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Recent Research on Bioinformatics for Precision Medicine
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Recent Research on Bioinformatics for Precision Medicine

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 4970

Special Issue Editor

Prof. Dr. George M. Spyrou

E-Mail Website
Guest Editor
1. The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus
2. Cyprus School of Molecular Medicine, P.O. Box 23462, 1683 Nicosia, Cyprus
Interests: systems bioinformatics; network-based analysis and integration; computational methods for biomarker discovery and drug repurposing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precision medicine represents a novel and revolutionary approach, by which a specific treatment is tailored on the basis of each patient’s epigenetic, molecular, and environmental characteristics. Bioinformatics for Precision Medicine is the information technology to develop medical knowledge and facilitate the delivery of patient medical care and clinical decision-making. In this regard, several therapies targeting specific molecular mechanisms have been implemented in medical practice.

In this scenario, precision medicine focuses on patients' diseases at different levels (from the genetic level to the clinical level), integrates multi-omics data, and seeks targeted treatments for each individual disease. Informatics techniques such as statistical methods, deep learning, and machine learning are opening up a new and effective way for personalized precision medicine.

In this Special Issue, we would focus on advanced bioinformatics for precision medicine and encourage research on methodologies, mechanism discovery, and tool development that enables precision medicine. This Special Issue calls for original research articles and reviews that address the progress and current understanding of the overlapping research topics：

  • Prediction of novel biomarkers
  • High-performance computing system application
  • Genomics markers knowledge discovery
  • Statistical models for cancer data analysis
  • Network medicine
  • Computational tools for precision/personalized medicine
  • Network-based diagnostics and therapeutics

Prof. Dr. George M. Spyrou
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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

  • bioinformatics
  • precision medicine
  • multi-omics data
  • deep learning

Published Papers (4 papers)

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Research

13 pages, 563 KiB  
Article
Differential Gene Regulatory Network Analysis between Azacitidine-Sensitive and -Resistant Cell Lines
by Heewon Park and Satoru Miyano
Int. J. Mol. Sci. 2024, 25(6), 3302; https://doi.org/10.3390/ijms25063302 - 14 Mar 2024
Viewed by 675
Abstract
Azacitidine, a DNA methylation inhibitor, is employed for the treatment of acute myeloid leukemia (AML). However, drug resistance remains a major challenge for effective azacitidine chemotherapy, though several studies have attempted to uncover the mechanisms of azacitidine resistance. With the aim to identify [...] Read more.
Azacitidine, a DNA methylation inhibitor, is employed for the treatment of acute myeloid leukemia (AML). However, drug resistance remains a major challenge for effective azacitidine chemotherapy, though several studies have attempted to uncover the mechanisms of azacitidine resistance. With the aim to identify the mechanisms underlying acquired azacitidine resistance in cancer cell lines, we developed a computational strategy that can identify differentially regulated gene networks between drug-sensitive and -resistant cell lines by extending the existing method, differentially coexpressed gene sets (DiffCoEx). The technique specifically focuses on cell line-specific gene network analysis. We applied our method to gene networks specific to azacitidine sensitivity and identified differentially regulated gene networks between azacitidine-sensitive and -resistant cell lines. The molecular interplay between the metallothionein gene family, C19orf33, ELF3, GRB7, IL18, NRN1, and RBM47 were identified as differentially regulated gene network in drug resistant cell lines. The biological mechanisms associated with azacitidine and AML for the markers in the identified networks were verified through the literature. Our results suggest that controlling the identified genes (e.g., the metallothionein gene family) and “cellular response”-related pathways (“cellular response to zinc ion”, “cellular response to copper ion”, and “cellular response to cadmium ion”, where the enriched functional-related genes are MT2A, MT1F, MT1G, and MT1E) may provide crucial clues to address azacitidine resistance in patients with AML. We expect that our strategy will be a useful tool to uncover patient-specific molecular interplay that provides crucial clues for precision medicine in not only gastric cancer but also complex diseases. Full article
(This article belongs to the Special Issue Recent Research on Bioinformatics for Precision Medicine)
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19 pages, 3441 KiB  
Article
Interpretable Multi-Scale Deep Learning for RNA Methylation Analysis across Multiple Species
by Rulan Wang, Chia-Ru Chung and Tzong-Yi Lee
Int. J. Mol. Sci. 2024, 25(5), 2869; https://doi.org/10.3390/ijms25052869 - 1 Mar 2024
Viewed by 759
Abstract
RNA modification plays a crucial role in cellular regulation. However, traditional high-throughput sequencing methods for elucidating their functional mechanisms are time-consuming and labor-intensive, despite extensive research. Moreover, existing methods often limit their focus to specific species, neglecting the simultaneous exploration of RNA modifications [...] Read more.
RNA modification plays a crucial role in cellular regulation. However, traditional high-throughput sequencing methods for elucidating their functional mechanisms are time-consuming and labor-intensive, despite extensive research. Moreover, existing methods often limit their focus to specific species, neglecting the simultaneous exploration of RNA modifications across diverse species. Therefore, a versatile computational approach is necessary for interpretable analysis of RNA modifications across species. A multi-scale biological language-based deep learning model is proposed for interpretable, sequential-level prediction of diverse RNA modifications. Benchmark comparisons across species demonstrate the model’s superiority in predicting various RNA methylation types over current state-of-the-art methods. The cross-species validation and attention weight visualization also highlight the model’s capability to capture sequential and functional semantics from genomic backgrounds. Our analysis of RNA modifications helps us find the potential existence of “biological grammars” in each modification type, which could be effective for mapping methylation-related sequential patterns and understanding the underlying biological mechanisms of RNA modifications. Full article
(This article belongs to the Special Issue Recent Research on Bioinformatics for Precision Medicine)
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34 pages, 5845 KiB  
Article
Screening and Structural Characterization of Heat Shock Response Elements (HSEs) in Entamoeba histolytica Promoters
by David Dorantes-Palma, Salvador Pérez-Mora, Elisa Azuara-Liceaga, Ernesto Pérez-Rueda, David Guillermo Pérez-Ishiwara, Misael Coca-González, María Olivia Medel-Flores and Consuelo Gómez-García
Int. J. Mol. Sci. 2024, 25(2), 1319; https://doi.org/10.3390/ijms25021319 - 21 Jan 2024
Cited by 1 | Viewed by 1208
Abstract
Entamoeba histolytica (E. histolytica) exhibits a remarkable capacity to respond to thermal shock stress through a sophisticated genetic regulation mechanism. This process is carried out via Heat Shock Response Elements (HSEs), which are recognized by Heat Shock Transcription Factors (EhHSTFs), enabling [...] Read more.
Entamoeba histolytica (E. histolytica) exhibits a remarkable capacity to respond to thermal shock stress through a sophisticated genetic regulation mechanism. This process is carried out via Heat Shock Response Elements (HSEs), which are recognized by Heat Shock Transcription Factors (EhHSTFs), enabling fine and precise control of gene expression. Our study focused on screening for HSEs in the promoters of the E. histolytica genome, specifically analyzing six HSEs, including Ehpgp5, EhrabB1, EhrabB4, EhrabB5, Ehmlbp, and Ehhsp100. We discovered 2578 HSEs, with 1412 in promoters of hypothetical genes and 1166 in coding genes. We observed that a single promoter could contain anywhere from one to five HSEs. Gene ontology analysis revealed the presence of HSEs in essential genes for the amoeba, including cysteine proteinases, ribosomal genes, Myb family DNA-binding proteins, and Rab GTPases, among others. Complementarily, our molecular docking analyses indicate that these HSEs are potentially recognized by EhHSTF5, EhHSTF6, and EhHSTF7 factors in their trimeric conformation. These findings suggest that E. histolytica has the capability to regulate a wide range of critical genes via HSE-EhHSTFs, not only for thermal stress response but also for vital functions of the parasite. This is the first comprehensive study of HSEs in the genome of E. histolytica, significantly contributing to the understanding of its genetic regulation and highlighting the complexity and precision of this mechanism in the parasite’s survival. Full article
(This article belongs to the Special Issue Recent Research on Bioinformatics for Precision Medicine)
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14 pages, 5892 KiB  
Article
3D Visualization, Skeletonization and Branching Analysis of Blood Vessels in Angiogenesis
by Vignesh Ramakrishnan, Rebecca Schönmehl, Annalena Artinger, Lina Winter, Hendrik Böck, Stephan Schreml, Florian Gürtler, Jimmy Daza, Volker H. Schmitt, Andreas Mamilos, Pablo Arbelaez, Andreas Teufel, Tanja Niedermair, Ondrej Topolcan, Marie Karlíková, Samuel Sossalla, Christoph B. Wiedenroth, Markus Rupp and Christoph Brochhausen
Int. J. Mol. Sci. 2023, 24(9), 7714; https://doi.org/10.3390/ijms24097714 - 23 Apr 2023
Cited by 5 | Viewed by 1646
Abstract
Angiogenesis is the process of new blood vessels growing from existing vasculature. Visualizing them as a three-dimensional (3D) model is a challenging, yet relevant, task as it would be of great help to researchers, pathologists, and medical doctors. A branching analysis on the [...] Read more.
Angiogenesis is the process of new blood vessels growing from existing vasculature. Visualizing them as a three-dimensional (3D) model is a challenging, yet relevant, task as it would be of great help to researchers, pathologists, and medical doctors. A branching analysis on the 3D model would further facilitate research and diagnostic purposes. In this paper, a pipeline of vision algorithms is elaborated to visualize and analyze blood vessels in 3D from formalin-fixed paraffin-embedded (FFPE) granulation tissue sections with two different staining methods. First, a U-net neural network is used to segment blood vessels from the tissues. Second, image registration is used to align the consecutive images. Coarse registration using an image-intensity optimization technique, followed by finetuning using a neural network based on Spatial Transformers, results in an excellent alignment of images. Lastly, the corresponding segmented masks depicting the blood vessels are aligned and interpolated using the results of the image registration, resulting in a visualized 3D model. Additionally, a skeletonization algorithm is used to analyze the branching characteristics of the 3D vascular model. In summary, computer vision and deep learning is used to reconstruct, visualize and analyze a 3D vascular model from a set of parallel tissue samples. Our technique opens innovative perspectives in the pathophysiological understanding of vascular morphogenesis under different pathophysiological conditions and its potential diagnostic role. Full article
(This article belongs to the Special Issue Recent Research on Bioinformatics for Precision Medicine)
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