Special Issue "Bioinformatics and Machine Learning in Disease Research"

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

Deadline for manuscript submissions: 20 December 2022 | Viewed by 474

Special Issue Editor

Dr. Natalie A. Twine
E-Mail Website
Guest Editor
1. Transformational Bioinformatics, Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation (CSIRO), North Ryde, NSW 2113, Australia
2. Applied BioSciences, Faculty of Science and Engineering, Macquarie University, Macquarie Park, NSW 2113, Australia
Interests: bioinformatics; transcriptomics; genomics; clinical genomics

Special Issue Information

Dear Colleagues,

Genomics is forecast to be the largest source of Big Data by 2025, totaling up to 40 exabytes per year and exceeding all current sources of Big Data combined (astronomy, YouTube, and Twitter). When other bioinformatics data are included in this, the vast volumes we have at our disposal become clear. ‘Data is the new gold’ is an often-used phrase that speaks to the value we can gain from such rich datasets, but to create this value, we need to effectively mine the data using innovative machine learning approaches.

Many prevalent human diseases are driven by multiple and complex factors, including diabetes, cardiovascular disease, and cancer. To understand the complex interplay of genetic and environmental factors driving these diseases, we need data-driven approaches which cater to large datasets, and machine learning offers us exactly this. Given the huge potential of machine learning and bioinformatics to understand human disease, a Special Issue of the journal Genes is being launched to explore the methods and applications of machine learning and bioinformatics in human disease. Authors are encouraged to submit original manuscripts describing the utilization of machine learning and bioinformatics to answer scientific questions relating to human disease. Also encouraged are papers describing new methods and reviews or comparisons of machine learning approaches in the context of human disease.

Dr. Natalie A. Twine
Guest Editor

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 2400 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

  • machine learning
  • bioinformatics
  • human disease
  • genomics
  • proteomics
  • transcriptomics
  • big data
  • complex genetic disease

Published Papers (1 paper)

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Research

Article
ASPN Is a Potential Biomarker and Associated with Immune Infiltration in Endometriosis
by and
Genes 2022, 13(8), 1352; https://doi.org/10.3390/genes13081352 - 28 Jul 2022
Viewed by 279
Abstract
Objective: Endometriosis is a benign gynecological disease characterized by distant metastasis. Previous studies have discovered abnormal numbers and function of immune cells in endometriotic lesions. We aimed to find potential biomarkers of endometriosis and to explore the relationship between ASPN and the immune [...] Read more.
Objective: Endometriosis is a benign gynecological disease characterized by distant metastasis. Previous studies have discovered abnormal numbers and function of immune cells in endometriotic lesions. We aimed to find potential biomarkers of endometriosis and to explore the relationship between ASPN and the immune microenvironment of endometriosis. Methods: We obtained the GSE141549 and GSE7305 datasets containing endometriosis and normal endometrial samples from the Gene Expression Omnibus database (GEO). In the GSE141549 dataset, differentially expressed genes (DEGs) were found. The Least Absolute Shrinkage and Selection Operator (Lasso) regression and generalized linear models (GLMs) were used to screen new biomarkers. The expression levels and diagnostic utility of biomarkers were assessed in GSE7305, and biomarker expression levels were further validated using qRT-PCR and western blot. We identified DEGs between high and low expression groups of key biomarkers. Enrichment analysis was carried out to discover the target gene’s biological function. We analyzed the relationship between key biomarker expression and patient clinical features. Finally, the immune cells that infiltrate endometriosis were assessed using the Microenvironment Cell Population-Counter (MCP-counter), and the correlation of biomarker expression with immune cell infiltration and immune checkpoints genes was studied. Results: There were a total of 38 DEGs discovered. Two machine learning techniques were used to identify 10 genes. Six biomarkers (SCG2, ASPN, SLIT2, GEM, EGR1, and FOS) had good diagnostic efficiency (AUC > 0.7) by internal and external validation. We excluded previously reported related genes (SLIT2, EGR1, and FOS). ASPN was the most significantly differentially expressed biomarker between normal and ectopic endometrial tissues, as verified by qPCR. The western blot assay revealed a significant upregulation of ASPN expression in endometriotic tissues. The investigation for DEGs in the ASPN high- and low-expression groups revealed that the DEGs were particularly enriched in extracellular matrix tissue, vascular smooth muscle contraction, cytokine interactions, the calcium signaling pathway, and the chemokine signaling pathway. High ASPN expression was related to r-AFS stage (p = 0.006), age (p = 0.03), and lesion location (p < 0.001). Univariate and multivariate logistic regression analysis showed that ASPN expression was an independent influencing factor in patients with endometriosis. Immune cell infiltration analysis revealed a significant increase in T-cell, B-cell, and fibroblast infiltration in endometriosis lesions; cytotoxic lymphocyte, NK-cell, and endothelial cell infiltration were reduced. Additionally, the percentage of T cells, B cells, fibroblasts, and endothelial cells was favorably connected with ASPN expression, while the percentage of cytotoxic lymphocytes and NK cells was negatively correlated. Immune checkpoint gene (CTLA4, LAG3, CD27, CD40, and ICOS) expression and ASPN expression were positively associated. Conclusions: Increased expression of ASPN is associated with immune infiltration in endometriosis, and ASPN can be used as a diagnostic biomarker as well as a potential immunotherapeutic target in endometriosis. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning in Disease Research)
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