Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
New Challenges in Integrative Biomedical Data Analysis
share announcement

New Challenges in Integrative Biomedical Data Analysis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (10 October 2021) | Viewed by 5640

Special Issue Editor

Dr. Je-Keun Rhee

E-Mail Website
Guest Editor
Department of Bioinformatics & Life Science, Soongsil University, Seoul 06978, Korea
Interests: bioinformatics; machine learning; genome informatics; cancer genomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The recent tremendous accumulation of biomedical data offers challenges and opportunities to understand biological phenomena and disease. In particular, biomedical data have been generated from multiple perspectives such as the genome, transcriptome, proteome, epigenome, microbiome, clinical information including electronic health records, lifestyle, and so on. In many cases, however, biomedical phenomena cannot be understood by a single data type. For instance, molecular profiling data can be much more helpful for precision medicine when it has been analyzed in a clinical setting. Moreover, integrative analysis of multi-omics data can help us to understand the complex biological systems and derive insights into cellular functions.

Thus, the development of methods to combine and analyze multiple biomedical data is one of the key challenges in the biomedical domain. Comprehensive and relevant analysis will answer many biomedical questions by overcoming the limitations of singular data.

This Special Issue welcomes both review papers and original research works for integrative biomedical data analysis, along with their applications. The topics include (but are not limited to) the following:

  • Biomedical discovery using multi-omics data;
  • Data analysis based on statistics/machine learning/data mining;
  • Pharmacogenomics, drug repositioning, computational drug discovery;
  • Radiogenomics;
  • Translational medicine;
  • High-performance computing;
  • Time-course data analysis;
  • Data preprocessing and cleaning;
  • Meta-analysis in biomedical research;
  • Standards for biomedical/clinical/healthcare data.

Prof. Dr. Je-Keun Rhee
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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • bioinformatics
  • multi-omics
  • machine learning
  • pharmacogenomics
  • drug repositioning
  • computational drug discovery
  • radiogenomics
  • translational medicine
  • high-performance computing for biomedical data
  • time series analysis of biomedical data
  • meta-analysis in biomedical research
  • standards for biomedical/clinical/healthcare data

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 1315 KiB  
Article
Pan-Cancer Analysis for Immune Cell Infiltration and Mutational Signatures Using Non-Negative Canonical Correlation Analysis
by Je-Keun Rhee
Appl. Sci. 2022, 12(13), 6596; https://doi.org/10.3390/app12136596 - 29 Jun 2022
Viewed by 1376
Abstract
Mutational signatures indicate the mutational processes and substitution patterns in cancer cell genomes. However, the functional consequences of mutational signatures remain unclear, and there have been no comprehensive systematic studies to examine the relationships between the mutational signatures and the immune cell infiltration. [...] Read more.
Mutational signatures indicate the mutational processes and substitution patterns in cancer cell genomes. However, the functional consequences of mutational signatures remain unclear, and there have been no comprehensive systematic studies to examine the relationships between the mutational signatures and the immune cell infiltration. Here, the relationship between mutational signatures and immune cell infiltration using non-negative canonical correlation analysis based on 8927 patients across 25 tumor types was investigated. By inspecting mutational signatures with the maximal coefficients determined by the non-negative canonical correlation analysis, the study identified mutational signatures related to immune cell infiltration composed of tumor microenvironments. The analysis was validated by showing that the genes associated with the identified mutational signatures were linked to overall survival by a Kaplan–Meier curve and a log-rank test and were mainly related to immunity by gene set enrichment analysis. These results will help expand our knowledge of tumor biology and recognize the functional roles and associations of immune systems with mutational signatures. Full article
(This article belongs to the Special Issue New Challenges in Integrative Biomedical Data Analysis)
►▼ Show Figures

Figure 1

24 pages, 8447 KiB  
Article
New Insight into Breast Cancer Cells Involving Drug Combinations for Dopamine and Serotonin Receptors
by Bárbara Costa, Rita Matos, Irina Amorim, Fátima Gärtner and Nuno Vale
Appl. Sci. 2021, 11(13), 6082; https://doi.org/10.3390/app11136082 - 30 Jun 2021
Cited by 3 | Viewed by 2151
Abstract
The breast cancer therapies available are insufficient, especially since first-line treatments, such as paclitaxel, result in drug resistance and their toxicity often limits their concentration. Strategies like drug repurposing are beneficial, and novel treatments can emerge by repurposing drugs that interfere with the [...] Read more.
The breast cancer therapies available are insufficient, especially since first-line treatments, such as paclitaxel, result in drug resistance and their toxicity often limits their concentration. Strategies like drug repurposing are beneficial, and novel treatments can emerge by repurposing drugs that interfere with the dopamine and serotonin receptors, and thus influence tumor growth. In this study, the MTT assay was used to test the efficacy of such repurposed drugs commonly used for neurodegenerative disorders that act on the dopamine and serotonin receptors to reduce the MCF-7 cell’s viability, either by their single use or in combination with the reference drug paclitaxel. Furthermore, the expression of vimentin and E-cadherin was assayed by immunofluorescence. The dopamine receptor-altering drugs benztropine and thioridazine resulted in the strongest reduction of cell viability when combined with paclitaxel, which may be connected to the alteration of E-cadherin rather than vimentin expression. More studies are needed to understand the mechanism of action of the combinations tested and the efficacious role of dopamine and serotonin. Full article
(This article belongs to the Special Issue New Challenges in Integrative Biomedical Data Analysis)
►▼ Show Figures

Figure 1

18 pages, 2369 KiB  
Article
Multi-Data Aspects of Protein Similarity with a Learning Technique to Identify Drug-Disease Associations
by Satanat Kitsiranuwat, Apichat Suratanee and Kitiporn Plaimas
Appl. Sci. 2021, 11(7), 2914; https://doi.org/10.3390/app11072914 - 24 Mar 2021
Cited by 4 | Viewed by 1610
Abstract
Drug repositioning has been proposed to develop drugs for diseases. However, the similarity in a single aspect may not be sufficient to reveal hidden information. Therefore, we established protein–protein similarity vectors (PPSVs) based on potential similarities in various types of biological information associated [...] Read more.
Drug repositioning has been proposed to develop drugs for diseases. However, the similarity in a single aspect may not be sufficient to reveal hidden information. Therefore, we established protein–protein similarity vectors (PPSVs) based on potential similarities in various types of biological information associated with proteins, including their network topology, proteomic data, functional analysis, and druggable property. Based on the proposed PPSVs, a separate drug–disease matrix was constructed for individual to prevent characteristics from being obscured between diseases. The classification technique was employed for prediction. The results showed that more than half of the tested disease models exhibited high performance, with overall F1 scores of more than 80%. Furthermore, comparing all diseases using traditional methods in one run, we obtained an (area under the curve) AUC of 98.9%. All candidate drugs were then tested in clinical trials (p-value < 2.2 × 10−16) and were known drugs based on their functions (p-value < 0.05). An analysis revealed that, in the functional aspect, the confidence value of an interaction in the protein–protein interaction network and the functional pathway score were the best descriptors for prediction. Based on the learning processes of PPSVs with an isolated disease, the classifier exhibited high performance in predicting and identifying new potential drugs for that disease. Full article
(This article belongs to the Special Issue New Challenges in Integrative Biomedical Data Analysis)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop