Special Issue "Systems Medicine and Bioinformatics"

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

Deadline for manuscript submissions: 20 July 2022.

Special Issue Editors

Dr. Ali Salehzadeh-Yazdi
E-Mail Website
Guest Editor
Research Group Quantitative and Systems Biology, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany
Interests: systems medicine; omics data integration; regulation of gene expression; metabolic modeling; protein–protein interaction; signaling pathways analysis; drug repurposing; bioinformatics and computational biology
Dr. Mohieddin Jafari
E-Mail Website
Guest Editor
Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Interests: proteomics; systems biology; mass spectrometry; bioinformatics; biological data mining; omics data integration; machine learning methods; network biology; network pharmacology

Special Issue Information

Dear Colleagues,

In precision medicine, determining the right treatment for the right patients based on a precise diagnosis is the ultimate goal. It guides us to provide considerable progress to our knowledge of disease etiology and pathogenesis and have a determinant impact on proposing novel potential biomarkers suitable for clinical practice.

To achieve this goal, molecular features and clinical phenotypes are used to perform the correct stratification of patients and classify the corresponding features. Recent advances in sequencing and multi-omics technologies transform biomedical research and change healthcare and medicine by providing big data as molecular and clinical features. In parallel, advanced statistical, computational, and mathematical tools to analyze, integrate, interpret big heterogeneous datasets, and develop multilevel models are demanded.

We encourage the submission of original and review articles to this Special Issue of the Journal of Personalized Medicine that cover the "Systems medicine and bioinformatics". Articles focused on empirical and computational studies, implementation of new models along with the validations, focusing on the barriers to expanding systems medicine into personalized and precision medicine will be welcome.

Dr. Ali Salehzadeh-Yazdi
Dr. Mohieddin Jafari
Guest Editors

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 papers will be 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 2000 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

  • systems medicine
  • high-throughput datasets analysis (genomic, epigenomic, functional genomic, etc.)
  • omics data integration
  • human disease network
  • network pharmacology
  • drug repurposing (drug repositioning)
  • machine learning methods
  • computational models and algorithms
  • phenotypes prediction
  • drug target prediction
  • signaling pathways analysis

Published Papers (11 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Article
ICBrainDB: An Integrated Database for Finding Associations between Genetic Factors and EEG Markers of Depressive Disorders
J. Pers. Med. 2022, 12(1), 53; https://doi.org/10.3390/jpm12010053 - 05 Jan 2022
Viewed by 141
Abstract
In this study, we collected and systemized diverse information related to depressive and anxiety disorders as the first step on the way to investigate the associations between molecular genetics, electrophysiological, behavioral, and psychological characteristics of people. Keeping that in mind, we developed an [...] Read more.
In this study, we collected and systemized diverse information related to depressive and anxiety disorders as the first step on the way to investigate the associations between molecular genetics, electrophysiological, behavioral, and psychological characteristics of people. Keeping that in mind, we developed an internet resource including a database and tools for primary presentation of the collected data of genetic factors, the results of electroencephalography (EEG) tests, and psychological questionnaires. The sample of our study was 1010 people from different regions of Russia. We created the integrated ICBrainDB database that enables users to easily access, download, and further process information about individual behavioral characteristics and psychophysiological responses along with inherited trait data. The data obtained can be useful in training neural networks and in machine learning construction processes in Big Data analysis. We believe that the existence of such a resource will play an important role in the further search for associations of genetic factors and EEG markers of depression. Full article
(This article belongs to the Special Issue Systems Medicine and Bioinformatics)
Show Figures

Figure 1

Article
Risk-Profile and Feature Selection Comparison in Diabetic Retinopathy
J. Pers. Med. 2021, 11(12), 1327; https://doi.org/10.3390/jpm11121327 - 08 Dec 2021
Viewed by 520
Abstract
One of the main microvascular complications presented in the Mexican population is diabetic retinopathy which affects 27.50% of individuals with type 2 diabetes. Therefore, the purpose of this study is to construct a predictive model to find out the risk factors of this [...] Read more.
One of the main microvascular complications presented in the Mexican population is diabetic retinopathy which affects 27.50% of individuals with type 2 diabetes. Therefore, the purpose of this study is to construct a predictive model to find out the risk factors of this complication. The dataset contained a total of 298 subjects, including clinical and paraclinical features. An analysis was constructed using machine learning techniques including Boruta as a feature selection method, and random forest as classification algorithm. The model was evaluated through a statistical test based on sensitivity, specificity, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results present significant values obtained by the model obtaining 69% of AUC. Moreover, a risk evaluation was incorporated to evaluate the impact of the predictors. The proposed method identifies creatinine, lipid treatment, glomerular filtration rate, waist hip ratio, total cholesterol, and high density lipoprotein as risk factors in Mexican subjects. The odds ratio increases by 3.5916 times for control patients which have high levels of cholesterol. It is possible to conclude that this proposed methodology is a preliminary computer-aided diagnosis tool for clinical decision-helping to identify the diagnosis of DR. Full article
(This article belongs to the Special Issue Systems Medicine and Bioinformatics)
Show Figures

Figure 1

Article
Mapping Compound Databases to Disease Maps—A MINERVA Plugin for CandActBase
J. Pers. Med. 2021, 11(11), 1072; https://doi.org/10.3390/jpm11111072 - 24 Oct 2021
Viewed by 429
Abstract
The MINERVA platform is currently the most widely used platform for visualizing and providing access to disease maps. Disease maps are systems biological maps of molecular interactions relevant in a certain disease context, where they can be used to support drug discovery. For [...] Read more.
The MINERVA platform is currently the most widely used platform for visualizing and providing access to disease maps. Disease maps are systems biological maps of molecular interactions relevant in a certain disease context, where they can be used to support drug discovery. For this purpose, we extended MINERVA’s own drug and chemical search using the MINERVA plugin starter kit. We developed a plugin to provide a linkage between disease maps in MINERVA and application-specific databases of candidate therapeutics. The plugin has three main functionalities; one shows all the targets of all the compounds in the database, the second is a compound-based search to highlight targets of specific compounds, and the third can be used to find compounds that affect a certain target. As a use case, we applied the plugin to link a disease map and compound database we previously established in the context of cystic fibrosis and, herein, point out possible issues and difficulties. The plugin is publicly available on GitLab; the use-case application to cystic fibrosis, connecting disease maps and the compound database CandActCFTR, is available online. Full article
(This article belongs to the Special Issue Systems Medicine and Bioinformatics)
Show Figures

Figure 1

Article
A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications
J. Pers. Med. 2021, 11(8), 782; https://doi.org/10.3390/jpm11080782 - 11 Aug 2021
Viewed by 1036
Abstract
Survival analysis of the Cancer Genome Atlas (TCGA) dataset is a well-known method for discovering gene expression-based prognostic biomarkers of head and neck squamous cell carcinoma (HNSCC). A cutoff point is usually used in survival analysis for patient dichotomization when using continuous gene [...] Read more.
Survival analysis of the Cancer Genome Atlas (TCGA) dataset is a well-known method for discovering gene expression-based prognostic biomarkers of head and neck squamous cell carcinoma (HNSCC). A cutoff point is usually used in survival analysis for patient dichotomization when using continuous gene expression values. There is some optimization software for cutoff determination. However, the software’s predetermined cutoffs are usually set at the medians or quantiles of gene expression values. There are also few clinicopathological features available in pre-processed datasets. We applied an in-house workflow, including data retrieving and pre-processing, feature selection, sliding-window cutoff selection, Kaplan–Meier survival analysis, and Cox proportional hazard modeling for biomarker discovery. In our approach for the TCGA HNSCC cohort, we scanned human protein-coding genes to find optimal cutoff values. After adjustments with confounders, clinical tumor stage and surgical margin involvement were found to be independent risk factors for prognosis. According to the results tables that show hazard ratios with Bonferroni-adjusted p values under the optimal cutoff, three biomarker candidates, CAMK2N1, CALML5, and FCGBP, are significantly associated with overall survival. We validated this discovery by using the another independent HNSCC dataset (GSE65858). Thus, we suggest that transcriptomic analysis could help with biomarker discovery. Moreover, the robustness of the biomarkers we identified should be ensured through several additional tests with independent datasets. Full article
(This article belongs to the Special Issue Systems Medicine and Bioinformatics)
Show Figures

Graphical abstract

Article
Identification of Therapeutic Targets for the Selective Killing of HBV-Positive Hepatocytes
J. Pers. Med. 2021, 11(7), 649; https://doi.org/10.3390/jpm11070649 - 10 Jul 2021
Viewed by 670
Abstract
The hepatitis B virus (HBV) infection is a major risk factor for cirrhosis and hepatocellular carcinoma. Most infected individuals become lifelong carriers of HBV as the drugs currently used to treat the patients can only control the disease, thereby achieving functional cure (loss [...] Read more.
The hepatitis B virus (HBV) infection is a major risk factor for cirrhosis and hepatocellular carcinoma. Most infected individuals become lifelong carriers of HBV as the drugs currently used to treat the patients can only control the disease, thereby achieving functional cure (loss of the hepatitis B surface antigen) but not complete cure (elimination of infected hepatocytes). Therefore, we aimed to identify the target genes for the selective killing of HBV-positive hepatocytes to develop a novel therapy for the treatment of HBV infection. Our strategy was to recognize the conditionally essential genes that are essential for the survival of HBV-positive hepatocytes, but non-essential for the HBV-negative hepatocytes. Using microarray gene expression data curated from the Gene Expression Omnibus database and the known essential genes from the Online GEne Essentiality database, we used two approaches, comprising the random walk with restart algorithm and the support vector machine approach, to determine the potential targets for the selective killing of HBV-positive hepatocytes. The final candidate genes list obtained using these two approaches consisted of 36 target genes, which may be conditionally essential for the cell survival of HBV-positive hepatocytes; however, this requires further experimental validation. Therefore, the genes identified in this study can be used as potential drug targets to develop novel therapeutic strategies for the treatment of HBV, and may ultimately help in achieving the elusive goal of a complete cure for hepatitis B. Full article
(This article belongs to the Special Issue Systems Medicine and Bioinformatics)
Show Figures

Figure 1

Article
Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models
J. Pers. Med. 2021, 11(6), 496; https://doi.org/10.3390/jpm11060496 - 01 Jun 2021
Cited by 5 | Viewed by 1161
Abstract
Metabolic heterogeneity is a hallmark of cancer and can distinguish a normal phenotype from a cancer phenotype. In the systems biology domain, context-specific models facilitate extracting physiologically relevant information from high-quality data. Here, to utilize the heterogeneity of metabolic patterns to discover biomarkers [...] Read more.
Metabolic heterogeneity is a hallmark of cancer and can distinguish a normal phenotype from a cancer phenotype. In the systems biology domain, context-specific models facilitate extracting physiologically relevant information from high-quality data. Here, to utilize the heterogeneity of metabolic patterns to discover biomarkers of all cancers, we benchmarked thousands of context-specific models using well-established algorithms for the integration of omics data into the generic human metabolic model Recon3D. By analyzing the active reactions capable of carrying flux and their magnitude through flux balance analysis, we proved that the metabolic pattern of each cancer is unique and could act as a cancer metabolic fingerprint. Subsequently, we searched for proper feature selection methods to cluster the flux states characterizing each cancer. We employed PCA-based dimensionality reduction and a random forest learning algorithm to reveal reactions containing the most relevant information in order to effectively identify the most influential fluxes. Conclusively, we discovered different pathways that are probably the main sources for metabolic heterogeneity in cancers. We designed the GEMbench website to interactively present the data, methods, and analysis results. Full article
(This article belongs to the Special Issue Systems Medicine and Bioinformatics)
Show Figures

Figure 1

Article
Differences among COVID-19, Bronchopneumonia and Atypical Pneumonia in Chest High Resolution Computed Tomography Assessed by Artificial Intelligence Technology
J. Pers. Med. 2021, 11(5), 391; https://doi.org/10.3390/jpm11050391 - 10 May 2021
Cited by 2 | Viewed by 818
Abstract
The aim of this study was to compare the results of automatic assessment of high resolution computed tomography (HRCT) by artificial intelligence (AI) in 150 patients from three subgroups: pneumonia in the course of COVID-19, bronchopneumonia and atypical pneumonia. The volume percentage of [...] Read more.
The aim of this study was to compare the results of automatic assessment of high resolution computed tomography (HRCT) by artificial intelligence (AI) in 150 patients from three subgroups: pneumonia in the course of COVID-19, bronchopneumonia and atypical pneumonia. The volume percentage of inflammation and the volume percentage of “ground glass” were significantly higher in the atypical (respectively, 11.04%, 8.61%) and the COVID-19 (12.41%, 10.41%) subgroups compared to the bronchopneumonia (5.12%, 3.42%) subgroup. The volume percentage of consolidation was significantly higher in the COVID-19 (2.95%) subgroup compared to the atypical (1.26%) subgroup. The percentage of “ground glass” in the volume of inflammation was significantly higher in the atypical (89.85%) subgroup compared to the COVID-19 (79.06%) subgroup, which in turn was significantly higher compared to the bronchopneumonia (68.26%) subgroup. HRCT chest images, analyzed automatically by artificial intelligence software, taking into account the structure including “ground glass” and consolidation, significantly differ in three subgroups: COVID-19 pneumonia, bronchopneumonia and atypical pneumonia. However, the partial overlap, particularly between COVID-19 pneumonia and atypical pneumonia, may limit the usefulness of automatic analysis in differentiating the etiology. In our future research, we plan to use artificial intelligence for objective assessment of the dynamics of pulmonary lesions during COVID-19 pneumonia. Full article
(This article belongs to the Special Issue Systems Medicine and Bioinformatics)
Show Figures

Figure 1

Article
Predictive Values of Preoperative Characteristics for 30-Day Mortality in Traumatic Hip Fracture Patients
J. Pers. Med. 2021, 11(5), 353; https://doi.org/10.3390/jpm11050353 - 28 Apr 2021
Cited by 2 | Viewed by 780
Abstract
Hip fracture patients have a high risk of mortality after surgery, with 30-day postoperative rates as high as 10%. This study aimed to explore the predictive ability of preoperative characteristics in traumatic hip fracture patients as they relate to 30-day postoperative mortality using [...] Read more.
Hip fracture patients have a high risk of mortality after surgery, with 30-day postoperative rates as high as 10%. This study aimed to explore the predictive ability of preoperative characteristics in traumatic hip fracture patients as they relate to 30-day postoperative mortality using readily available variables in clinical practice. All adult patients who underwent primary emergency hip fracture surgery in Sweden between 2008 and 2017 were included in the analysis. Associations between the possible predictors and 30-day mortality was performed using a multivariate logistic regression (LR) model; the bidirectional stepwise method was used for variable selection. An LR model and convolutional neural network (CNN) were then fitted for prediction. The relative importance of individual predictors was evaluated using the permutation importance and Gini importance. A total of 134,915 traumatic hip fracture patients were included in the study. The CNN and LR models displayed an acceptable predictive ability for predicting 30-day postoperative mortality using a test dataset, displaying an area under the ROC curve (AUC) of as high as 0.76. The variables with the highest importance in prediction were age, sex, hypertension, dementia, American Society of Anesthesiologists (ASA) classification, and the Revised Cardiac Risk Index (RCRI). Both the CNN and LR models achieved an acceptable performance in identifying patients at risk of mortality 30 days after hip fracture surgery. The most important variables for prediction, based on the variables used in the current study are age, hypertension, dementia, sex, ASA classification, and RCRI. Full article
(This article belongs to the Special Issue Systems Medicine and Bioinformatics)
Show Figures

Figure 1

Article
Integrating Patient-Specific Information into Logic Models of Complex Diseases: Application to Acute Myeloid Leukemia
J. Pers. Med. 2021, 11(2), 117; https://doi.org/10.3390/jpm11020117 - 10 Feb 2021
Cited by 3 | Viewed by 973
Abstract
High throughput technologies such as deep sequencing and proteomics are increasingly becoming mainstream in clinical practice and support diagnosis and patient stratification. Developing computational models that recapitulate cell physiology and its perturbations in disease is a required step to help with the interpretation [...] Read more.
High throughput technologies such as deep sequencing and proteomics are increasingly becoming mainstream in clinical practice and support diagnosis and patient stratification. Developing computational models that recapitulate cell physiology and its perturbations in disease is a required step to help with the interpretation of results of high content experiments and to devise personalized treatments. As complete cell-models are difficult to achieve, given limited experimental information and insurmountable computational problems, approximate approaches should be considered. We present here a general approach to modeling complex diseases by embedding patient-specific genomics data into actionable logic models that take into account prior knowledge. We apply the strategy to acute myeloid leukemia (AML) and assemble a network of logical relationships linking most of the genes that are found frequently mutated in AML patients. We derive Boolean models from this network and we show that by priming the model with genomic data we can infer relevant patient-specific clinical features. Here we propose that the integration of literature-derived causal networks with patient-specific data should be explored to help bedside decisions. Full article
(This article belongs to the Special Issue Systems Medicine and Bioinformatics)
Show Figures

Graphical abstract

Review

Jump to: Research

Review
Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation
J. Pers. Med. 2022, 12(2), 166; https://doi.org/10.3390/jpm12020166 (registering DOI) - 26 Jan 2022
Viewed by 186
Abstract
The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes [...] Read more.
The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas. Full article
(This article belongs to the Special Issue Systems Medicine and Bioinformatics)
Show Figures

Figure 1

Review
Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence
J. Pers. Med. 2021, 11(4), 280; https://doi.org/10.3390/jpm11040280 - 07 Apr 2021
Cited by 1 | Viewed by 1256
Abstract
In the big data era, artificial intelligence techniques have been applied to tackle traditional issues in the study of neurodegenerative diseases. Despite the progress made in understanding the complex (epi)genetics signatures underlying neurodegenerative disorders, performing early diagnosis and developing drug repurposing strategies remain [...] Read more.
In the big data era, artificial intelligence techniques have been applied to tackle traditional issues in the study of neurodegenerative diseases. Despite the progress made in understanding the complex (epi)genetics signatures underlying neurodegenerative disorders, performing early diagnosis and developing drug repurposing strategies remain serious challenges for such conditions. In this context, the integration of multi-omics, neuroimaging, and electronic health records data can be exploited using deep learning methods to provide the most accurate representation of patients possible. Deep learning allows researchers to find multi-modal biomarkers to develop more effective and personalized treatments, early diagnosis tools, as well as useful information for drug discovering and repurposing in neurodegenerative pathologies. In this review, we will describe how relevant studies have been able to demonstrate the potential of deep learning to enhance the knowledge of neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases through the integration of all sources of biomedical data. Full article
(This article belongs to the Special Issue Systems Medicine and Bioinformatics)
Show Figures

Figure 1

Back to TopTop