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Feature Papers in Computational Biology and Medicine
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Feature Papers in Computational Biology and Medicine

A special issue of BioMedInformatics (ISSN 2673-7426). This special issue belongs to the section "Computational Biology and Medicine".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 23751

Special Issue Editor

Dr. Hans Binder

E-Mail Website
Guest Editor
Interdisciplinary Centre for Bioinformatics, Leipzig University, Haertelstr. 16–18, D-04107 Leipzig, Germany
Interests: genome medicine; computational biology; genomic regulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multiomics diagnostics for cancer

Multi-omics high-throughput technologies are producing a steeply increasing number of data sets, which are not restricted to only one but consist of multiple omics modalities extracted from the same samples (e.g., in patient-matched tumor specimens). These data offer tremendous opportunities for enhancing our molecular understanding of biological systems, particularly regarding different but mutually linked layers of genomic regulation, usually subsumed as the basic “omes”—genome, epigenome, transcriptome, proteome and metabolome. The joint, integrative analysis of these omics modalities and the development of appropriate computational methods is essential to obtain a comprehensive overview of the otherwise fragmented information hidden in this data. The molecular pathomechanisms of cancer are often driven by a complex interplay between the omes, including genetic defects, epigenetic reprogramming, and perturbed transcription factor networks. Practical objectives of computational methods are the description of cancer heterogeneity in terms of subtypes and the extraction of prognostic markers from the different “omes”, for example, by asking whether single omics modalities or combinations of them are better suited and if so, what modality is “the best” or how to combine them optimally. The Special Issue aims to address a wide scope of areas related to cancer diagnostics, ranging from computational methods integrating different omes (e.g., for subtyping cancer heterogeneity, their prognosis and personalization), to applications to different cancer types and omics realms (genetics, transcriptomics, epigenetics, metabolomics, proteomics) and their associations with clinical data.

Understanding Pathomechanisms in the single-cell omics era

Single-cell sequencing technologies are revolutionizing our view of biological systems across several research fields by illuminating the complex cell type and state landscape. They make it possible to understand cancer as an evolving cellular disease, provide new details of aberrant cellular functions of neurodegenerative diseases, decipher the diversity of immune cells and their interactions in the tumor microenvironment, pathomechanisms of autoimmunity and inflammatory diseases with single-immune-cell resolution, and provide new insights into a series of other complex and/or rare diseases. This Special Issue will collect methods and applications of single-cell omics analytics, primarily transcriptomics but also chromatin accessibility (ATAC) and other omics applications, and, as an option, their combination with bulk omics data. The central objective is to learn about the particular benefit of the cellular view for the understanding of pathomechanisms and potential treatment options.

Dr. Hans Binder
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. BioMedInformatics is an international peer-reviewed open access quarterly 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 1000 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

  • multiomics diagnostics for cancer
  • understanding patho mechanisms in the era of single cell transcriptomics
  • systems biology of drug repurposing

Published Papers (15 papers)

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25 pages, 5755 KiB  
Article
Deep Segmentation Techniques for Breast Cancer Diagnosis
by Storm Schutte and Jia Uddin
BioMedInformatics 2024, 4(2), 921-945; https://doi.org/10.3390/biomedinformatics4020052 - 1 Apr 2024
Viewed by 829
Abstract
Background: This research goes into in deep learning technologies within the realm of medical imaging, with a specific focus on the detection of anomalies in medical pathology, emphasizing breast cancer. It underscores the critical importance of segmentation techniques in identifying diseases and addresses [...] Read more.
Background: This research goes into in deep learning technologies within the realm of medical imaging, with a specific focus on the detection of anomalies in medical pathology, emphasizing breast cancer. It underscores the critical importance of segmentation techniques in identifying diseases and addresses the challenges of scarce labelled data in Whole Slide Images. Additionally, the paper provides a review, cataloguing 61 deep learning architectures identified during the study. Objectives: The aim of this study is to present and assess a novel quantitative approach utilizing specific deep learning architectures, namely the Feature Pyramid Net-work and the Linknet model, both of which integrate a ResNet34 layer encoder to enhance performance. The paper also seeks to examine the efficiency of a semi-supervised training regimen using a dual model architecture, consisting of ‘Teacher’ and ‘Student’ models, in addressing the issue of limited labelled datasets. Methods: Employing a semi-supervised training methodology, this research enables the ‘Student’ model to learn from the ‘Teacher’ model’s outputs. The study methodically evaluates the models’ stability, accuracy, and segmentation capabilities, employing metrics such as the Dice Coefficient and the Jaccard Index for comprehensive assessment. Results: The investigation reveals that the Linknet model exhibits good performance, achieving an accuracy rate of 94% in the detection of breast cancer tissues utilizing a 21-seed parameter for the initialization of model weights. It further excels in generating annotations for the ‘Student’ model, which then achieves a 91% accuracy with minimal computational demands. Conversely, the Feature Pyramid Network model demonstrates a slightly lower accuracy of 93% in the Teacher model but exhibits improved and more consistent results in the ‘Student’ model, reaching 95% accuracy with a 42-seed parameter. Conclusions: This study underscores the efficacy and potential of the Feature Pyra-mid Network and Linknet models in the domain of medical image analysis, particularly in the detection of breast cancer, and suggests their broader applicability in various medical segmentation tasks related to other pathology disorders. Furthermore, the research enhances the understanding of the pivotal role that deep learning technologies play in advancing diagnostic methods within the field of medical imaging. Full article
(This article belongs to the Special Issue Feature Papers in Computational Biology and Medicine)
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21 pages, 4992 KiB  
Article
Forecasting Survival Rates in Metastatic Colorectal Cancer Patients Undergoing Bevacizumab-Based Chemotherapy: A Machine Learning Approach
by Sergio Sánchez-Herrero, Abtin Tondar, Elena Perez-Bernabeu, Laura Calvet and Angel A. Juan
BioMedInformatics 2024, 4(1), 733-753; https://doi.org/10.3390/biomedinformatics4010041 - 2 Mar 2024
Viewed by 573
Abstract
Background: Antibiotics can play a pivotal role in the treatment of colorectal cancer (CRC) at various stages of the disease, both directly and indirectly. Identifying novel patterns of antibiotic effects or responses in CRC within extensive medical data poses a significant challenge that [...] Read more.
Background: Antibiotics can play a pivotal role in the treatment of colorectal cancer (CRC) at various stages of the disease, both directly and indirectly. Identifying novel patterns of antibiotic effects or responses in CRC within extensive medical data poses a significant challenge that can be addressed through algorithmic approaches. Machine Learning (ML) emerges as a promising solution for predicting clinical outcomes using clinical and heterogeneous cancer data. In the pursuit of our objective, we employed ML techniques for predicting CRC mortality and antibiotic influence. Methods: We utilized a dataset to examine the accuracy of death prediction in metastatic colorectal cancer. In addition, we analyzed the association between antibiotic exposure and mortality in metastatic colorectal cancer. The dataset comprised 147 patients, nineteen independent variables, and one dependent variable. Our analysis involved testing different classification-supervised ML, including an oversampling pool for classification models, Logistic Regression, Decision Trees, Naive Bayes, Support Vector Machine, Random Forest, XGBboost Classifier, a consensus of all models, and a consensus of top models (meta models). Results: The consensus of the top models’ classifier exhibited the highest accuracy among the algorithms tested (93%). This model met the standards for good accuracy, surpassing the 90% threshold considered useful in ML applications. Consistent with the accuracy results, other metrics are also good, including precision (0.96), recall (0.93), F-Beta (0.94), and AUC (0.93). Hazard ratio analysis suggests that there is no discernible difference between patients who received antibiotics and those who did not. Conclusions: Our modelling approach provides an alternative for analyzing and predicting the relationship between antibiotics and mortality in metastatic colorectal cancer patients treated with bevacizumab, complementing classic statistical methods. This methodology lays the groundwork for future use of datasets in cancer treatment research and highlights the advantages of meta models. Full article
(This article belongs to the Special Issue Feature Papers in Computational Biology and Medicine)
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12 pages, 1024 KiB  
Article
The Development and Usability Assessment of an Augmented Reality Decision Support System to Address Burn Patient Management
by Sena Veazey, Nicole Caldwell, David Luellen, Angela Samosorn, Allison McGlasson, Patricia Colston, Craig Fenrich, Jose Salinas, Jared Mike, Jacob Rivera and Maria Serio-Melvin
BioMedInformatics 2024, 4(1), 709-720; https://doi.org/10.3390/biomedinformatics4010039 - 1 Mar 2024
Viewed by 1167
Abstract
Critical care injuries, such as burn trauma, require specialized skillsets and knowledge. A clinical decision support system to aid clinicians in providing burn patient management can increase proficiency and provide knowledge content for specific interventions. In austere environments, decision support tools can be [...] Read more.
Critical care injuries, such as burn trauma, require specialized skillsets and knowledge. A clinical decision support system to aid clinicians in providing burn patient management can increase proficiency and provide knowledge content for specific interventions. In austere environments, decision support tools can be used to aid in decision making and task guidance when skilled personnel or resources are limited. Therefore, we developed a novel software system that utilizes augmented reality (AR) capabilities to provide enhanced step-by-step instructions based on best practices for managing burn patients. To better understand how new technologies, such as AR, can be used for burn care management, we developed a burn care application for use on a heads-up display. We developed four sub-set applications for documenting and conducting burn wound mapping, fluid resuscitation, medication calculations, and an escharotomy. After development, we conducted a usability study utilizing the System Usability Scale, pre- and post- simulation surveys, and after-action reviews to evaluate the AR-based software application in a simulation scenario. Results of the study indicate that the decision support tool has generalized usability and subjects were able to use the software as intended. Here we present the first use case of a comprehensive burn management system utilizing augmented reality capabilities to deliver care. Full article
(This article belongs to the Special Issue Feature Papers in Computational Biology and Medicine)
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23 pages, 4265 KiB  
Article
Machine Learning Approach to Identify Case-Control Studies on ApoE Gene Mutations Linked to Alzheimer’s Disease in Italy
by Giorgia Francesca Saraceno, Diana Marisol Abrego-Guandique, Roberto Cannataro, Maria Cristina Caroleo and Erika Cione
BioMedInformatics 2024, 4(1), 600-622; https://doi.org/10.3390/biomedinformatics4010033 - 23 Feb 2024
Viewed by 1372
Abstract
Background: An application of artificial intelligence is machine learning, which allows computer programs to learn and create data. Methods: In this work, we aimed to evaluate the performance of the MySLR machine learning platform, which implements the Latent Dirichlet Allocation (LDA) algorithm in [...] Read more.
Background: An application of artificial intelligence is machine learning, which allows computer programs to learn and create data. Methods: In this work, we aimed to evaluate the performance of the MySLR machine learning platform, which implements the Latent Dirichlet Allocation (LDA) algorithm in the identification and screening of papers present in the literature that focus on mutations of the apolipoprotein E (ApoE) gene in Italian Alzheimer’s Disease patients. Results: MySLR excludes duplicates and creates topics. MySLR was applied to analyze a set of 164 scientific publications. After duplicate removal, the results allowed us to identify 92 papers divided into two relevant topics characterizing the investigated research area. Topic 1 contains 70 papers, and topic 2 contains the remaining 22. Despite the current limitations, the available evidence suggests that articles containing studies on Italian Alzheimer’s Disease (AD) patients were 65.22% (n = 60). Furthermore, the presence of papers about mutations, including single nucleotide polymorphisms (SNPs) ApoE gene, the primary genetic risk factor of AD, for the Italian population was 5.4% (n = 5). Conclusion: The results show that the machine learning platform helped to identify case-control studies on ApoE gene mutations, including SNPs, but not only conducted in Italy. Full article
(This article belongs to the Special Issue Feature Papers in Computational Biology and Medicine)
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21 pages, 3029 KiB  
Article
Depleted-MLH1 Expression Predicts Prognosis and Immunotherapeutic Efficacy in Uterine Corpus Endometrial Cancer: An In Silico Approach
by Tesfaye Wolde, Jing Huang, Peng Huang, Vijay Pandey and Peiwu Qin
BioMedInformatics 2024, 4(1), 326-346; https://doi.org/10.3390/biomedinformatics4010019 - 1 Feb 2024
Viewed by 953
Abstract
Uterine corpus endometrial carcinoma (UCEC) poses significant clinical challenges due to its high incidence and poor prognosis, exacerbated by the lack of effective screening methods. The standard treatment for UCEC typically involves surgical intervention, with radiation and chemotherapy as potential adjuvant therapies. In [...] Read more.
Uterine corpus endometrial carcinoma (UCEC) poses significant clinical challenges due to its high incidence and poor prognosis, exacerbated by the lack of effective screening methods. The standard treatment for UCEC typically involves surgical intervention, with radiation and chemotherapy as potential adjuvant therapies. In recent years, immunotherapy has emerged as a promising avenue for the advanced treatment of UCEC. This study employs a multi-omics approach, analyzing RNA-sequencing data and clinical information from The Cancer Genome Atlas (TCGA), Gene Expression Profiling Interactive Analysis (GEPIA), and GeneMANIA databases to investigate the prognostic value of MutL Homolog 1 (MLH1) gene expression in UCEC. The dysregulation of MLH1 in UCEC is linked to adverse prognostic outcomes and suppressed immune cell infiltration. Gene Set Enrichment Analysis (GSEA) data reveal MLH1’s involvement in immune-related processes, while its expression correlates with tumor mutational burden (TMB) and microsatellite instability (MSI). Lower MLH1 expression is associated with poorer prognosis, reduced responsiveness to Programmed cell death protein 1 (PD-1)/Programmed death-ligand 1 (PD-L1) inhibitors, and heightened sensitivity to anti-cancer agents. This comprehensive analysis establishes MLH1 as a potential biomarker for predicting prognosis, immunotherapy response, and drug sensitivity in UCEC, offering crucial insights for the clinical management of patients. Full article
(This article belongs to the Special Issue Feature Papers in Computational Biology and Medicine)
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17 pages, 533 KiB  
Article
Factors Associated with Unplanned Hospital Readmission after Discharge: A Descriptive and Predictive Study Using Electronic Health Record Data
by Safaa Dafrallah and Moulay A. Akhloufi
BioMedInformatics 2024, 4(1), 219-235; https://doi.org/10.3390/biomedinformatics4010014 - 12 Jan 2024
Viewed by 570
Abstract
Hospital readmission involves the unplanned emergency admission of patients within 30 days from discharge after the previous admission. According to the Canadian Health Institute (CIHI), 1 in 11 patients were readmitted within 30 days of leaving the hospital in 2021. In the USA, [...] Read more.
Hospital readmission involves the unplanned emergency admission of patients within 30 days from discharge after the previous admission. According to the Canadian Health Institute (CIHI), 1 in 11 patients were readmitted within 30 days of leaving the hospital in 2021. In the USA, nearly 20% of Medicare patients were readmitted after discharge, where the average cost of readmission was approximately USD 15,000, as reported by the Agency for Healthcare Research and Quality (AHQR) in 2018. To tackle this issue, we first conducted a descriptive analysis study to understand the risk factors associated with hospital readmission, and then we applied machine learning approaches to predict hospital readmission by using patients’ demographic and clinical data extracted from the Electronic Health Record of the MIMIC-III clinical database. The results showed that the number of previous admissions during the last 12 months, hyperosmolar imbalance and comorbidity index were the top three significant factors for hospital readmission. The predictive model achieved a performance of 95.6% AP and an AUC = 97.3% using the Gradient Boosting algorithm trained on all features. Full article
(This article belongs to the Special Issue Feature Papers in Computational Biology and Medicine)
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16 pages, 1820 KiB  
Article
OutSplice: A Novel Tool for the Identification of Tumor-Specific Alternative Splicing Events
by Joseph Bendik, Sandhya Kalavacherla, Nicholas Webster, Joseph Califano, Elana J. Fertig, Michael F. Ochs, Hannah Carter and Theresa Guo
BioMedInformatics 2023, 3(4), 853-868; https://doi.org/10.3390/biomedinformatics3040053 - 8 Oct 2023
Viewed by 1132
Abstract
Protein variation that occurs during alternative splicing has been shown to play a major role in disease onset and oncogenesis. Due to this, we have developed OutSplice, a user-friendly algorithm to classify splicing outliers in tumor samples compared to a distribution of normal [...] Read more.
Protein variation that occurs during alternative splicing has been shown to play a major role in disease onset and oncogenesis. Due to this, we have developed OutSplice, a user-friendly algorithm to classify splicing outliers in tumor samples compared to a distribution of normal samples. Several tools have previously been developed to help uncover splicing events, each coming with varying methodologies, complexities, and features that can make it difficult for a new researcher to use or to determine which tool they should be using. Therefore, we benchmarked several algorithms to determine which may be best for a particular user’s needs and demonstrate how OutSplice differs from these methodologies. We find that despite detecting a lower number of genes with significant aberrant events, OutSplice is able to identify those that are biologically impactful. Additionally, we identify 17 genes that contain significant splicing alterations in tumor tissue that were discovered across at least 5 of the tested algorithms, making them good candidates for future studies. Overall, researchers should consider a combined use of OutSplice with other splicing software to help provide additional validation for aberrant splicing events and to narrow down biologically relevant events. Full article
(This article belongs to the Special Issue Feature Papers in Computational Biology and Medicine)
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22 pages, 2601 KiB  
Article
Towards an Affordable Means of Surgical Depth of Anesthesia Monitoring: An EMG-ECG-EEG Case Study
by Ejay Nsugbe, Stephanie Connelly and Ian Mutanga
BioMedInformatics 2023, 3(3), 769-790; https://doi.org/10.3390/biomedinformatics3030049 - 4 Sep 2023
Viewed by 1317
Abstract
The anesthetic dosing procedure is a key element of safe surgical practice, where it is paramount to ensure sufficient dosing of the anesthetic agent to the patient in order to reach the desired depth of sedation for the necessary procedure. One means of [...] Read more.
The anesthetic dosing procedure is a key element of safe surgical practice, where it is paramount to ensure sufficient dosing of the anesthetic agent to the patient in order to reach the desired depth of sedation for the necessary procedure. One means of monitoring the depth of anesthesia (DoA) involves the use of the bispectral index (BIS), which decodes electroencephalography (EEG) signals acquired from the frontal cortex in a continuous fashion. The shortcomings of this include the complexity of the decoding of EEG signals, insensitivity to certain anesthetic agents, and the costly nature of the technology, which limits its adoption in resource-constrained settings. In this paper, we investigate an alternative source of physiological measurement modalities that can track DoA sufficiently while being much more affordable. Thus, we investigate this notion with the use of the University of Queensland database, which comprises EEG-EMG-ECG physiological data from patients going through a variety of surgical procedures. As part of this, select patient datasets were utilized in addition to a variety of signal decomposition and machine learning models—which totaled around 200 simulations—in order to investigate the most optimal combination of algorithms to track DoA using different physiological measurement modalities. The results showed that under certain algorithmic combinations and modeling processes, the ECG measurement (a ubiquitous monitor in anesthetic practice) can rival and occasionally surpass the accuracy of the EEG for DoA monitoring. In addition to this, we also propose a 2-phase modeling process that involves an algorithmic selection stage followed by a model deployment stage. Subsequent work in this area is advised to involve the acquisition of more physiological data from a broader mix of patients in order to further validate the consistency of the findings made in this study. Full article
(This article belongs to the Special Issue Feature Papers in Computational Biology and Medicine)
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16 pages, 2316 KiB  
Article
Effective Feature Engineering and Classification of Breast Cancer Diagnosis: A Comparative Study
by Emilija Strelcenia and Simant Prakoonwit
BioMedInformatics 2023, 3(3), 616-631; https://doi.org/10.3390/biomedinformatics3030042 - 2 Aug 2023
Cited by 3 | Viewed by 2723
Abstract
Breast cancer is among the most common cancers found in women, causing cancer-related deaths and making it a severe public health issue. Early prediction of breast cancer can increase the chances of survival and promote early medical treatment. Moreover, the accurate classification of [...] Read more.
Breast cancer is among the most common cancers found in women, causing cancer-related deaths and making it a severe public health issue. Early prediction of breast cancer can increase the chances of survival and promote early medical treatment. Moreover, the accurate classification of benign cases can prevent cancer patients from undergoing unnecessary treatments. Therefore, the accurate and early diagnosis of breast cancer and the classification into benign or malignant classes are much-needed research topics. This paper presents an effective feature engineering method to extract and modify features from data and the effects on different classifiers using the Wisconsin Breast Cancer Diagnosis Dataset. We then use the feature to compare six popular machine-learning models for classification. The models compared were Logistic Regression, Random Forest, Decision Tree, K-Neighbors, Multi-Layer Perception (MLP), and XGBoost. The results showed that the Decision Tree model, when applied to the proposed feature engineering, was the best performing, achieving an average accuracy of 98.64%. Full article
(This article belongs to the Special Issue Feature Papers in Computational Biology and Medicine)
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12 pages, 1084 KiB  
Article
Using Machine Learning to Expand the Ann Arbor Staging System for Hodgkin and Non-Hodgkin Lymphoma
by Huan Wang, Zhenqiu Liu, Julie Yang, Li Sheng and Dechang Chen
BioMedInformatics 2023, 3(3), 514-525; https://doi.org/10.3390/biomedinformatics3030035 - 3 Jul 2023
Viewed by 1839
Abstract
The Ann Arbor system is disadvantaged in utilizing information from additional prognostic factors. In this study, we applied the Ensemble Algorithm for Clustering Cancer Data (EACCD) to create a prognostic system for lymphoma that integrates additional prognostic factors. Hodgkin and non-Hodgkin lymphoma survival [...] Read more.
The Ann Arbor system is disadvantaged in utilizing information from additional prognostic factors. In this study, we applied the Ensemble Algorithm for Clustering Cancer Data (EACCD) to create a prognostic system for lymphoma that integrates additional prognostic factors. Hodgkin and non-Hodgkin lymphoma survival data were extracted from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute and divided into the training set (131,725 cases) and the validation set (15,683 cases). Five prognostic factors were studied: Ann Arbor stage, type, site, age, and sex. EACCD was applied to the training set to produce a prognostic system, called an EACCD system, for convenience. The EACCD system stratified patients into eight prognostic groups with well-separated survival curves. These eight prognostic groups had significantly higher accuracies in survival prediction than the 24 Ann Arbor substages. A higher-risk group in the EACCD system roughly corresponds to a higher Ann Arbor substage. The proposed system shows a good performance in risk stratification and survival prediction on both the training and the validation sets. The EACCD system expands the traditional Ann Arbor staging system by leveraging additional prognostic information and is expected to advance treatment management for lymphoma patients. Full article
(This article belongs to the Special Issue Feature Papers in Computational Biology and Medicine)
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12 pages, 2779 KiB  
Article
Integrative Molecular Analysis of DNA Methylation Dynamics Unveils Molecules with Prognostic Potential in Breast Cancer
by Rashid Mehmood, Alanoud Alsaleh, Muzamil Y. Want, Ijaz Ahmad, Sami Siraj, Muhammad Ishtiaq, Faizah A. Alshehri, Muhammad Naseem and Noriko Yasuhara
BioMedInformatics 2023, 3(2), 434-445; https://doi.org/10.3390/biomedinformatics3020029 - 5 Jun 2023
Cited by 1 | Viewed by 1606
Abstract
DNA methylation acts as a major epigenetic modification in mammals, characterized by the transfer of a methyl group to a cytosine. DNA methylation plays a pivotal role in regulating normal development, and misregulation in cells leads to an abnormal phenotype as is seen [...] Read more.
DNA methylation acts as a major epigenetic modification in mammals, characterized by the transfer of a methyl group to a cytosine. DNA methylation plays a pivotal role in regulating normal development, and misregulation in cells leads to an abnormal phenotype as is seen in several cancers. Any mutations or expression anomalies of genes encoding regulators of DNA methylation may lead to abnormal expression of critical molecules. A comprehensive genomic study encompassing all the genes related to DNA methylation regulation in relation to breast cancer is lacking. We used genomic and transcriptomic datasets from the Cancer Genome Atlas (TGCA) Pan-Cancer Atlas, Genotype-Tissue Expression (GTEx) and microarray platforms and conducted in silico analysis of all the genes related to DNA methylation with respect to writing, reading and erasing this epigenetic mark. Analysis of mutations was conducted using cBioportal, while Xena and KMPlot were utilized for expression changes and patient survival, respectively. Our study identified multiple mutations in the genes encoding regulators of DNA methylation. The expression profiling of these showed significant differences between normal and disease tissues. Moreover, deregulated expression of some of the genes, namely DNMT3B, MBD1, MBD6, BAZ2B, ZBTB38, KLF4, TET2 and TDG, was correlated with patient prognosis. The current study, to our best knowledge, is the first to provide a comprehensive molecular and genetic profile of DNA methylation machinery genes in breast cancer and identifies DNA methylation machinery as an important determinant of the disease progression. The findings of this study will advance our understanding of the etiology of the disease and may serve to identify alternative targets for novel therapeutic strategies in cancer. Full article
(This article belongs to the Special Issue Feature Papers in Computational Biology and Medicine)
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32 pages, 25637 KiB  
Article
Heart Rate Variability by Dynamical Patterns in Windows of Holter Electrocardiograms: A Method to Discern Left Ventricular Hypertrophy in Heart Transplant Patients Shortly after the Transplant
by Danuta Makowiec, Joanna Wdowczyk and Marcin Gruchała
BioMedInformatics 2023, 3(1), 220-251; https://doi.org/10.3390/biomedinformatics3010015 - 1 Mar 2023
Cited by 1 | Viewed by 1655
Abstract
Background: The Holter electrocardiogram (ECG) provides a long signal that represents the heart’s responses to both autonomic regulation and various phenomena, including heart tissue remodeling. Loss of information is a common result when using global statistical metrics. Method: Breaking the signal into short [...] Read more.
Background: The Holter electrocardiogram (ECG) provides a long signal that represents the heart’s responses to both autonomic regulation and various phenomena, including heart tissue remodeling. Loss of information is a common result when using global statistical metrics. Method: Breaking the signal into short data segments (e.g., windows) provides access to transient heart rate characteristics. Symbolization of the ECG by patterns of accelerations and/or decelerations allows using entropic metrics in the assessment of heart rate complexity. Two types of analysis are proposed: (i) visualization of the pattern dynamics of the whole signal, and (ii) scanning the signal for pattern dynamics in a sliding window. The method was applied to a cohort of 42 heart transplant (HTX) recipients divided into the following groups: a left ventricle of normal geometry (NG), concentrically remodeled (CR), hypertrophic remodeled (H), and to the control group (CG) consisting of signals of 41 healthy coevals. The Kruskal–Wallis test was used to assess group differences. Statistical conclusions were verified via bootstrap methods. Results: The visualization of the group pattern dynamics showed severely limited autonomic regulations in HTX patients when compared to CG. The analysis (in segments) prove that the pattern dynamics of the NG group are different from the pattern dynamics observed in the CR and H groups. Conclusion: Dynamic pattern entropy estimators tested in moving windows recognized left ventricular remodeling in stable HTX patients. Full article
(This article belongs to the Special Issue Feature Papers in Computational Biology and Medicine)
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16 pages, 2623 KiB  
Article
Modeling the Double Peak Phenomenon in Drug Absorption Kinetics: The Case of Amisulpride
by Rania Kousovista, Georgia Karali and Vangelis Karalis
BioMedInformatics 2023, 3(1), 177-192; https://doi.org/10.3390/biomedinformatics3010013 - 1 Mar 2023
Cited by 1 | Viewed by 3492
Abstract
An interesting issue observed in some drugs is the “double peak phenomenon” (DPP). In DPP, the concentration-time (C-t) profile does not follow the usual shape but climbs to a peak and then begins to degrade before rising again to a second peak. Such [...] Read more.
An interesting issue observed in some drugs is the “double peak phenomenon” (DPP). In DPP, the concentration-time (C-t) profile does not follow the usual shape but climbs to a peak and then begins to degrade before rising again to a second peak. Such a phenomenon is observed in the case of amisulpride, which is a second-generation antipsychotic. The aim of this study was to develop a model for the description of double peaks in amisulpride after oral administration. Amisulpride plasma C-t data were obtained from a 2 × 2 crossover bioequivalence study in 24 healthy adult subjects. A nonlinear mixed-effects modeling approach was applied in order to perform the analysis. Participants’ characteristics, such as demographics (e.g., body weight, gender, etc.), have also been investigated. A model for describing the double peak phenomenon was successfully developed. Simulations were run using this model to investigate the impact of significant covariates and recommend appropriate dosage regimens. For comparison purposes and to investigate the suitability of our developed model for describing the double peak phenomenon, modeling of previously published population pharmacokinetic models was also applied to the C-t data of this study. Full article
(This article belongs to the Special Issue Feature Papers in Computational Biology and Medicine)
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13 pages, 7393 KiB  
Article
Patient-Level Omics Data Analysis Identifies Gene-Specific Survival Associations for a PD-1/PD-L1 Network in Pleural Mesothelioma
by Geraldine M. O’Connor and Emyr Y. Bakker
BioMedInformatics 2022, 2(4), 580-592; https://doi.org/10.3390/biomedinformatics2040037 - 11 Nov 2022
Cited by 1 | Viewed by 1843
Abstract
Immune checkpoint blockade targeting PDCD1 (PD-1) or CD274 (PD-L1) has demonstrated efficacy and interest across multiple cancers. However, the exact determinants of the response and cancer-specific molecular features remain unclear. A recent pan-cancer study identified a PDCD1/CD274-related immunotherapy network of 40 genes [...] Read more.
Immune checkpoint blockade targeting PDCD1 (PD-1) or CD274 (PD-L1) has demonstrated efficacy and interest across multiple cancers. However, the exact determinants of the response and cancer-specific molecular features remain unclear. A recent pan-cancer study identified a PDCD1/CD274-related immunotherapy network of 40 genes that had differential patient survival associations across multiple cancers. However, the survival relevance of this network in mesothelioma could not be assessed due to a lack of available survival data for the mesothelioma study included. Mesothelioma, a rare cancer that most commonly arises in the pleural membranes around the lung, does have immune checkpoint blockade as an approved treatment strategy, yet questions over its efficacy remain. RNA-seq data from 87 pleural mesothelioma patients were interrogated on cBioPortal to assess the role of the PDCD1/CD274 network identified in a previous study, in addition to identifying repurposed drugs that may have therapeutic efficacy. Extensive literature searches were conducted to identify known information from the literature around the genes shown to impact patient survival (CCR5, GATD3A/GATD3, CXCR6, GZMA, and TBC1D10C). The same literature validation was performed for putative repurposed drugs that were identified as potential immunotherapeutic adjuvants in the context of mesothelioma (disulfiram, terfenadine, maraviroc, clioquinol, chloroxine, and oxyphenbutazone). Only disulfiram returned a specifically focused research article based on the literature search. This article demonstrated cytotoxicity in a panel of five human MPM cell lines of mixed histology (epithelioid, biphasic, and sarcomatoid). There was little information on the remaining five drugs, yet the clear preclinical efficacy of disulfiram validates the methodology used herein and prompts further exploration of the remaining drugs in mesothelioma. This study ultimately sheds light on novel preclinical information of genes related to PDCD1/CD274 in mesothelioma, as well as identifying putative drugs that may have therapeutic efficacy either independently or as an immunotherapeutic adjuvant. Full article
(This article belongs to the Special Issue Feature Papers in Computational Biology and Medicine)
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Study Protocol
Designing, Development, and Evaluation of an Informatics Platform for Enhancing Treatment Adherence in Latent Tuberculosis Infection Patients: A Study Protocol
by Rohitashwa Kumar, Manmohan Singhal, Devendra Kumar, Ashish Joshi and KM Monirul Islam
BioMedInformatics 2023, 3(1), 252-259; https://doi.org/10.3390/biomedinformatics3010016 - 7 Mar 2023
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Abstract
Introduction: Digital health interventions are gradually being incorporated into the management of tuberculosis to ensure treatment adherence, but only a small number of trials focusing on latent tuberculosis infection (LTBI) care have tested and evaluated them. It is anticipated that 170 million persons [...] Read more.
Introduction: Digital health interventions are gradually being incorporated into the management of tuberculosis to ensure treatment adherence, but only a small number of trials focusing on latent tuberculosis infection (LTBI) care have tested and evaluated them. It is anticipated that 170 million persons with LTBI may eventually develop active TB; thus, treatment of LTBI patients is an important aspect, along with ensuring treatment adherence. Digital platforms can be beneficial to ensure treatment adherence in LTBI patients, as various studies have shown the positive impact of digital interventions in improving patients’ treatment adherence and treatment outcome. This study aims to explore the various available digital interventions worldwide for treatment adherence in LTBI patients and develop an informatics platform for enhancing treatment adherence in LTBI patients. Methods: This will be a quasi-experimental study divided into three phases. In the first phase, a scoping review method will be used to conduct a systematic literature review using the PRISMA tool to report on various digital interventions focused on treatment adherence in LTBI patients. In the second phase, a text message-based digital platform will be developed, and in the third phase of the study, an evaluation of the digital platform will be done using qualitative and quantitative questionnaires. The study will be conducted using a mixed-methods approach between January 2023 and December 2023. The sample size will be 162 participants, of whom 81 will be assigned to an intervention group and 81 will receive the usual care from the respective chest clinic as a control group. Results: A descriptive analysis of demographic variables and other variables will be done. Continuous variables will be described as mean ± standard deviation (M ± SD), medians (inter-quartile ranges) (M (IQR)), and medians (5th percentile to 95th percentile) (P5-P95). A two-sample independent T-test, the chi-square test, and the Mann-Whitney test will be used for comparisons between groups. Treatment success between control and intervention will be compared through a chi-square test. Conclusions: The key finding of the study will be an understanding of the efficiency of digital platforms for improving treatment adherence in latent TB patients in India. Full article
(This article belongs to the Special Issue Feature Papers in Computational Biology and Medicine)
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