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Bioinformatics of Disease Research
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Bioinformatics of Disease Research

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

Deadline for manuscript submissions: 10 May 2024 | Viewed by 3004

Special Issue Editor

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

Special Issue Information

Dear Colleagues,

Recent technological advances, including that of DNA sequencing, have enabled us to understand most of our diseases in terms of genetic information, which is stored as massive amounts of data. Thus, in modern medical research, computational methods in the analyses of such genetic data are essential. Nevertheless, in my opinion, the value of computational works based on pure public data still tends to be underestimated. It is true that there are works with less novelty, typically just applying existing tools to public data and/or just repeating very similar procedures to another dataset. However, there are also plenty of pure computational works reporting novel/significant biomedical discoveries based on a combination of public data on genomics/epigenomics. In this Special Issue, I would like to invite the submission of the latter kind of work, hoping that this Special Issue will become a showcase of valuable computational works even if they are based on public data only. Of course, we will also welcome manuscripts based on their own wet experiments if they are valuable in terms of the bioinformatics of disease research. We look forward to your submission.

Prof. Dr. Kenta Nakai
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 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

  • computational approach
  • combination of public data
  • genomics/epigenomics
  • biomarkers in disease
  • multi-omics study
  • application of AI techniques
  • medical informatics

Published Papers (2 papers)

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Research

18 pages, 3455 KiB  
Article
Deciphering the Immune Microenvironment at the Forefront of Tumor Aggressiveness by Constructing a Regulatory Network with Single-Cell and Spatial Transcriptomic Data
by Kun Xu, Dongshuo Yu, Siwen Zhang, Lanming Chen, Zhenhao Liu and Lu Xie
Genes 2024, 15(1), 100; https://0-doi-org.brum.beds.ac.uk/10.3390/genes15010100 - 15 Jan 2024
Viewed by 1525
Abstract
The heterogeneity and intricate cellular architecture of complex cellular ecosystems play a crucial role in the progression and therapeutic response of cancer. Understanding the regulatory relationships of malignant cells at the invasive front of the tumor microenvironment (TME) is important to explore the [...] Read more.
The heterogeneity and intricate cellular architecture of complex cellular ecosystems play a crucial role in the progression and therapeutic response of cancer. Understanding the regulatory relationships of malignant cells at the invasive front of the tumor microenvironment (TME) is important to explore the heterogeneity of the TME and its role in disease progression. In this study, we inferred malignant cells at the invasion front by analyzing single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data of ER-positive (ER+) breast cancer patients. In addition, we developed a software pipeline for constructing intercellular gene regulatory networks (IGRNs), which help to reduce errors generated by single-cell communication analysis and increase the confidence of selected cell communication signals. Based on the constructed IGRN between malignant cells at the invasive front of the TME and the immune cells of ER+ breast cancer patients, we found that a high expression of the transcription factors FOXA1 and EZH2 played a key role in driving tumor progression. Meanwhile, elevated levels of their downstream target genes (ESR1 and CDKN1A) were associated with poor prognosis of breast cancer patients. This study demonstrates a bioinformatics workflow of combining scRNA-seq and ST data; in addition, the study provides the software pipelines for constructing IGRNs automatically (cIGRN). This strategy will help decipher cancer progression by revealing bidirectional signaling between invasive frontline malignant tumor cells and immune cells, and the selected signaling molecules in the regulatory network may serve as biomarkers for mechanism studies or therapeutic targets. Full article
(This article belongs to the Special Issue Bioinformatics of Disease Research)
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16 pages, 2940 KiB  
Article
The Spherical Evolutionary Multi-Objective (SEMO) Algorithm for Identifying Disease Multi-Locus SNP Interactions
by Fuxiang Ren, Shiyin Li, Zihao Wen, Yidi Liu and Deyu Tang
Genes 2024, 15(1), 11; https://0-doi-org.brum.beds.ac.uk/10.3390/genes15010011 - 20 Dec 2023
Viewed by 784
Abstract
Single-nucleotide polymorphisms (SNPs), as disease-related biogenetic markers, are crucial in elucidating complex disease susceptibility and pathogenesis. Due to computational inefficiency, it is difficult to identify high-dimensional SNP interactions efficiently using combinatorial search methods, so the spherical evolutionary multi-objective (SEMO) algorithm for detecting multi-locus [...] Read more.
Single-nucleotide polymorphisms (SNPs), as disease-related biogenetic markers, are crucial in elucidating complex disease susceptibility and pathogenesis. Due to computational inefficiency, it is difficult to identify high-dimensional SNP interactions efficiently using combinatorial search methods, so the spherical evolutionary multi-objective (SEMO) algorithm for detecting multi-locus SNP interactions was proposed. The algorithm uses a spherical search factor and a feedback mechanism of excellent individual history memory to enhance the balance between search and acquisition. Moreover, a multi-objective fitness function based on the decomposition idea was used to evaluate the associations by combining two functions, K2-Score and LR-Score, as an objective function for the algorithm’s evolutionary iterations. The performance evaluation of SEMO was compared with six state-of-the-art algorithms on a simulated dataset. The results showed that SEMO outperforms the comparative methods by detecting SNP interactions quickly and accurately with a shorter average run time. The SEMO algorithm was applied to the Wellcome Trust Case Control Consortium (WTCCC) breast cancer dataset and detected two- and three-point SNP interactions that were significantly associated with breast cancer, confirming the effectiveness of the algorithm. New combinations of SNPs associated with breast cancer were also identified, which will provide a new way to detect SNP interactions quickly and accurately. Full article
(This article belongs to the Special Issue Bioinformatics of Disease Research)
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