and progression of OA. HRI can improve diagnosis and show great potential in clinical application. Celastrol, droxinostat, torin-2, and narciclasine may be potential compounds for the treatment of OA patients. Full article
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