Table of Content

Bioinformatics Study of Diseases

Submission Deadline: 31 December 2023 Submit to Special Issue

Guest Editors

Prof. Dr. Qingjia Chi, Department of Engineering Structure and Mechanics, Wuhan University of Technology, Wuhan 430070, China. qingjia@whut.edu.cn

Summary

Bioinformatics analysis is now widely used in the therapeutic diagnosis and prediction of diseases. It is of great significance for disease gene diagnosis, gene function discovery, protein structure prediction, structure-based drug design, drug synthesis, and pharmaceutical industry, shortening drug development time, and personalized treatment of diseases. However, there is an urgent need for bioinformatics to use artificial intelligence and develop powerful machine learning and deep learning algorithms to improve and build disease diagnosis and prognosis models.

   

We welcome original research, reviews, and other articles relevant to the bioinformatics study of disease in this special issue. Topics include but may not be limited to:

 

Prognostic and diagnostic models of disease

Omics sequencing method development

Database construction

Multi-omics data integration

Bulk RNA-seq and single cell RNA-seq

Transcriptome bioinformatics

Bulk RNA-seq and

Proteome bioinformatics

Machine learning and deep learning algorithms in bioinformatics

Epigenetic bioinformatics  

Traditional Chinese medicine systems biology and network pharmacology

Disease biomarker identification


Keywords

Bioinformatics, Prognostic Models, Diagnostic Models, Biomarker, Machine Learning, Drug Targets

Published Papers


  • Open Access

    ARTICLE

    Increased MAD2L2 expression predicts poor clinical outcome in Colon Adenocarcinoma

    HAOTONG SUN, HEYING WANG, XIN LI, YANJIE HAO, JUN LING, HUAN WANG, FEIMIAO WANG, FANG XU
    BIOCELL, Vol.47, No.3, pp. 607-618, 2023, DOI:10.32604/biocell.2023.026445
    (This article belongs to this Special Issue: Bioinformatics Study of Diseases)
    Abstract Background: Colon adenocarcinoma (COAD) is the second leading cause of cancer death worldwide thus, identification of COAD biomarkers is critical. Mitotic Arrest Deficient 2 Like 2 (MAD2L2) is a key factor in mammalian DNA damage repair and is highly expressed in many malignant tumors. This is a comprehensive study of MAD2L2 expression, its diagnostic value, prognostic analysis, potential biological function, and impact on the immune system of patients with COAD. Methods: Gene expression, clinical relevance, prognostic analysis, diagnostic value, GO/KEGG cluster analysis, data obtained from TCGA, and bioinformatics statistical analysis were performed using the R package. Immune responses to MAD2L2More >

  • Open Access

    ARTICLE

    A model based on eight iron metabolism-related genes accurately predicts acute myeloid leukemia prognosis

    ZHANSHU LIU, XI HUANG
    BIOCELL, Vol.47, No.3, pp. 593-605, 2023, DOI:10.32604/biocell.2023.024148
    (This article belongs to this Special Issue: Bioinformatics Study of Diseases)
    Abstract Purpose: Iron metabolism maintains the balance between iron absorption and excretion. Abnormal iron metabolism can cause numerous diseases, including tumor. This study determined the iron metabolism-related genes (IMRGs) signature that can predict the prognosis of acute myeloid leukemia (AML). The roles of these genes in the immune microenvironment were also explored. Methods: A total of 514 IMRGs were downloaded from the Molecular Characteristics Database (MSigDB). IMRGs related to AML prognosis were identified using Cox regression and LASSO analyses and were used to construct the risk score model. AML patients were stratified into high-risk groups (cluster 1) and low-risk groups (cluster… More >

  • Open Access

    ARTICLE

    SPP1 and the risk score model to improve the survival prediction of patients with hepatocellular carcinoma based on multiple algorithms and back propagation neural networks

    WENLI ZENG, FENG LING, KAINUO DANG, QINGJIA CHI
    BIOCELL, Vol.47, No.3, pp. 581-592, 2023, DOI:10.32604/biocell.2023.025957
    (This article belongs to this Special Issue: Bioinformatics Study of Diseases)
    Abstract Hepatocellular carcinoma (HCC) is associated with poor prognosis and fluctuations in immune status. Although studies have found that secreted phosphoprotein 1 (SPP1) is involved in HCC progression, its independent prognostic value and immune-mediated role remain unclear. Using The Cancer Genome Atlas and Gene Expression Omnibus data, we found that low expression of SPP1 is significantly associated with improved survival of HCC patients and that SPP1 expression is correlated with clinical characteristics. Univariate and multivariate Cox regression confirmed that SPP1 is an independent prognostic factor of HCC. Subsequently, we found that T cell CD4 memory-activated monocytes, M0 macrophages, and resting mast… More >

  • Open Access

    ARTICLE

    dbSCI: A manually curated database of SARS-CoV-2 inhibitors for COVID-19

    QIANG WANG, GUO ZHAO, LONGXIANG XIE, XUAN LI, XIXI YU, QIONGSHAN LI, BAOPING ZHENG, ZULIPINUER WUSIMAN, XIANGQIAN GUO
    BIOCELL, Vol.47, No.2, pp. 367-371, 2023, DOI:10.32604/biocell.2023.025310
    (This article belongs to this Special Issue: Bioinformatics Study of Diseases)
    Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogen of the ongoing coronavirus disease 2019 (COVID-19) global pandemic. Here, by centralizing published cell-based experiments, clinical trials, and virtual drug screening data from the NCBI PubMed database, we developed a database of SARS-CoV-2 inhibitors for COVID-19, dbSCI, which includes 234 SARS-CoV-2 inhibitors collected from publications based on cell-based experiments, 81 drugs of COVID-19 in clinical trials and 1305 potential SARS-CoV-2 inhibitors from bioinformatics analyses. dbSCI provides four major functions: (1) search the drug target or its inhibitor for SARS-CoV-2, (2) browse target/inhibitor information collected from cell experiments, clinical trials, and… More >

  • Open Access

    ARTICLE

    A pan-cancer analysis of the biological function and clinical value of BTLA in tumors

    XIANGLAI JIANG, JIN HE, YONGFENG WANG, JIAHUI LIU, XIANGYANG LI, XIANGUI HE, HUI CAI
    BIOCELL, Vol.47, No.2, pp. 351-366, 2023, DOI:10.32604/biocell.2023.025157
    (This article belongs to this Special Issue: Bioinformatics Study of Diseases)
    Abstract B and T-lymphocyte attenuator (BTLA) plays an immunosuppressive role by inhibiting T- and B-cell functions. BTLA is associated with a variety of diseases, especially cancer immunity. However, the function of BTLA in various cancers and its clinical prognostic value have still not been comprehensively analyzed. This study aimed to identify the relationship between BTLA and cancer from the perspectives of differences in BTLA expression, its clinical value, immune infiltration, and the correlation with immune-related genes in various cancers. Data regarding mRNA expression, miRNA expression, lncRNA expression, and clinical data of patients of 33 existing cancers were collected from the TCGA… More >

  • Open Access

    ARTICLE

    Prognostic model for prostate cancer based on glycolysis-related genes and non-negative matrix factorization analysis

    ZECHAO LU, FUCAI TANG, HAOBIN ZHOU, ZEGUANG LU, WANYAN CAI, JIAHAO ZHANG, ZHICHENG TANG, YONGCHANG LAI, ZHAOHUI HE
    BIOCELL, Vol.47, No.2, pp. 339-350, 2023, DOI:10.32604/biocell.2023.023750
    (This article belongs to this Special Issue: Bioinformatics Study of Diseases)
    Abstract Background: Establishing an appropriate prognostic model for PCa is essential for its effective treatment. Glycolysis is a vital energy-harvesting mechanism for tumors. Developing a prognostic model for PCa based on glycolysis-related genes is novel and has great potential. Methods: First, gene expression and clinical data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and glycolysis-related genes were obtained from the Molecular Signatures Database (MSigDB). Gene enrichment analysis was performed to verify that glycolysis functions were enriched in the genes we obtained, which were used in non-negative matrix factorization (NMF) to identify clusters.… More >

  • Open Access

    REVIEW

    Review on microbial metabolomics of probiotics and pathogens: Methodologies and applications

    XIN MENG, XUE LI, LIANRONG YANG, RUI YIN, LEHUI QI, QI GUO
    BIOCELL, Vol.47, No.1, pp. 91-107, 2023, DOI:10.32604/biocell.2023.024310
    (This article belongs to this Special Issue: Bioinformatics Study of Diseases)
    Abstract In recent years, microbial metabolomics, a new field that has attracted wide attention, provides a map of metabolic pathways and clarifies the interaction mechanism between microorganisms and hosts. Many microorganisms are found in the human intestine, oral cavity, vagina, etc. Probiotics could maintain the good health of the host, while pathogens and an imbalance of bacterial flora lead to a series of diseases of the body and mind. Metabolomics is a science for qualitative and quantitative analysis of all metabolites in an organism or biological system, which could provide key information to understand the related metabolic pathways and associated changes.… More >

  • Open Access

    ARTICLE

    ABCC8 is correlated with immune cell infiltration and overall survival in lower grade glioma

    LIPING GONG, MING JIA
    BIOCELL, Vol.47, No.1, pp. 109-123, 2023, DOI:10.32604/biocell.2023.024620
    (This article belongs to this Special Issue: Bioinformatics Study of Diseases)
    Abstract ATP binding cassette subfamily C member 8 (ABCC8) encodes a protein regulating the ATP-sensitive potassium channel. Whether the level of ABCC8 mRNA in lower grade glioma (LGG) correlates with immune cell infiltration and patient outcomes has not been evaluated until now. Comparisons of ABCC8 expression between different tumors and normal tissues were evaluated by exploring publicly available datasets. The association between ABCC8 and tumor immune cell infiltration, diverse gene mutation characteristics, tumor mutation burden (TMB), and survival in LGG was also investigated in several independent datasets. Pathway enrichment analysis was conducted to search for ABCC8-associated signaling pathways. Through an online… More >

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