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

Deadline for manuscript submissions: 25 November 2023 | Viewed by 8949

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Prof. Dr. Dorota Bielińska-Wąż

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Department of Radiological Informatics and Statistics, Medical University of Gdańsk, 80-210 Gdańsk, Poland
Interests: bioinformatics; graphical representations of biological sequences; biophysics; mathematical modeling in biomedical and social sciences; health and biomedical informatics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Piotr Wąż

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Faculty of Health Sciences, Medical University of Gdańsk, 80-210 Gdańsk, Poland
Interests: bioinformatics; graphical representations of biological sequences; computational statistics; mathematical modeling in medicine, physics, astronomy; computational pharmacology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are launching a Special Issue entitled Bioinformatics and Medicine and are looking to publish original research, reviews, and combined original–review papers. This Special Issue is mainly devoted to a branch of bioinformatics known as “alignment-free bioinformatics methods”. This branch of bioinformatics, developed over the last two decades, is one of the most promising directions in the development of this area of science. In particular, articles on graphical representation methods, aimed at both the graphical and numerical analysis of the similarity/dissimilarity of biological sequences (DNA, RNA, and protein), are welcome. The submitted articles may contain descriptions of new algorithms. Papers dealing with different aspects of the graphical or numerical comparisons of the considered objects or focused on discussing a variety of applications of the methods already published in the biomedical sciences are also welcome. We will also accept papers related to standard bioinformatics methods and medical informatics.

Prof. Dr. Dorota Bielińska-Wąż
Prof. Dr. Piotr Wąż
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 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
alignment-free bioinformatics methods
graphical bioinformatics
biomedical informatics
data analysis
mathematical modeling

Published Papers (8 papers)

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Research

Open AccessArticle

Classification Maps: A New Mathematical Tool Supporting the Diagnosis of Age-Related Macular Degeneration

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J. Pers. Med. 2023, 13(7), 1074; https://doi.org/10.3390/jpm13071074 - 29 Jun 2023

Viewed by 450

Abstract

Objective: A new diagnostic graphical tool—classification maps—supporting the detection of Age-Related Macular Degeneration (AMD) has been constructed. Methods: The classification maps are constructed using the ordinal regression model. In the ordinal regression model, the ordinal variable (the dependent variable) is the degree of the advancement of AMD. The other variables, such as CRT (Central Retinal Thickness), GCC (Ganglion Cell Complex), MPOD (Macular Pigment Optical Density), ETDRS (Early Treatment Diabetic Retinopathy Study), Snellen and Age have also been used in the analysis and are represented on the axes of the maps. Results: Here, 132 eyes were examined and classified to the AMD advancement level according to the four-point Age-Related Eye Disease Scale (AREDS): AREDS 1, AREDS 2, AREDS 3 and AREDS 4. These data were used for the creation of two-dimensional classification maps for each of the four stages of AMD. Conclusions: The maps allow us to perform the classification of the patient’s eyes to particular stages of AMD. The pairs of the variables represented on the axes of the maps can be treated as diagnostic identifiers necessary for the classification to particular stages of AMD. Full article

(This article belongs to the Special Issue Bioinformatics and Medicine)

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Open AccessArticle

Integrated Bioinformatics Investigation of Novel Biomarkers of Uterine Leiomyosarcoma Diagnosis and Outcome

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J. Pers. Med. 2023, 13(6), 985; https://doi.org/10.3390/jpm13060985 - 13 Jun 2023

Viewed by 961

Abstract

Uterine leiomyosarcomas (uLMS) have a poor prognosis and a high percentage of recurrent disease. Bioinformatics has become an integral element in rare cancer studies by overcoming the inability to collect a large enough study population. This study aimed to investigate and highlight crucial genes, pathways, miRNAs, and transcriptional factors (TF) on uLMS samples from five Gene Expression Omnibus datasets and The Cancer Genome Atlas Sarcoma study. Forty-one common differentially expressed genes (DEGs) were enriched and annotated by the DAVID software. With protein–protein interaction (PPI) network analysis, we selected ten hub genes that were validated with the TNMplotter web tool. We used the USCS Xena browser for survival analysis. We also predicted TF-gene and miRNA-gene regulatory networks along with potential drug molecules. TYMS and TK1 correlated with overall survival in uLMS patients. Finally, our results propose further validation of hub genes (TYMS and TK1), miR-26b-5p, and Sp1 as biomarkers of pathogenesis, prognosis, and differentiation of uLMS. Regarding the aggressive behavior and poor prognosis of uLMS, with the lack of standard therapeutic regimens, in our opinion, the results of our study provide enough evidence for further investigation of the molecular basis of uLMS occurrence and its implication in the diagnosis and therapy of this rare gynecological malignancy. Full article

(This article belongs to the Special Issue Bioinformatics and Medicine)

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Open AccessArticle

Susceptibility to Colorectal Cancer Based on HSD17B4 rs721673 and rs721675 Polymorphisms and Alcohol Intake among Taiwan Biobank Participants: A Retrospective Case Control Study Using the Nationwide Claims Data

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J. Pers. Med. 2023, 13(4), 576; https://doi.org/10.3390/jpm13040576 - 24 Mar 2023

Viewed by 764

Abstract

Colorectal cancer (CRC) is a major public health issue, and there are limited studies on the association between 17β-hydroxysteroid dehydrogenase type 4 (HSD17B4) polymorphism and CRC. We used two national databases from Taiwan to examine whether HSD17B4 rs721673, rs721675, and alcohol intake were independently and interactively correlated with CRC development. We linked the Taiwan Biobank (TWB) participants’ health and lifestyle information and genotypic data from 2012 to 2018 to the National Health Insurance Database (NHIRD) to confirm their medical records. We performed a genome-wide association study (GWAS) using data from 145 new incident CRC cases and matched 1316 healthy, non-CRC individuals. We calculated the odds ratios (OR) and 95% confidence intervals (CI) for CRC based on multiple logistic regression analyses. HSD17B4 rs721673 and rs721675 on chromosome 5 were significantly and positively correlated with CRC (rs721673 A > G, aOR = 2.62, p = 2.90 × 10⁻⁸; rs721675 A > T, aOR = 2.61, p = 1.01 × 10⁻⁶). Within the high-risk genotypes, significantly higher ORs were observed among the alcohol intake group. Our results demonstrated that the rs721673 and rs721675 risk genotypes of HSD17B4 might increase the risk of CRC development in Taiwanese adults, especially those with alcohol consumption habits. Full article

(This article belongs to the Special Issue Bioinformatics and Medicine)

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Open AccessArticle

High-Throughput Sequencing Reveals That Rotundine Inhibits Colorectal Cancer by Regulating Prognosis-Related Genes

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J. Pers. Med. 2023, 13(3), 550; https://doi.org/10.3390/jpm13030550 - 20 Mar 2023

Viewed by 931

Abstract

Background: Rotundine is an herbal medicine with anti-cancer effects. However, little is known about the anti-cancer effect of rotundine on colorectal cancer. Therefore, our study aimed to investigate the specific molecular mechanism of rotundine inhibition of colorectal cancer. Methods: MTT and cell scratch assay were performed to investigate the effects of rotundine on the viability, migration, and invasion ability of SW480 cells. Changes in cell apoptosis were analyzed by flow cytometry. DEGs were detected by high-throughput sequencing after the action of rotundine on SW480 cells, and the DEGs were subjected to function enrichment analysis. Bioinformatics analyses were performed to screen out prognosis-related DEGs of COAD. Followed by enrichment analysis of prognosis-related DEGs. Furthermore, prognostic models were constructed, including ROC analysis, risk curve analysis, PCA and t-SNE, Nomo analysis, and Kaplan–Meier prognostic analysis. Results: In this study, we showed that rotundine concentrations of 50 μM, 100 μM, 150 μM, and 200 μM inhibited the proliferation, migration, and invasion of SW480 cells in a time- and concentration-dependent manner. Rotundine does not induce SW480 cell apoptosis. Compared to the control group, high-throughput results showed that there were 385 DEGs in the SW480 group. And DEGs were associated with the Hippo signaling pathway. In addition, 16 of the DEGs were significantly associated with poorer prognosis in COAD, with MEF2B, CCDC187, PSD2, RGS16, PLXDC1, HELB, ASIC3, PLCH2, IGF2BP3, CLHC1, DNHD1, SACS, H1-4, ANKRD36, and ZNF117 being highly expressed in COAD and ARV1 being lowly expressed. Prognosis-related DEGs were mainly enriched in cancer-related pathways and biological functions, such as inositol phosphate metabolism, enterobactin transmembrane transporter activity, and enterobactin transport. Prognostic modeling also showed that these 16 DEGs could be used as predictors of overall survival prognosis in COAD patients. Conclusions: Rotundine inhibits the development and progression of colorectal cancer by regulating the expression of these prognosis-related genes. Our findings could further provide new directions for the treatment of colorectal cancer. Full article

(This article belongs to the Special Issue Bioinformatics and Medicine)

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Open AccessArticle

Identification of Five Tumor Antigens for Development and Two Immune Subtypes for Personalized Medicine of mRNA Vaccines in Papillary Renal Cell Carcinoma

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J. Pers. Med. 2023, 13(2), 359; https://doi.org/10.3390/jpm13020359 - 18 Feb 2023

Viewed by 1105

Abstract

Increasing evidence has revealed the promise of mRNA-type cancer vaccines as a new direction for cancer immune treatment in several solid tumors, however, its application in papillary renal cell carcinoma (PRCC) remains unclear. The purpose of this study was to identify potential tumor antigens and robust immune subtypes for the development and appropriate use of anti-PRCC mRNA vaccines, respectively. Raw sequencing data and clinical information of PRCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The cBioPortal was utilized for the visualization and comparison of genetic alterations. The TIMER was used to assess the correlation between preliminary tumor antigens and the abundance of infiltrated antigen presenting cells (APCs). Immune subtypes were determined by the consensus clustering algorithm, and clinical and molecular discrepancies were further explored for a deeper understanding of immune subtypes. Five tumor antigens, including ALOX15B, HS3ST2, PIGR, ZMYND15 and LIMK1, were identified for PRCC, which were correlated with patients’ prognoses and infiltration levels of APCs. Two immune subtypes (IS1 and IS2) were disclosed with obviously distinct clinical and molecular characteristics. Compared with IS2, IS1 exhibited a significantly immune-suppressive phenotype, which largely weakened the efficacy of the mRNA vaccine. Overall, our study provides some insights for the design of anti-PRCC mRNA vaccines and, more importantly, the selection of suitable patients to be vaccinated. Full article

(This article belongs to the Special Issue Bioinformatics and Medicine)

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Open AccessArticle

Progression of Selected Parameters of the Clinical Profile of Patients with Periodontitis Using Kohonen’s Self-Organizing Maps

Agata Ossowska

Aida Kusiak

and

Dariusz Świetlik

J. Pers. Med. 2023, 13(2), 346; https://doi.org/10.3390/jpm13020346 - 16 Feb 2023

Viewed by 802

Abstract

(1) Background: Periodontitis is an inflammatory condition that affects the tissues surrounding the tooth and causes clinical attachment loss, which is the loss of periodontal attachment (CAL). Periodontitis can advance in various ways, with some patients experiencing severe periodontitis in a short period of time while others may experience mild periodontitis for the rest of their lives. In this study, we have used an alternative methodology to conventional statistics, self-organizing maps (SOM), to group the clinical profiles of patients with periodontitis. (2) Methods: To predict the periodontitis progression and to choose the best treatment plan, we can use artificial intelligence, more precisely Kohonen’s self-organizing maps (SOM). In this study, 110 patients, both genders, between the ages of 30 and 60, were included in this retrospective analysis. (3) Results: To discover the pattern of patients according to the periodontitis grade and stage, we grouped the neurons together to form three clusters: Group 1 was made up of neurons 12 and 16 that represented a percentage of slow progression of almost 75%; Group 2 was made up of neurons 3, 4, 6, 7, 11, and 14 in which the percentage of moderate progression was almost 65%; and Group 3 was made up of neurons 1, 2, 5, 8, 9, 10, 13, and 15 that represented a percentage of rapid progression of almost 60%. There were statistically significant differences in the approximate plaque index (API), and bleeding on probing (BoP) versus groups (p < 0.0001). The post-hoc tests showed that API, BoP, pocket depth (PD), and CAL values were significantly lower in Group 1 relative to Group 2 (p < 0.05) and Group 3 (p < 0.05). A detailed statistical analysis showed that the PD value was significantly lower in Group 1 relative to Group 2 (p = 0.0001). Furthermore, the PD was significantly higher in Group 3 relative to Group 2 (p = 0.0068). There was a statistically significant CAL difference between Group 1 relative to Group 2 (p = 0.0370). (4) Conclusions: Self-organizing maps, in contrast to conventional statistics, allow us to view the issue of periodontitis advancement by illuminating how the variables are organized in one or the other of the various suppositions. Full article

(This article belongs to the Special Issue Bioinformatics and Medicine)

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Open AccessArticle

Construction of Tongue Image-Based Machine Learning Model for Screening Patients with Gastric Precancerous Lesions

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J. Pers. Med. 2023, 13(2), 271; https://doi.org/10.3390/jpm13020271 - 31 Jan 2023

Cited by 3 | Viewed by 1173

Abstract

Screening patients with precancerous lesions of gastric cancer (PLGC) is important for gastric cancer prevention. The accuracy and convenience of PLGC screening could be improved with the use of machine learning methodologies to uncover and integrate valuable characteristics of noninvasive medical images related to PLGC. In this study, we therefore focused on tongue images and for the first time constructed a tongue image-based PLGC screening deep learning model (AITongue). The AITongue model uncovered potential associations between tongue image characteristics and PLGC, and integrated canonical risk factors, including age, sex, and Hp infection. Five-fold cross validation analysis on an independent cohort of 1995 patients revealed the AITongue model could screen PLGC individuals with an AUC of 0.75, 10.3% higher than that of the model with only including canonical risk factors. Of note, we investigated the value of the AITongue model in predicting PLGC risk by establishing a prospective PLGC follow-up cohort, reaching an AUC of 0.71. In addition, we developed a smartphone-based app screening system to enhance the application convenience of the AITongue model in the natural population from high-risk areas of gastric cancer in China. Collectively, our study has demonstrated the value of tongue image characteristics in PLGC screening and risk prediction. Full article

(This article belongs to the Special Issue Bioinformatics and Medicine)

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Open AccessArticle

Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning

and

Sujatha Krishnamoorthy

J. Pers. Med. 2022, 12(9), 1454; https://doi.org/10.3390/jpm12091454 - 05 Sep 2022

Cited by 6 | Viewed by 1395

Abstract

Diabetic retinopathy (DR) is a drastic disease. DR embarks on vision impairment when it is left undetected. In this article, learning-based techniques are presented for the segmentation and classification of DR lesions. The pre-trained Xception model is utilized for deep feature extraction in the segmentation phase. The extracted features are fed to Deeplabv3 for semantic segmentation. For the training of the segmentation model, an experiment is performed for the selection of the optimal hyperparameters that provided effective segmentation results in the testing phase. The multi-classification model is developed for feature extraction using the fully connected (FC) MatMul layer of efficient-net-b0 and pool-10 of the squeeze-net. The extracted features from both models are fused serially, having the dimension of N × 2020, amidst the best N × 1032 features chosen by applying the marine predictor algorithm (MPA). The multi-classification of the DR lesions into grades 0, 1, 2, and 3 is performed using neural network and KNN classifiers. The proposed method performance is validated on open access datasets such as DIARETDB1, e-ophtha-EX, IDRiD, and Messidor. The obtained results are better compared to those of the latest published works. Full article

(This article belongs to the Special Issue Bioinformatics and Medicine)

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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Nonlinear Mendelian Randomization: quadratic causality method (QC Method) and two-stage method
Authors: Xinpei Wang, Jinzhu Jia# and Tao Huang#
Affiliation: Peking University
Abstract: Background: Using Mendelian randomization (MR) approach to explore the causal relationship between exposure and outcome can effectively avoid reverse causality and confounding bias. However, most of the current MR methods are only suitable for cases where the effect of exposure on the outcome is linear. Methods: In this paper, we proposed QC method and two-stage method to identify and estimate the quadratic causality of exposure on the outcome. We simulated a series of scenarios and compared the performance of these two methods with fractional polynomial method and linearity-based ratio method. We also applied these methods to the real data of UK Biobank (UKB) to investigate the effect of body mass index (BMI) on nine metabolic phenotypes. Results: We proposed two approaches to nonlinear MR, either by fitting linear model of exposure-instrument and quadratic model of outcome-instrument (QC method), or by fitting linear model of exposure-instrument and quadratic model of outcome-predicted exposure (two-stage method), we could identify and estimate the quadratic causality of exposure on the outcome. A series of simulation results showed that our QC method and two-stage method had power and low type I error. In real data applications, QC method and two-stage method found that BMI had a J-shaped effect on basal metabolic rate and had an invert J-shaped effect on the level of high-density lipoprotein cholesterol in UKB participants. Conclusion: We developed QC method and two-stage method for MR studies, which can identify and estimate the quadratic effect of exposure on the outcome. The R package for QC method is publicly available at https://github.com/XinpeiW/QCMR.

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