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Development and Application of Bioinformatics in Personalized Medicine
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Development and Application of Bioinformatics in Personalized Medicine

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Omics/Informatics".

Deadline for manuscript submissions: closed (5 February 2023) | Viewed by 12259

Special Issue Editor

Dr. Panagiotis Moulos

E-Mail Website
Guest Editor
Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center ‘Alexander Fleming’, Athens, Greece
Interests: bioinformatics; genomics; clinical bioinformatics; biostatistics

Special Issue Information

Dear Colleagues,

We live in the era of personalized medicine where technological advancements provide the means to systematically and holistically investigate the human genome and use the findings towards better healthcare and well-being. Such advancements in combination with vast amounts of research data accumulated over the past couple of decades offer unique opportunities for breakthrough discoveries through Bioinformatics which is the realization of Data Science in Life Sciences. Furthermore, technological breakthroughs in genomics such as Next Generation Sequencing (NGS), proteomics, metabolomics, and other -omics techniques have boosted the domains of Clinical -omics – the combined investigation of the human genome, proteome, and metabolome for clinical applications – and as a result, the emergence of Clinical Bioinformatics is prevalent.

We are pleased to invite you to submit papers with the results achieved with the application of bioinformatics personalized medicine approaches, including but not limited to the single and/or combined usage of -omics techniques. Articles presenting state of the art methodological aspects such as new algorithms and software focusing on and promoting personalized medicine and healthcare are welcome.

This Special Issue aims to familiarize researchers in basic and applied research, as well as physicians with the latest advances in applying bioinformatics to promote and improve personalized medicine, approaches taking advantage of modern high resolution -omics data. 

In this Special Issue, original research, methodological and software articles, and reviews are welcome. Research areas may include (but not limited to) the development and utilization of bioinformatics approaches for the following:

•    Personalized treatments
•    Development of molecular disease signatures
•    Disease-specific -omics databases
•    Disease-specific knowledge and biomarker databases
•    Multi-omics integrations for disease characterization and treatment
•    Discovery of single or multi-omics disease biomarkers
•    Big -omics data for personalized medicine
•    Imaging techniques and analysis for personalized treatment
•    Algorithms for -omics data analysis and integration with the goal of providing personalized treatments
•    Scientific software centered around personalized medicine and treatment
•    Single-cell -omics and bioinformatics for personalized medicine
•    Development of Polygenic Risk Scores related to disease
•    Pharmacogenomics, pharmacoproteomics, and pharmacometabolomics

We look forward to receiving your contributions.

Dr. Panagiotis Moulos
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. Journal of Personalized Medicine 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

  • bioinformatics
  • personalized medicine
  • clinical genomics
  • clinical proteomics
  • clinical metabolomics
  • clinical genetics
  • algorithms
  • software
  • disease signatures
  • pharmacogenomics

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

13 pages, 3722 KiB  
Article
Elucidating the Potential Inhibitor against Type 2 Diabetes Mellitus Associated Gene of GLUT4
by Afaf Aldahish, Prasanalakshmi Balaji, Rajalakshimi Vasudevan, Geetha Kandasamy, Jainey P. James and Kousalya Prabahar
J. Pers. Med. 2023, 13(4), 660; https://doi.org/10.3390/jpm13040660 - 12 Apr 2023
Cited by 2 | Viewed by 2353
Abstract
Diabetes is a chronic hyperglycemic disorder that leads to a group of metabolic diseases. This condition of chronic hyperglycemia is caused by abnormal insulin levels. The impact of hyperglycemia on the human vascular tree is the leading cause of disease and death in [...] Read more.
Diabetes is a chronic hyperglycemic disorder that leads to a group of metabolic diseases. This condition of chronic hyperglycemia is caused by abnormal insulin levels. The impact of hyperglycemia on the human vascular tree is the leading cause of disease and death in type 1 and type 2 diabetes. People with type 2 diabetes mellitus (T2DM) have abnormal secretion as well as the action of insulin. Type 2 (non-insulin-dependent) diabetes is caused by a combination of genetic factors associated with decreased insulin production, insulin resistance, and environmental conditions. These conditions include overeating, lack of exercise, obesity, and aging. Glucose transport limits the rate of dietary glucose used by fat and muscle. The glucose transporter GLUT4 is kept intracellular and sorted dynamically, and GLUT4 translocation or insulin-regulated vesicular traffic distributes it to the plasma membrane. Different chemical compounds have antidiabetic properties. The complexity, metabolism, digestion, and interaction of these chemical compounds make it difficult to understand and apply them to reduce chronic inflammation and thus prevent chronic disease. In this study, we have applied a virtual screening approach to screen the most suitable and drug-able chemical compounds to be used as potential drug targets against T2DM. We have found that out of 5000 chemical compounds that we have analyzed, only two are known to be more effective as per our experiments based upon molecular docking studies and virtual screening through Lipinski’s rule and ADMET properties. Full article
(This article belongs to the Special Issue Development and Application of Bioinformatics in Personalized Medicine)
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14 pages, 993 KiB  
Article
Robust Bioinformatics Approaches Result in the First Polygenic Risk Score for BMI in Greek Adults
by Maria Kafyra, Ioanna Panagiota Kalafati, Maria Dimitriou, Effimia Grigoriou, Alexandros Kokkinos, Loukianos Rallidis, Genovefa Kolovou, Georgios Trovas, Eirini Marouli, Panos Deloukas, Panagiotis Moulos and George V. Dedoussis
J. Pers. Med. 2023, 13(2), 327; https://doi.org/10.3390/jpm13020327 - 14 Feb 2023
Viewed by 2190
Abstract
Quantifying the role of genetics via construction of polygenic risk scores (PRSs) is deemed a resourceful tool to enable and promote effective obesity prevention strategies. The present paper proposes a novel methodology for PRS extraction and presents the first PRS for body mass [...] Read more.
Quantifying the role of genetics via construction of polygenic risk scores (PRSs) is deemed a resourceful tool to enable and promote effective obesity prevention strategies. The present paper proposes a novel methodology for PRS extraction and presents the first PRS for body mass index (BMI) in a Greek population. A novel pipeline for PRS derivation was used to analyze genetic data from a unified database of three cohorts of Greek adults. The pipeline spans various steps of the process, from iterative dataset splitting to training and test partitions, calculation of summary statistics and PRS extraction, up to PRS aggregation and stabilization, achieving higher evaluation metrics. Using data from 2185 participants, implementation of the pipeline enabled consecutive repetitions in splitting training and testing samples and resulted in a 343-single nucleotide polymorphism PRS yielding an R2 = 0.3241 (beta = 1.011, p-value = 4 × 10−193) for BMI. PRS-included variants displayed a variety of associations with known traits (i.e., blood cell count, gut microbiome, lifestyle parameters). The proposed methodology led to creation of the first-ever PRS for BMI in Greek adults and aims at promoting a facilitating approach to reliable PRS development and integration in healthcare practice. Full article
(This article belongs to the Special Issue Development and Application of Bioinformatics in Personalized Medicine)
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17 pages, 1715 KiB  
Article
Multi-Objective Genetic Algorithm for Cluster Analysis of Single-Cell Transcriptomes
by Konghao Zhao, Jason M. Grayson and Natalia Khuri
J. Pers. Med. 2023, 13(2), 183; https://doi.org/10.3390/jpm13020183 - 20 Jan 2023
Cited by 4 | Viewed by 3180
Abstract
Cells are the basic building blocks of human organisms, and the identification of their types and states in transcriptomic data is an important and challenging task. Many of the existing approaches to cell-type prediction are based on clustering methods that optimize only one [...] Read more.
Cells are the basic building blocks of human organisms, and the identification of their types and states in transcriptomic data is an important and challenging task. Many of the existing approaches to cell-type prediction are based on clustering methods that optimize only one criterion. In this paper, a multi-objective Genetic Algorithm for cluster analysis is proposed, implemented, and systematically validated on 48 experimental and 60 synthetic datasets. The results demonstrate that the performance and the accuracy of the proposed algorithm are reproducible, stable, and better than those of single-objective clustering methods. Computational run times of multi-objective clustering of large datasets were studied and used in supervised machine learning to accurately predict the execution times of clustering of new single-cell transcriptomes. Full article
(This article belongs to the Special Issue Development and Application of Bioinformatics in Personalized Medicine)
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21 pages, 3238 KiB  
Article
Image Analysis in Digital Pathology Utilizing Machine Learning and Deep Neural Networks
by Paris Amerikanos and Ilias Maglogiannis
J. Pers. Med. 2022, 12(9), 1444; https://doi.org/10.3390/jpm12091444 - 1 Sep 2022
Cited by 19 | Viewed by 3748
Abstract
Detection of regions of interest (ROIs) in whole slide images (WSIs) in a clinical setting is a highly subjective and a labor-intensive task. In this work, recent developments in machine learning and computer vision algorithms are presented to assess their possible usage and [...] Read more.
Detection of regions of interest (ROIs) in whole slide images (WSIs) in a clinical setting is a highly subjective and a labor-intensive task. In this work, recent developments in machine learning and computer vision algorithms are presented to assess their possible usage and performance to enhance and accelerate clinical pathology procedures, such as ROI detection in WSIs. In this context, a state-of-the-art deep learning framework (Detectron2) was trained on two cases linked to the TUPAC16 dataset for object detection and on the JPATHOL dataset for instance segmentation. The predictions were evaluated against competing models and further possible improvements are discussed. Full article
(This article belongs to the Special Issue Development and Application of Bioinformatics in Personalized Medicine)
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