BioMedInformatics

Parkinson’s Disease (PD) is a neurological condition that worsens with time and is characterized bysymptoms such as cognitive impairment andbradykinesia, stiffness, and tremors. Parkinson’s is attributed to the interference of brain cells responsible for dopamine production, a substance regulating communication between brain cells. The brain cells involved in dopamine generation handle adaptation and control, and smooth movement. Convolutional Neural Networks are used to extract distinctive visual characteristics from numerous graphomotor sample representations generated by both PD and control participants. The proposed method presents an optimal feature selection technique based on Deep Learning (DL) and the Dynamic Bag of Features Optimization Technique (DBOFOT). Our method combines neural network-based feature extraction with a strong optimization technique to dynamically choose the most relevant characteristics from biological data. Advanced DL architectures are then used to classify the chosen features, guaranteeing excellent computational efficiency and accuracy. The framework’s adaptability to different datasets further highlights its versatility and potential for further medical applications. With a high accuracy of 0.93, the model accurately identifies 93% of the cases that are categorized as Parkinson’s. Additionally, it has a recall of 0.89, which means that 89% of real Parkinson’s patients are accurately identified. While the recall for Class 0 (Healthy) is 0.75, meaning that 75% of the real healthy cases are properly categorized, the precision decreases to 0.64 for this class, indicating a larger false positive rate. Full article

(1) Background: Differences in health outcomes across populations are due to disparities in access to the social determinants of health (SDoH), such as educational level, household income, and internet access. With several positive outcomes reported with patient portal use, examining the associated social determinants of health is imperative. Objective: This study analyzed the association between social determinants of health—education, health insurance, household income, rurality, and internet access—and patient portal use among adults in the United States before and after the COVID-19 pandemic. (2) Methods: The research used a quantitative, retrospective study design and secondary data from the combined cycles 1 to 4 of the Health Information National Trends Survey 5 (N = 14,103) and 6 (N = 5958). Descriptive statistics and logistic regression were conducted to examine the association between the variables operationalizing SDoH and the use of patient portals. (3) Results: Forty-percent (40%) of respondents reported using a patient portal before the pandemic, and this increased to 61% in 2022. The multivariable logistic regression showed higher odds of patient portal utilization by women compared to men (AOR = 1.56; CI, 1.32–1.83), those with at least a college degree compared to less than high school education (AOR = 2.23; CI, 1.29–3.83), and annual family income of USD 75,000 and above compared to those <USD 20,000 (AOR = 1.59; CI, 1.18–2.15). Those with access to the internet and health insurance also had significantly higher odds of using their patient portals. However, those who identified as Hispanic and non-Hispanic Black and residing in a rural area rather than urban (AOR = 0.72; CI, 0.54–0.95) had significantly lower odds of using their patient portals even after the pandemic. (4) Conclusions: The social determinants of health included in this study showed significant influence on patient portal utilization, which has implications for policymakers and public health stakeholders tasked with promoting patient portal utilization and its benefits. Full article

Background: Heart failure poses a significant global health challenge, with high rates of readmission and mortality. Accurate models to predict these outcomes are essential for effective patient management. This study investigates the impact of data pre-processing techniques on XGBoost model performance in predicting all-cause readmission and mortality among heart failure patients. Methods: A dataset of 168 features from 2008 heart failure patients was used. Pre-processing included handling missing values, categorical encoding, and standardization. Four imputation techniques were compared: Mean, Multivariate Imputation by Chained Equations (MICEs), k-nearest Neighbors (kNNs), and Random Forest (RF). XGBoost models were evaluated using accuracy, recall, F1-score, and Area Under the Curve (AUC). Robustness was assessed through 10-fold cross-validation. Results: The XGBoost model with kNN imputation, one-hot encoding, and standardization outperformed others, with an accuracy of 0.614, recall of 0.551, and F1-score of 0.476. The MICE-based model achieved the highest AUC (0.647) and mean AUC (0.65 ± 0.04) in cross-validation. All pre-processed models outperformed the default XGBoost model (AUC: 0.60). Conclusions: Data pre-processing, especially MICE with one-hot encoding and standardization, improves XGBoost performance in heart failure prediction. However, moderate AUC scores suggest further steps are needed to enhance predictive accuracy. Full article

Background: Gene regulatory networks (GRNs) are complex gene interactions essential for organismal development and stability, and they are crucial for understanding gene-disease links in drug development. Advances in bioinformatics, driven by genomic data and machine learning, have significantly expanded GRN research, enabling deeper insights into these interactions. Methods: This study proposes and demonstrates the potential of BioGRNsemble, a modular and flexible approach for inferring gene regulatory networks from RNA-Seq data. Integrating the GENIE3 and GRNBoost2 algorithms, the BioGRNsemble methodology focuses on providing trimmed-down sub-regulatory networks consisting of transcription and target genes. Results: The methodology was successfully tested on a Drosophila melanogaster Eye gene expression dataset. Our validation analysis using the TFLink online database yielded 3703 verified predicted gene links, out of 534,843 predictions. Conclusion: Although the BioGRNsemble approach presents a promising method for inferring smaller, focused regulatory networks, it encounters challenges related to algorithm sensitivity, prediction bias, validation difficulties, and the potential exclusion of broader regulatory interactions. Improving accuracy and comprehensiveness will require addressing these issues through hyperparameter fine-tuning, the development of alternative scoring mechanisms, and the incorporation of additional validation methods. Full article

Background/Objectives: Clinical trials frequently employ diverse terminologies and definitions to describe similar outcomes, leading to ambiguity and inconsistency in data interpretation. Addressing the variability in clinical outcome reports and integrating semantically similar outcomes is important in healthcare and clinical research. Variability in outcome reporting not only hinders the comparability of clinical trial results but also poses significant challenges in evidence synthesis, meta-analysis, and evidence-based decision-making. Methods: This study investigates variability reduction in outcome measures reporting using rule-based and large language-based models. It aims to mitigate the challenges associated with variability in outcome reporting by comparing these two models. The first approach, which is rule-based, will leverage well-known ontologies, and the second approach exploits sentence-bidirectional encoder representations from transformers (SBERT) to identify semantically similar outcomes along with Generative Pre-training Transformer (GPT) to refine the results. Results: The results show that the relatively low percentages of outcomes are linked to established rule-based ontologies. Analysis of outcomes by word count highlighted the absence of ontological linkage for three-word outcomes, which indicates potential gaps in semantic representation. Conclusions: Employing large language models (LLMs), this study demonstrates its ability to identify similar outcomes, even with more than three words, suggesting a crucial role in outcome harmonization efforts, potentially reducing redundancy and enhancing data interoperability. Full article

Recent developments in Artificial Intelligence have increasingly focused on explainability research. The potential of Explainable Artificial Intelligence (XAI) in producing trustworthy computer-aided diagnosis systems and its usage for knowledge discovery are gaining interest in the medical imaging (MI) community to support the diagnostic process and the discovery of image biomarkers. Most of the existing XAI applications in MI are focused on interpreting the predictions made using deep neural networks, typically including attribution techniques with saliency map approaches and other feature visualization methods. However, these are often criticized for providing incorrect and incomplete representations of the black-box models’ behaviour. This highlights the importance of proposing models intentionally designed to be self-explanatory. In particular, part-prototype (PP) models are interpretable-by-design computer vision (CV) models that base their decision process on learning and identifying representative prototypical parts from input images, and they are gaining increasing interest and results in MI applications. However, the medical field has unique characteristics that could benefit from more advanced implementations of these types of architectures. This narrative review summarizes existing PP networks, their application in MI analysis, and current challenges. Full article

Background: To elucidate the effects of local anesthetic agents (LAs), guinea pigs are used in pharmacological education. Herein, we aimed to develop a simulator for LAs. Previously, we developed a statistical model to simulate the LAs’ effects, and we estimated their parameters (mean [$\mu$] and logarithm of standard deviation [$log\sigma$]) based on the results of animal experiments. The results of the Monte Carlo simulation were similar to those from the animal experiments. However, the drug parameter values widely varied among individuals, because this simulation did not consider correlations among parameters. Method: In this study, we set the correlations among these parameters, and we performed simulations using Monte Carlo simulation. Results: Weakly negative correlations were observed between $\mu$ and $log\sigma$ ($r_{\mu -log\sigma }$). In contrast, weakly positive correlations were observed among $\mu$ ($r_{\mu }$) and among $log\sigma$ ($r_{log\sigma }$). In the Monte Carlo simulation, the variability in duration was significant for small $r_{\mu -log\sigma }$ values, and the correlation for the duration between two drugs was significant for large $r_{\mu }$ and $r_{log\sigma }$ values. When parameters were generated considering the correlation among the parameters, the correlation of the duration among the drugs became larger. Conclusions: These results suggest that parameter generation considering the correlation among parameters is important to reproduce the results of animal experiments in simulations. Full article

Analyses of COVID-19 vaccines have become a forefront of pandemic-related research, as jurisdictions around the world encourage vaccinations as the most assured method to curtail the need for stringent public health measures. Kaplan–Meier models, a form of “survival analysis”, provide a statistical approach to improve the understanding of time-to-event probabilities of occurrence. In applications of epidemiology and the study of vaccines, survival analyses can be implemented to quantify the probability of testing positive for SARS-CoV-2, given a population’s vaccination status. In this study, a large proportion of Ontario COVID-19 testing data is used to derive Kaplan–Meier probability curves for individuals who received two doses of a vaccine during a period of peak Delta variant cases, and again for those receiving three doses during a peak time of the Omicron variant. Data consisting of 614,470 individuals with two doses of a COVID-19 vaccine, and 49,551 individuals with three-doses of vaccine, show that recipients of the Moderna vaccine are slightly less likely to test positive for the virus in a 38-day period following their last vaccination than recipients of the Pfizer vaccine, although the difference between the two is marginal in most age groups. This result is largely consistent for two doses of the vaccines during a Delta variant period, as well as an Omicron variant period. The evaluated probabilities of testing positive align with the publicly reported vaccine efficacies of the mRNA vaccines, supporting the resolution that Kaplan–Meier methods in determining vaccine benefits are a justifiable and useful approach in addressing vaccine-related concerns in the COVID-19 landscape. Full article

Background: The opioid crisis remains a significant public health challenge in North America, highlighted by the substantial need for tools to analyze and understand opioid potency and prescription patterns. Methods: The OralOpioids package automates the retrieval, processing, and analysis of opioid data from Health Canada’s Drug Product Database (DPD) and the U.S. Food and Drug Administration’s (FDA) National Drug Code (NDC) database. It includes functions such as load_Opioid_Table, which integrates country-specific data processing and Morphine Equivalent Dose (MED) calculations, providing a comprehensive dataset for analysis. The package facilitates a comprehensive examination of opioid prescriptions, allowing researchers to identify high-risk opioids and patterns that could inform policy and healthcare practices. Results: The integration of MED calculations with Canadian and U.S. data provides a robust tool for assessing opioid potency and prescribing practices. The OralOpioids R package is an essential tool for public health researchers, enabling a detailed analysis of North American opioid prescriptions. Conclusions: By providing easy access to opioid potency data and supporting cross-national studies, the package plays a critical role in addressing the opioid crisis. It suggests a model for similar tools that could be adapted for global use, enhancing our capacity to manage and mitigate opioid misuse effectively. Full article

Lung cancer is a leading cause of cancer-related deaths worldwide, emphasizing the significance of early detection. Computer-aided diagnostic systems have emerged as valuable tools for aiding radiologists in the analysis of medical images, particularly in the context of lung cancer screening. A typical pipeline for lung cancer diagnosis involves pulmonary nodule detection, segmentation, and classification. Although traditional machine learning methods have been deployed in the previous years with great success, this literature review focuses on state-of-the-art deep learning methods. The objective is to extract key insights and methodologies from deep learning studies that exhibit high experimental results in this domain. This paper delves into the databases utilized, preprocessing steps applied, data augmentation techniques employed, and proposed methods deployed in studies with exceptional outcomes. The reviewed studies predominantly harness cutting-edge deep learning methodologies, encompassing traditional convolutional neural networks (CNNs) and advanced variants such as 3D CNNs, alongside other innovative approaches such as Capsule networks and transformers. The methods examined in these studies reflect the continuous evolution of deep learning techniques for pulmonary nodule detection, segmentation, and classification. The methodologies, datasets, and techniques discussed here collectively contribute to the development of more efficient computer-aided diagnostic systems, empowering radiologists and dfhealthcare professionals in the fight against this deadly disease. Full article

Cell-free protein synthesis (CFPS) has emerged as a powerful tool for protein production, with applications ranging from basic research to biotechnology and pharmaceutical development. However, enhancing the efficiency of CFPS systems remains a crucial challenge for realizing their full potential. Computational strategies offer promising avenues for optimizing CFPS efficiency by providing insights into complex biological processes and enabling rational design approaches. This review provides a comprehensive overview of the computational approaches aimed at enhancing CFPS efficiency. The introduction outlines the significance of CFPS and the role of computational methods in addressing efficiency limitations. It discusses mathematical modeling and simulation-based approaches for predicting protein synthesis kinetics and optimizing CFPS reactions. The review also delves into the design of DNA templates, including codon optimization strategies and mRNA secondary structure prediction tools, to improve protein synthesis efficiency. Furthermore, it explores computational techniques for engineering cell-free transcription and translation machinery, such as the rational design of expression systems and the predictive modeling of ribosome dynamics. The predictive modeling of metabolic pathways and the energy utilization in CFPS systems is also discussed, highlighting metabolic flux analysis and resource allocation strategies. Machine learning and artificial intelligence approaches are being increasingly employed for CFPS optimization, including neural network models, deep learning algorithms, and reinforcement learning for adaptive control. This review presents case studies showcasing successful CFPS optimization using computational methods and discusses applications in synthetic biology, biotechnology, and pharmaceuticals. The challenges and limitations of current computational approaches are addressed, along with future perspectives and emerging trends, such as the integration of multi-omics data and advances in high-throughput screening. The conclusion summarizes key findings, discusses implications for future research directions and applications, and emphasizes opportunities for interdisciplinary collaboration. This review offers valuable insights and prospects regarding computational strategies to enhance CFPS efficiency. It serves as a comprehensive resource, consolidating current knowledge in the field and guiding further advancements. Full article

Background: Evaluating chest X-rays is a complex and high-demand task due to the intrinsic challenges associated with diagnosing a wide range of pulmonary conditions. Therefore, advanced methodologies are required to categorize multiple conditions from chest X-ray images accurately. Methods: This study introduces an optimized deep learning approach designed for the multi-label categorization of chest X-ray images, covering a broad spectrum of conditions, including lung opacity, normative pulmonary states, COVID-19, bacterial pneumonia, viral pneumonia, and tuberculosis. An optimized deep learning model based on the modified VGG16 architecture with SE blocks was developed and applied to a large dataset of chest X-ray images. The model was evaluated against state-of-the-art techniques using metrics such as accuracy, F1-score, precision, recall, and area under the curve (AUC). Results: The modified VGG16-SE model demonstrated superior performance across all evaluated metrics. The model achieved an accuracy of 98.49%, an F1-score of 98.23%, a precision of 98.41%, a recall of 98.07% and an AUC of 98.86%. Conclusion: This study provides an effective deep learning approach for categorizing chest X-rays. The model’s high performance across various lung conditions suggests its potential for integration into clinical workflows, enhancing the accuracy and speed of pulmonary disease diagnosis. Full article

Background: Advancements in genomic technologies are rapidly evolving, with the potential to transform laboratory diagnostics by enabling high-throughput analysis of complex biological data, such as microbiome data. Large Language Models (LLMs) have shown significant promise in extracting actionable insights from vast datasets, but their application in generating microbiome findings reports with clinical interpretations and lifestyle recommendations has not been explored yet. Methods: This article introduces an innovative framework that utilizes LLMs to automate the generation of findings reports in the context of microbiome diagnostics. The proposed model integrates LLMs within an event-driven, workflow-based architecture, designed to enhance scalability and adaptability in clinical laboratory environments. Special focus is given to aligning the model with clinical standards and regulatory guidelines such as the In-Vitro Diagnostic Regulation (IVDR) and the guidelines published by the High-Level Expert Group on Artificial Intelligence (HLEG AI). The implementation of this model was demonstrated through a prototype called “MicroFlow”. Results: The implementation of MicroFlow indicates the viability of automating findings report generation using LLMs. Initial evaluation by laboratory expert users indicated that the integration of LLMs is promising, with the generated reports being plausible and useful, although further testing on real-world data is necessary to assess the model’s accuracy and reliability. Conclusions: This work presents a potential approach for using LLMs to support the generation of findings reports in microbiome diagnostics. While the initial results seem promising, further evaluation and refinement are needed to ensure the model’s effectiveness and adherence to clinical standards. Future efforts will focus on improvements based on feedback from laboratory experts and comprehensive testing on real patient data. Full article

Total ankle arthroplasty (TAA) is a motion-preserving treatment for end-stage ankle arthritis. An effective tool for analyzing these implants’ mechanical performance and longevity in silico is finite element analysis (FEA). An FEA in ABAQUS was used to statically analyze the mechanical behavior of the ultra-high-molecular-weight polyethylene (UHMWPE) bearing component at varying dorsiflexion/plantarflexion ankle angles and axial loading conditions during the stance phase of the gait cycle for a single cycle. The von Mises stress and contact pressure were examined on the articulating surface of the bearing component in two newly installed fixed-bearing TAA implants (Wright Medical INBONE II and Exactech Vantage). Six different FEA models of variable ankle compressive load levels and ankle angle positions, for the varying subphases of the stance phase of the gait cycle, were created. The components in these models were constrained to be conducive to the bone–implant interface, where implant loosening occurs. Our results showed that the von Mises stress and contact pressure distributions increased as the compressive load increased. The highest stress was noted at dorsiflexion angles > 15°, in areas where the UHMWPE liner was thinnest, at the edges of the talar and UHMWPE components, and during the terminal stance phase of the gait cycle. This static structural analysis highlighted these failure regions are susceptible to yielding and wear and indicated stress magnitudes that are in agreement (within 25%) with those in previous static structural TAA FEAs. The mechanical wear of the UHMWPE bearing component in TAA can lead to aseptic loosening and peri-implant cyst formation over time, requiring surgical revision. This study provides ankle replacement manufacturers and orthopedic surgeons with a better understanding of the stress response and contact pressure sustained by TAA implants, which is critical to optimizing implant longevity and improving patient care. Full article

Background: Machine learning models can provide quick and reliable assessments in place of medical practitioners. With over 50 million adults in the United States suffering from osteoarthritis, there is a need for models capable of interpreting musculoskeletal ultrasound images. However, machine learning requires lots of data, which poses significant challenges in medical imaging. Therefore, we explore two strategies for enriching a musculoskeletal ultrasound dataset independent of these limitations: traditional augmentation and diffusion-based image synthesis. Methods: First, we generate augmented and synthetic images to enrich our dataset. Then, we compare the images qualitatively and quantitatively, and evaluate their effectiveness in training a deep learning model for detecting thickened synovium and knee joint recess distension. Results: Our results suggest that synthetic images exhibit some anatomical fidelity, diversity, and help a model learn representations consistent with human opinion. In contrast, augmented images may impede model generalizability. Finally, a model trained on synthetically enriched data outperforms models trained on un-enriched and augmented datasets. Conclusions: We demonstrate that diffusion-based image synthesis is preferable to traditional augmentation. Our study underscores the importance of leveraging dataset enrichment strategies to address data scarcity in medical imaging and paves the way for the development of more advanced diagnostic tools. Full article

Self-supervised learning (SSL) has gained significant attention in the past decade for its capacity to utilize non-annotated datasets to learn meaningful data representations. In the medical domain, the challenge of constructing large annotated datasets presents a significant limitation, rendering SSL an ideal approach to address this constraint. In this study, we introduce a novel pretext task tailored to stimulus-driven eye movement data, along with a denoising task to improve the robustness against simulated eye tracking failures. Our proposed task aims to capture both the characteristics of the pilot (brain) and the motor (eye) by learning to reconstruct the eye movement position signal using up to 12.5% of the unmasked eye movement signal patches, along with the entire REMOBI target signal. Thus, the encoder learns a high-dimensional representation using a multivariate time series of length 8192 points, corresponding to approximately 40 s. We evaluate the learned representation on screening eight distinct groups of pathologies, including dyslexia, reading disorder, and attention deficit disorder, across four datasets of varying complexity and size. Furthermore, we explore various head architecture designs along with different transfer learning methods, demonstrating promising results with improvements of up to approximately 15%, leading to an overall macro F1 score of 61% and 61.5% on the Saccade and the Vergence datasets, respectively. Notably, our method achieves macro F1 scores of 64.7%, 66.1%, and 61.1% for screening dyslexia, reading disorder, and attention deficit disorder, respectively, on clinical data. These findings underscore the potential of self-learning algorithms in pathology screening, particularly in domains involving complex data such as stimulus-driven eye movement analysis. Full article

Background: As patients interact with a healthcare service system, patterns of service utilization (PSUs) emerge. These PSUs are embedded in the sparse high-dimensional space of longitudinal cross-continuum health service encounter data. Once extracted, PSUs can provide quality assurance/quality improvement (QA/QI) efforts with the information required to optimize service system structures and functions. This may improve outcomes for complex patients with chronic diseases. Method: Working with longitudinal cross-continuum encounter data from a regional health service system, various pattern detection analyses were conducted, employing (1) graph community detection algorithms, (2) natural language processing (NLP) clustering, and (3) a hybrid NLP–graph method. Result: These approaches produced similar PSUs, as determined from a clinical perspective by clinical subject matter experts and service system operations experts. Conclusions: The similarity in the results provides validation for the methodologies. Moreover, the results stress the need to engage with clinical or service system operations experts, both in providing the taxonomies and ontologies of the service system, the cohort definitions, and determining the level of granularity that produces the most clinically meaningful results. Finally, the uniqueness of each approach provides an opportunity to take advantage of the various analytical capabilities that each approach brings, which will be further explored in our future research. Full article

Background: In biomedical imaging research, experimental biologists generate vast amounts of data that require advanced computational analysis. Breakthroughs in experimental techniques, such as multiplex immunofluorescence tissue imaging, enable detailed proteomic analysis, but most biomedical researchers lack the programming and Artificial Intelligence (AI) expertise to leverage these innovations effectively. Methods: Cinco de Bio (CdB) is a web-based, collaborative low-code/no-code modelling and execution platform designed to address this challenge. It is designed along Model-Driven Development (MDD) and Service-Orientated Architecture (SOA) to enable modularity and scalability, and it is underpinned by formal methods to ensure correctness. The pre-processing of immunofluorescence images illustrates the ease of use and ease of modelling with CdB in comparison with the current, mostly manual, approaches. Results: CdB simplifies the deployment of data processing services that may use heterogeneous technologies. User-designed models support both a collaborative and user-centred design for biologists. Domain-Specific Languages for the Application domain (A-DSLs) are supported through data and process ontologies/taxonomies. They allow biologists to effectively model workflows in the terminology of their field. Conclusions: Comparative analysis of similar platforms in the literature illustrates the superiority of CdB along a number of comparison dimensions. We are expanding the platform’s capabilities and applying it to other domains of biomedical research. Full article