Journal Description
BioMedInformatics
BioMedInformatics
is an international, peer-reviewed, open access journal on all areas of biomedical informatics, as well as computational biology and medicine, published quarterly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, EBSCO, and other databases.
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 21 days after submission; acceptance to publication is undertaken in 8.8 days (median values for papers published in this journal in the second half of 2023).
- Journal Rank: CiteScore - Q2 (Health Professions (miscellaneous))
- Recognition of Reviewers: APC discount vouchers, optional signed peer review, and reviewer names published annually in the journal.
Latest Articles
Understanding the Molecular Actions of Spike Glycoprotein in SARS-CoV-2 and Issues of a Novel Therapeutic Strategy for the COVID-19 Vaccine
BioMedInformatics 2024, 4(2), 1531-1555; https://doi.org/10.3390/biomedinformatics4020084 (registering DOI) - 9 Jun 2024
Abstract
In vaccine development, many use the spike protein (S protein), which has multiple “spike-like” structures protruding from the spherical structure of the coronavirus, as an antigen. However, there are concerns about its effectiveness and toxicity. When S protein is used in a vaccine,
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In vaccine development, many use the spike protein (S protein), which has multiple “spike-like” structures protruding from the spherical structure of the coronavirus, as an antigen. However, there are concerns about its effectiveness and toxicity. When S protein is used in a vaccine, its ability to attack viruses may be weak, and its effectiveness in eliciting immunity will only last for a short period of time. Moreover, it may cause “antibody-dependent immune enhancement”, which can enhance infections. In addition, the three-dimensional (3D) structure of epitopes is essential for functional analysis and structure-based vaccine design. Additionally, during viral infection, large amounts of extracellular vesicles (EVs) are secreted from infected cells, which function as a communication network between cells and coordinate the response to infection. Under conditions where SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) molecular vaccination produces overwhelming SARS-CoV-2 spike glycoprotein, a significant proportion of the overproduced intracellular spike glycoprotein is transported via EVs. Therefore, it will be important to understand the infection mechanisms of SARA-CoV-2 via EV-dependent and EV-independent uptake into cells and to model the infection processes based on 3D structural features at interaction sites.
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(This article belongs to the Special Issue Features of Bioinformatic Analyses for SARS-CoV-2 Infections and Vaccination)
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Open AccessArticle
Calibrating Glucose Sensors at the Edge: A Stress Generation Model for Tiny ML Drift Compensation
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Anna Sabatini, Costanza Cenerini, Luca Vollero and Danilo Pau
BioMedInformatics 2024, 4(2), 1519-1530; https://doi.org/10.3390/biomedinformatics4020083 (registering DOI) - 9 Jun 2024
Abstract
Background: Continuous glucose monitoring (CGM) systems offer the advantage of noninvasive monitoring and continuous data on glucose fluctuations. This study introduces a new model that enables the generation of synthetic but realistic databases that integrate physiological variables and sensor attributes into a
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Background: Continuous glucose monitoring (CGM) systems offer the advantage of noninvasive monitoring and continuous data on glucose fluctuations. This study introduces a new model that enables the generation of synthetic but realistic databases that integrate physiological variables and sensor attributes into a dataset generation model and this, in turn, enables the design of improved CGM systems. Methods: The presented approach uses a combination of physiological data and sensor characteristics to construct a model that considers the impact of these variables on the accuracy of CGM measures. A dataset of 500 sensor responses over a 15-day period is generated and analyzed using machine learning algorithms (random forest regressor and support vector regressor). Results: The random forest and support vector regression models achieved Mean Absolute Errors (MAEs) of mg/dL and mg/dL, respectively. In contrast, models trained solely on single sensor outputs recorded an average MAE of mg/dL. These findings demonstrate the variable impact of integrating multiple data sources on the predictive accuracy of CGM systems, as well as the complexity of the dataset. Conclusions: This approach provides a foundation for developing more precise algorithms and introduces its initial application of Tiny Machine Control Units (MCUs). More research is recommended to refine these models and validate their effectiveness in clinical settings.
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(This article belongs to the Special Issue Editor's Choices Series for Methods in Biomedical Informatics Section)
Open AccessArticle
Abdominal MRI Unconditional Synthesis with Medical Assessment
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Bernardo Gonçalves, Mariana Silva, Luísa Vieira and Pedro Vieira
BioMedInformatics 2024, 4(2), 1506-1518; https://doi.org/10.3390/biomedinformatics4020082 - 7 Jun 2024
Abstract
Current computer vision models require a significant amount of annotated data to improve their performance in a particular task. However, obtaining the required annotated data is challenging, especially in medicine. Hence, data augmentation techniques play a crucial role. In recent years, generative models
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Current computer vision models require a significant amount of annotated data to improve their performance in a particular task. However, obtaining the required annotated data is challenging, especially in medicine. Hence, data augmentation techniques play a crucial role. In recent years, generative models have been used to create artificial medical images, which have shown promising results. This study aimed to use a state-of-the-art generative model, StyleGAN3, to generate realistic synthetic abdominal magnetic resonance images. These images will be evaluated using quantitative metrics and qualitative assessments by medical professionals. For this purpose, an abdominal MRI dataset acquired at Garcia da Horta Hospital in Almada, Portugal, was used. A subset containing only axial gadolinium-enhanced slices was used to train the model. The obtained Fréchet inception distance value (12.89) aligned with the state of the art, and a medical expert confirmed the significant realism and quality of the images. However, specific issues were identified in the generated images, such as texture variations, visual artefacts and anatomical inconsistencies. Despite these, this work demonstrated that StyleGAN3 is a viable solution to synthesise realistic medical imaging data, particularly in abdominal imaging.
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(This article belongs to the Special Issue Advances in Quantitative Imaging Analysis: From Theory to Practice)
Open AccessArticle
Anomaly Detection and Artificial Intelligence Identified the Pathogenic Role of Apoptosis and RELB Proto-Oncogene, NF-kB Subunit in Diffuse Large B-Cell Lymphoma
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Joaquim Carreras and Rifat Hamoudi
BioMedInformatics 2024, 4(2), 1480-1505; https://doi.org/10.3390/biomedinformatics4020081 - 7 Jun 2024
Abstract
Background: Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent lymphomas. DLBCL is phenotypically, genetically, and clinically heterogeneous. Aim: We aim to identify new prognostic markers. Methods: We performed anomaly detection analysis, other artificial intelligence techniques, and conventional statistics using gene
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Background: Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent lymphomas. DLBCL is phenotypically, genetically, and clinically heterogeneous. Aim: We aim to identify new prognostic markers. Methods: We performed anomaly detection analysis, other artificial intelligence techniques, and conventional statistics using gene expression data of 414 patients from the Lymphoma/Leukemia Molecular Profiling Project (GSE10846), and immunohistochemistry in 10 reactive tonsils and 30 DLBCL cases. Results: First, an unsupervised anomaly detection analysis pinpointed outliers (anomalies) in the series, and 12 genes were identified: DPM2, TRAPPC1, HYAL2, TRIM35, NUDT18, TMEM219, CHCHD10, IGFBP7, LAMTOR2, ZNF688, UBL7, and RELB, which belonged to the apoptosis, MAPK, MTOR, and NF-kB pathways. Second, these 12 genes were used to predict overall survival using machine learning, artificial neural networks, and conventional statistics. In a multivariate Cox regression analysis, high expressions of HYAL2 and UBL7 were correlated with poor overall survival, whereas TRAPPC1, IGFBP7, and RELB were correlated with good overall survival (p < 0.01). As a single marker and only in RCHOP-like treated cases, the prognostic value of RELB was confirmed using GSEA analysis and Kaplan–Meier with log-rank test and validated in the TCGA and GSE57611 datasets. Anomaly detection analysis was successfully tested in the GSE31312 and GSE117556 datasets. Using immunohistochemistry, RELB was positive in B-lymphocytes and macrophage/dendritic-like cells, and correlation with HLA DP-DR, SIRPA, CD85A (LILRB3), PD-L1, MARCO, and TOX was explored. Conclusions: Anomaly detection and other bioinformatic techniques successfully predicted the prognosis of DLBCL, and high RELB was associated with a favorable prognosis.
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(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)
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Open AccessArticle
Physiological Data Augmentation for Eye Movement Gaze in Deep Learning
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Alae Eddine El Hmimdi and Zoï Kapoula
BioMedInformatics 2024, 4(2), 1457-1479; https://doi.org/10.3390/biomedinformatics4020080 - 6 Jun 2024
Abstract
In this study, the challenges posed by limited annotated medical data in the field of eye movement AI analysis are addressed through the introduction of a novel physiologically based gaze data augmentation library. Unlike traditional augmentation methods, which may introduce artifacts and alter
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In this study, the challenges posed by limited annotated medical data in the field of eye movement AI analysis are addressed through the introduction of a novel physiologically based gaze data augmentation library. Unlike traditional augmentation methods, which may introduce artifacts and alter pathological features in medical datasets, the proposed library emulates natural head movements during gaze data collection. This approach enhances sample diversity without compromising authenticity. The library evaluation was conducted on both CNN and hybrid architectures using distinct datasets, demonstrating its effectiveness in regularizing the training process and improving generalization. What is particularly noteworthy is the achievement of a macro F1 score of up to 79% when trained using the proposed augmentation (EMULATE) with the three HTCE variants. This pioneering approach leverages domain-specific knowledge to contribute to the robustness and authenticity of deep learning models in the medical domain.
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(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)
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Open AccessReview
Unlocking the Future of Drug Development: Generative AI, Digital Twins, and Beyond
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Zamara Mariam, Sarfaraz K. Niazi and Matthias Magoola
BioMedInformatics 2024, 4(2), 1441-1456; https://doi.org/10.3390/biomedinformatics4020079 - 6 Jun 2024
Abstract
This article delves into the intersection of generative AI and digital twins within drug discovery, exploring their synergistic potential to revolutionize pharmaceutical research and development. Through various instances and examples, we illuminate how generative AI algorithms, capable of simulating vast chemical spaces and
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This article delves into the intersection of generative AI and digital twins within drug discovery, exploring their synergistic potential to revolutionize pharmaceutical research and development. Through various instances and examples, we illuminate how generative AI algorithms, capable of simulating vast chemical spaces and predicting molecular properties, are increasingly integrated with digital twins of biological systems to expedite drug discovery. By harnessing the power of computational models and machine learning, researchers can design novel compounds tailored to specific targets, optimize drug candidates, and simulate their behavior within virtual biological environments. This paradigm shift offers unprecedented opportunities for accelerating drug development, reducing costs, and, ultimately, improving patient outcomes. As we navigate this rapidly evolving landscape, collaboration between interdisciplinary teams and continued innovation will be paramount in realizing the promise of generative AI and digital twins in advancing drug discovery.
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(This article belongs to the Special Issue Advances in Structural Bioinformatics and Next-Generation Sequence Analysis for Drug Design)
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Open AccessArticle
A Study on the Effects of Cementless Total Knee Arthroplasty Implants’ Surface Morphology via Finite Element Analysis
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Peter J. Hunt, Mohammad Noori, Scott J. Hazelwood, Naudereh B. Noori and Wael A. Altabey
BioMedInformatics 2024, 4(2), 1425-1440; https://doi.org/10.3390/biomedinformatics4020078 - 3 Jun 2024
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Total knee arthroplasty (TKA) is one of the most commonly performed orthopedic surgeries, with nearly one million performed in 2020 in the United States alone. Changing patient demographics, predominately indicated by increases in younger, more active, and more obese patients undergoing TKA, poses
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Total knee arthroplasty (TKA) is one of the most commonly performed orthopedic surgeries, with nearly one million performed in 2020 in the United States alone. Changing patient demographics, predominately indicated by increases in younger, more active, and more obese patients undergoing TKA, poses a challenge to orthopedic surgeons as these factors present a greater risk of long-term complications. Historically, cemented TKA has been the gold standard for fixation, but long-term aseptic loosening continues to be a risk for cemented implants. Cementless TKA, which relies on the surface morphology of a porous coating for biologic fixation of implant to bone, may provide improved long-term survivorship compared with cement. The quality of this bond is dependent on an interference fit and the roughness, or coefficient of friction, between the implant and the bonebone. Stress shielding is a measure of the difference in the stress experienced by implanted bone versus surrounding native bone. A finite element model (FEM) can be used to quantify and better understand stress shielding in order to better evaluate and optimize implant design. In this study, a FEM was constructed to investigate how the surface coating of cementless implants (coefficient of friction) and the location of the coating application affected the stress-shielding response in the tibia. It was determined that the stress distribution in the native tibia surrounding a cementless TKA implant was dependent on the coefficient of friction applied at the tip of the implant’s stem. Materials with lower friction coefficients applied to the stem tip resulted in higher compressive stress experienced by implanted bone, and more favorable overall stress-shielding responses.
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Open AccessArticle
Evaluating Ovarian Cancer Chemotherapy Response Using Gene Expression Data and Machine Learning
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Soukaina Amniouel, Keertana Yalamanchili, Sreenidhi Sankararaman and Mohsin Saleet Jafri
BioMedInformatics 2024, 4(2), 1396-1424; https://doi.org/10.3390/biomedinformatics4020077 - 22 May 2024
Abstract
Background: Ovarian cancer (OC) is the most lethal gynecological cancer in the United States. Among the different types of OC, serous ovarian cancer (SOC) stands out as the most prevalent. Transcriptomics techniques generate extensive gene expression data, yet only a few of these
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Background: Ovarian cancer (OC) is the most lethal gynecological cancer in the United States. Among the different types of OC, serous ovarian cancer (SOC) stands out as the most prevalent. Transcriptomics techniques generate extensive gene expression data, yet only a few of these genes are relevant to clinical diagnosis. Methods: Methods for feature selection (FS) address the challenges of high dimensionality in extensive datasets. This study proposes a computational framework that applies FS techniques to identify genes highly associated with platinum-based chemotherapy response on SOC patients. Using SOC datasets from the Gene Expression Omnibus (GEO) database, LASSO and varSelRF FS methods were employed. Machine learning classification algorithms such as random forest (RF) and support vector machine (SVM) were also used to evaluate the performance of the models. Results: The proposed framework has identified biomarkers panels with 9 and 10 genes that are highly correlated with platinum–paclitaxel and platinum-only response in SOC patients, respectively. The predictive models have been trained using the identified gene signatures and accuracy of above 90% was achieved. Conclusions: In this study, we propose that applying multiple feature selection methods not only effectively reduces the number of identified biomarkers, enhancing their biological relevance, but also corroborates the efficacy of drug response prediction models in cancer treatment.
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(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)
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Bioinformatics-Based Identification of Human B-Cell Receptor (BCR) Stimulation-Associated Genes and Putative Promoters
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Ethan Deitcher, Kirk Trisler, Branden S. Moriarity, Caleb J. Bostwick, Fleur A. D. Leenen and Steven R. Deitcher
BioMedInformatics 2024, 4(2), 1384-1395; https://doi.org/10.3390/biomedinformatics4020076 - 20 May 2024
Abstract
Genome engineered B-cells are being developed for chronic, systemic in vivo protein replacement therapies and for localized, tumor cell-actuated anticancer therapeutics. For continuous systemic engineered protein production, expression may be driven by constitutively active promoters. For actuated payload delivery, B-cell conditional expression could
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Genome engineered B-cells are being developed for chronic, systemic in vivo protein replacement therapies and for localized, tumor cell-actuated anticancer therapeutics. For continuous systemic engineered protein production, expression may be driven by constitutively active promoters. For actuated payload delivery, B-cell conditional expression could be based on transgene alternate splicing or heterologous promotors activated after engineered B-cell receptor (BCR) stimulation. This study used a bioinformatics-based approach to identify putative BCR-stimulated gene promoters. Gene expression data at four timepoints (60, 90, 210, and 390 min) following in vitro BCR stimulation using an anti-IgM antibody in B-cells from six healthy donors were analyzed using R (4.2.2). Differentially upregulated genes were stringently defined as those with adjusted p-value < 0.01 and a log2FoldChange > 1.5. The most upregulated and statistically significant genes were further analyzed to find those with the lowest unstimulated B-cell expression. Of the 46 significantly upregulated genes at 390 min post-BCR stimulation, 6 had average unstimulated expression below the median unstimulated expression at 390 min for all 54,675 gene probes. This bioinformatics-based identification of 6 relatively quiescent genes at baseline that are upregulated by BCR-stimulation (“on-switch”) provides a set of promising promotors for inclusion in future transgene designs and engineered B-cell therapeutics development.
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(This article belongs to the Section Applied Biomedical Data Science)
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Open AccessCommunication
The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare
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Ankush U. Patel, Qiangqiang Gu, Ronda Esper, Danielle Maeser and Nicole Maeser
BioMedInformatics 2024, 4(2), 1363-1383; https://doi.org/10.3390/biomedinformatics4020075 - 17 May 2024
Abstract
As artificial intelligence (AI) integrates within the intersecting domains of healthcare and computational biology, developing interpretable models tailored to medical contexts is met with significant challenges. Explainable AI (XAI) is vital for fostering trust and enabling effective use of AI in healthcare, particularly
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As artificial intelligence (AI) integrates within the intersecting domains of healthcare and computational biology, developing interpretable models tailored to medical contexts is met with significant challenges. Explainable AI (XAI) is vital for fostering trust and enabling effective use of AI in healthcare, particularly in image-based specialties such as pathology and radiology where adjunctive AI solutions for diagnostic image analysis are increasingly utilized. Overcoming these challenges necessitates interdisciplinary collaboration, essential for advancing XAI to enhance patient care. This commentary underscores the critical role of interdisciplinary conferences in promoting the necessary cross-disciplinary exchange for XAI innovation. A literature review was conducted to identify key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare. The distinctive contributions of specialized conferences in fostering dialogue, driving innovation, and influencing research directions were scrutinized. Best practices and recommendations for fostering collaboration, organizing conferences, and achieving targeted XAI solutions were adapted from the literature. By enabling crucial collaborative junctures that drive XAI progress, interdisciplinary conferences integrate diverse insights to produce new ideas, identify knowledge gaps, crystallize solutions, and spur long-term partnerships that generate high-impact research. Thoughtful structuring of these events, such as including sessions focused on theoretical foundations, real-world applications, and standardized evaluation, along with ample networking opportunities, is key to directing varied expertise toward overcoming core challenges. Successful collaborations depend on building mutual understanding and respect, clear communication, defined roles, and a shared commitment to the ethical development of robust, interpretable models. Specialized conferences are essential to shape the future of explainable AI and computational biology, contributing to improved patient outcomes and healthcare innovations. Recognizing the catalytic power of this collaborative model is key to accelerating the innovation and implementation of interpretable AI in medicine.
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(This article belongs to the Topic Computational Intelligence and Bioinformatics (CIB))
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Machine Learning in Allergic Contact Dermatitis: Identifying (Dis)similarities between Polysensitized and Monosensitized Patients
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Aikaterini Kyritsi, Anna Tagka, Alexander Stratigos and Vangelis D. Karalis
BioMedInformatics 2024, 4(2), 1348-1362; https://doi.org/10.3390/biomedinformatics4020074 - 17 May 2024
Abstract
Background: Allergic contact dermatitis (ACD) is a delayed hypersensitivity reaction occurring in sensitized individuals due to exposure to allergens. Polysensitization, defined as positive reactions to multiple unrelated haptens, increases the risk of ACD development and affects patients’ quality of life. The aim of
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Background: Allergic contact dermatitis (ACD) is a delayed hypersensitivity reaction occurring in sensitized individuals due to exposure to allergens. Polysensitization, defined as positive reactions to multiple unrelated haptens, increases the risk of ACD development and affects patients’ quality of life. The aim of this study is to apply machine learning in order to analyze the association between ACD, polysensitization, individual susceptibility, and patients’ characteristics. Methods: Patch test results and demographics from 400 ACD patients (Study protocol Nr. 3765/2022), categorized as polysensitized or monosensitized, were analyzed. Classic statistical analysis and multiple correspondence analysis (MCA) were utilized to explore relationships among variables. Results: The findings revealed significant associations between patient characteristics and ACD patterns, with hand dermatitis showing the strongest correlation. MCA provided insights into the complex interplay of demographic and clinical factors influencing ACD prevalence. Conclusion: Overall, this study highlights the potential of machine learning in unveiling hidden patterns within dermatological data, paving the way for future advancements in the field.
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(This article belongs to the Special Issue Editor's Choices Series for Methods in Biomedical Informatics Section)
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Open AccessReview
A Comprehensive Review of the Impact of Machine Learning and Omics on Rare Neurological Diseases
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Nofe Alganmi
BioMedInformatics 2024, 4(2), 1329-1347; https://doi.org/10.3390/biomedinformatics4020073 - 16 May 2024
Abstract
Background: Rare diseases, predominantly caused by genetic factors and often presenting neurological manifestations, are significantly underrepresented in research. This review addresses the urgent need for advanced research in rare neurological diseases (RNDs), which suffer from a data scarcity and diagnostic challenges. Bridging the
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Background: Rare diseases, predominantly caused by genetic factors and often presenting neurological manifestations, are significantly underrepresented in research. This review addresses the urgent need for advanced research in rare neurological diseases (RNDs), which suffer from a data scarcity and diagnostic challenges. Bridging the gap in RND research is the integration of machine learning (ML) and omics technologies, offering potential insights into the genetic and molecular complexities of these conditions. Methods: We employed a structured search strategy, using a combination of machine learning and omics-related keywords, alongside the names and synonyms of 1840 RNDs as identified by Orphanet. Our inclusion criteria were limited to English language articles that utilized specific ML algorithms in the analysis of omics data related to RNDs. We excluded reviews and animal studies, focusing solely on studies with the clear application of ML in omics data to ensure the relevance and specificity of our research corpus. Results: The structured search revealed the growing use of machine learning algorithms for the discovery of biomarkers and diagnosis of rare neurological diseases (RNDs), with a primary focus on genomics and radiomics because genetic factors and imaging techniques play a crucial role in determining the severity of these diseases. With AI, we can improve diagnosis and mutation detection and develop personalized treatment plans. There are, however, several challenges, including small sample sizes, data heterogeneity, model interpretability, and the need for external validation studies. Conclusions: The sparse knowledge of valid biomarkers, disease pathogenesis, and treatments for rare diseases presents a significant challenge for RND research. The integration of omics and machine learning technologies, coupled with collaboration among stakeholders, is essential to develop personalized treatment plans and improve patient outcomes in this critical medical domain.
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(This article belongs to the Special Issue Editor's Choices Series for Clinical Informatics Section)
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Open AccessReview
Perspectives on Resolving Diagnostic Challenges between Myocardial Infarction and Takotsubo Cardiomyopathy Leveraging Artificial Intelligence
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Serin Moideen Sheriff, Aaftab Sethi, Divyanshi Sood, Sourav Bansal, Aastha Goudel, Manish Murlidhar, Devanshi N. Damani, Kanchan Kulkarni and Shivaram P. Arunachalam
BioMedInformatics 2024, 4(2), 1308-1328; https://doi.org/10.3390/biomedinformatics4020072 - 13 May 2024
Abstract
Background: cardiovascular diseases, including acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC), are significant causes of morbidity and mortality worldwide. Timely differentiation of these conditions is essential for effective patient management and improved outcomes. Methods: We conducted a review focusing on studies that
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Background: cardiovascular diseases, including acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC), are significant causes of morbidity and mortality worldwide. Timely differentiation of these conditions is essential for effective patient management and improved outcomes. Methods: We conducted a review focusing on studies that applied artificial intelligence (AI) techniques to differentiate between acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC). Inclusion criteria comprised studies utilizing various AI modalities, such as deep learning, ensemble methods, or other machine learning techniques, for discrimination between AMI and TTC. Additionally, studies employing imaging techniques, including echocardiography, cardiac magnetic resonance imaging, and coronary angiography, for cardiac disease diagnosis were considered. Publications included were limited to those available in peer-reviewed journals. Exclusion criteria were applied to studies not relevant to the discrimination between AMI and TTC, lacking detailed methodology or results pertinent to the AI application in cardiac disease diagnosis, not utilizing AI modalities or relying solely on invasive techniques for differentiation between AMI and TTC, and non-English publications. Results: The strengths and limitations of AI-based approaches are critically evaluated, including factors affecting performance, such as reliability and generalizability. The review delves into challenges associated with model interpretability, ethical implications, patient perspectives, and inconsistent image quality due to manual dependency, highlighting the need for further research. Conclusions: This review article highlights the promising advantages of AI technologies in distinguishing AMI from TTC, enabling early diagnosis and personalized treatments. However, extensive validation and real-world implementation are necessary before integrating AI tools into routine clinical practice. It is vital to emphasize that while AI can efficiently assist, it cannot entirely replace physicians. Collaborative efforts among clinicians, researchers, and AI experts are essential to unlock the potential of these transformative technologies fully.
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(This article belongs to the Special Issue Computational Biology and Artificial Intelligence in Medicine)
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Open AccessArticle
IMPI: An Interface for Low-Frequency Point Mutation Identification Exemplified on Resistance Mutations in Chronic Myeloid Leukemia
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Julia Vetter, Jonathan Burghofer, Theodora Malli, Anna M. Lin, Gerald Webersinke, Markus Wiederstein, Stephan M. Winkler and Susanne Schaller
BioMedInformatics 2024, 4(2), 1289-1307; https://doi.org/10.3390/biomedinformatics4020071 - 13 May 2024
Abstract
Background: In genomics, highly sensitive point mutation detection is particularly relevant for cancer diagnosis and early relapse detection. Next-generation sequencing combined with unique molecular identifiers (UMIs) is known to improve the mutation detection sensitivity. Methods: We present an open-source bioinformatics framework named Interface
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Background: In genomics, highly sensitive point mutation detection is particularly relevant for cancer diagnosis and early relapse detection. Next-generation sequencing combined with unique molecular identifiers (UMIs) is known to improve the mutation detection sensitivity. Methods: We present an open-source bioinformatics framework named Interface for Point Mutation Identification (IMPI) with a graphical user interface (GUI) for processing especially small-scale NGS data to identify variants. IMPI ensures detailed UMI analysis and clustering, as well as initial raw read processing, and consensus sequence building. Furthermore, the effects of custom algorithm and parameter settings for NGS data pre-processing and UMI collapsing (e.g., UMI clustered versus unclustered (raw) reads) can be investigated. Additionally, IMPI implements optimization and quality control methods; an evolution strategy is used for parameter optimization. Results: IMPI was designed, implemented, and tested using BCR::ABL1 fusion gene kinase domain sequencing data. In summary, IMPI enables a detailed analysis of the impact of UMI clustering and parameter setting changes on the measured allele frequencies. Conclusions: Regarding the BCR::ABL1 data, IMPI’s results underlined the need for caution while designing specialized single amplicon NGS approaches due to methodical limitations (e.g., high PCR-mediated recombination rate). This cannot be corrected using UMIs.
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(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)
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Open AccessArticle
Cancer Classification from Gene Expression Using Ensemble Learning with an Influential Feature Selection Technique
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Nusrath Tabassum, Md Abdus Samad Kamal, M. A. H. Akhand and Kou Yamada
BioMedInformatics 2024, 4(2), 1275-1288; https://doi.org/10.3390/biomedinformatics4020070 - 13 May 2024
Abstract
Uncontrolled abnormal cell growth, known as cancer, may lead to tumors, immune system deterioration, and other fatal disability. Early cancer identification makes cancer treatment easier and increases the recovery rate, resulting in less mortality. Gene expression data play a crucial role in cancer
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Uncontrolled abnormal cell growth, known as cancer, may lead to tumors, immune system deterioration, and other fatal disability. Early cancer identification makes cancer treatment easier and increases the recovery rate, resulting in less mortality. Gene expression data play a crucial role in cancer classification at an early stage. Accurate cancer classification is a complex and challenging task due to the high-dimensional nature of the gene expression data relative to the small sample size. This research proposes using a dimensionality-reduction technique to address this limitation. Specifically, the mutual information (MI) technique is first utilized to select influential biomarker genes. Next, an ensemble learning model is applied to the reduced dataset using only the most influential features (genes) to develop an effective cancer classification model. The bagging method, where the base classifiers are Multilayer Perceptrons (MLPs), is chosen as an ensemble technique. The proposed cancer classification model, the MI-Bagging method, is applied to several benchmark gene expression datasets containing distinctive cancer classes. The cancer classification accuracy of the proposed model is compared with the relevant existing methods. The experimental results indicate that the proposed model outperforms the existing methods, and it is effective and competent for cancer classification despite the limited size of gene expression data with high dimensionality. The highest accuracy achieved by the proposed method demonstrates that the proposed emerging gene-expression-based cancer classifier has the potential to help in cancer treatment and lead to a higher cancer survival rate in the future.
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(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)
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Open AccessArticle
A Smartphone-Based Algorithm for L Test Subtask Segmentation
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Alexis L. McCreath Frangakis, Edward D. Lemaire and Natalie Baddour
BioMedInformatics 2024, 4(2), 1262-1274; https://doi.org/10.3390/biomedinformatics4020069 - 10 May 2024
Abstract
Background: Subtask segmentation can provide useful information from clinical tests, allowing clinicians to better assess a patient’s mobility status. A new smartphone-based algorithm was developed to segment the L Test of functional mobility into stand-up, sit-down, and turn subtasks. Methods: Twenty-one able-bodied participants
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Background: Subtask segmentation can provide useful information from clinical tests, allowing clinicians to better assess a patient’s mobility status. A new smartphone-based algorithm was developed to segment the L Test of functional mobility into stand-up, sit-down, and turn subtasks. Methods: Twenty-one able-bodied participants each completed five L Test trials, with a smartphone attached to their posterior pelvis. The smartphone used a custom-designed application that collected linear acceleration, gyroscope, and magnetometer data, which were then put into a threshold-based algorithm for subtask segmentation. Results: The algorithm produced good results (>97% accuracy, >98% specificity, >74% sensitivity) for all subtasks. Conclusions: These results were a substantial improvement compared with previously published results for the L Test, as well as similar functional mobility tests. This smartphone-based approach is an accessible method for providing useful metrics from the L Test that can lead to better clinical decision-making.
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(This article belongs to the Special Issue Editor's Choices Series for Methods in Biomedical Informatics Section)
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Open AccessArticle
ConsensusPrime—A Bioinformatic Pipeline for Efficient Consensus Primer Design—Detection of Various Resistance and Virulence Factors in MRSA—A Case Study
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Maximilian Collatz, Martin Reinicke, Celia Diezel, Sascha D. Braun, Stefan Monecke, Annett Reissig and Ralf Ehricht
BioMedInformatics 2024, 4(2), 1249-1261; https://doi.org/10.3390/biomedinformatics4020068 - 10 May 2024
Abstract
Background: The effectiveness and reliability of diagnostic tests that detect DNA sequences largely hinge on the quality of the used primers and probes. This importance is especially evident when considering the specific sample being analyzed, as it affects the molecular background and potential
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Background: The effectiveness and reliability of diagnostic tests that detect DNA sequences largely hinge on the quality of the used primers and probes. This importance is especially evident when considering the specific sample being analyzed, as it affects the molecular background and potential for cross-reactivity, ultimately determining the test’s performance. Methods: Predicting primers based on the consensus sequence of the target has multiple advantages, including high specificity, diagnostic reliability, broad applicability, and long-term validity. Automated curation of the input sequences ensures high-quality primers and probes. Results: Here, we present a use case for developing a set of consensus primers and probes to identify antibiotic resistance and virulence genes in Staphylococcus (S.) aureus using the ConsensusPrime pipeline. Extensive qPCR experiments with several S. aureus strains confirm the exceptional quality of the primers designed using the pipeline. Conclusions: By improving the quality of the input sequences and using the consensus sequence as a basis, the ConsensusPrime pipeline pipeline ensures high-quality primers and probes, which should be the basis of molecular assays.
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(This article belongs to the Special Issue Editor's Choice Series for the Computational Biology and Medicine Section)
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Open AccessReview
Current Applications of Artificial Intelligence in the Neonatal Intensive Care Unit
by
Dimitrios Rallis, Maria Baltogianni, Konstantina Kapetaniou and Vasileios Giapros
BioMedInformatics 2024, 4(2), 1225-1248; https://doi.org/10.3390/biomedinformatics4020067 - 9 May 2024
Abstract
Artificial intelligence (AI) refers to computer algorithms that replicate the cognitive function of humans. Machine learning is widely applicable using structured and unstructured data, while deep learning is derived from the neural networks of the human brain that process and interpret information. During
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Artificial intelligence (AI) refers to computer algorithms that replicate the cognitive function of humans. Machine learning is widely applicable using structured and unstructured data, while deep learning is derived from the neural networks of the human brain that process and interpret information. During the last decades, AI has been introduced in several aspects of healthcare. In this review, we aim to present the current application of AI in the neonatal intensive care unit. AI-based models have been applied to neurocritical care, including automated seizure detection algorithms and electroencephalogram-based hypoxic-ischemic encephalopathy severity grading systems. Moreover, AI models evaluating magnetic resonance imaging contributed to the progress of the evaluation of the neonatal developing brain and the understanding of how prenatal events affect both structural and functional network topologies. Furthermore, AI algorithms have been applied to predict the development of bronchopulmonary dysplasia and assess the extubation readiness of preterm neonates. Automated models have been also used for the detection of retinopathy of prematurity and the need for treatment. Among others, AI algorithms have been utilized for the detection of sepsis, the need for patent ductus arteriosus treatment, the evaluation of jaundice, and the detection of gastrointestinal morbidities. Finally, AI prediction models have been constructed for the evaluation of the neurodevelopmental outcome and the overall mortality of neonates. Although the application of AI in neonatology is encouraging, further research in AI models is warranted in the future including retraining clinical trials, validating the outcomes, and addressing serious ethics issues.
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(This article belongs to the Special Issue Editor-in-Chief's Choices in Biomedical Informatics)
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Open AccessArticle
Selection of the Discriming Feature Using the BEMD’s BIMF for Classification of Breast Cancer Mammography Image
by
Fatima Ghazi, Aziza Benkuider, Fouad Ayoub and Khalil Ibrahimi
BioMedInformatics 2024, 4(2), 1202-1224; https://doi.org/10.3390/biomedinformatics4020066 - 9 May 2024
Abstract
Mammogram exam images are useful in identifying diseases, such as breast cancer, which is one of the deadliest cancers, affecting adult women around the world. Computational image analysis and machine learning techniques can help experts identify abnormalities in these images. In this work
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Mammogram exam images are useful in identifying diseases, such as breast cancer, which is one of the deadliest cancers, affecting adult women around the world. Computational image analysis and machine learning techniques can help experts identify abnormalities in these images. In this work we present a new system to help diagnose and analyze breast mammogram images. To do this, the system a method the Selection of the Most Discriminant Attributes of the images preprocessed by BEMD “SMDA-BEMD”, this entails picking the most pertinent traits from the collection of variables that characterize the state under study. A reduction of attribute based on a transformation of the data also called an extraction of characteristics by extracting the Haralick attributes from the Co-occurrence Matrices Methods “GLCM” this reduction which consists of replacing the initial set of data by a new reduced set, constructed at from the initial set of features extracted by images decomposed using Bidimensional Empirical Multimodal Decomposition “BEMD”, for discrimination of breast mammogram images (healthy and pathology) using BEMD. This decomposition makes it possible to decompose an image into several Bidimensional Intrinsic Mode Functions “BIMFs” modes and a residue. The results obtained show that mammographic images can be represented in a relatively short space by selecting the most discriminating features based on a supervised method where they can be differentiated with high reliability between healthy mammographic images and pathologies, However, certain aspects and findings demonstrate how successful the suggested strategy is to detect the tumor. A BEMD technique is used as preprocessing on mammographic images. This suggested methodology makes it possible to obtain consistent results and establishes the discrimination threshold for mammography images (healthy and pathological), the classification rate is improved (98.6%) compared to existing cutting-edge techniques in the field. This approach is tested and validated on mammographic medical images from the Kenitra-Morocco reproductive health reference center (CRSRKM) which contains breast mammographic images of normal and pathological cases.
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(This article belongs to the Special Issue Feature Papers on Methods in Biomedical Informatics)
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Diagnostic Tool for Early Detection of Rheumatic Disorders Using Machine Learning Algorithm and Predictive Models
by
Godfrey A. Mills, Dzifa Dey, Mohammed Kassim, Aminu Yiwere and Kenneth Broni
BioMedInformatics 2024, 4(2), 1174-1201; https://doi.org/10.3390/biomedinformatics4020065 - 8 May 2024
Abstract
Background: Rheumatic diseases are chronic diseases that affect joints, tendons, ligaments, bones, muscles, and other vital organs. Detection of rheumatic diseases is a complex process that requires careful analysis of heterogeneous content from clinical examinations, patient history, and laboratory investigations. Machine learning techniques
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Background: Rheumatic diseases are chronic diseases that affect joints, tendons, ligaments, bones, muscles, and other vital organs. Detection of rheumatic diseases is a complex process that requires careful analysis of heterogeneous content from clinical examinations, patient history, and laboratory investigations. Machine learning techniques have made it possible to integrate such techniques into the complex diagnostic process to identify inherent features that lead to disease formation, development, and progression for remedial measures. Methods: An automated diagnostic tool using a multilayer neural network computational engine is presented to detect rheumatic disorders and the type of underlying disorder for therapeutic strategies. Rheumatic disorders considered are rheumatoid arthritis, osteoarthritis, and systemic lupus erythematosus. The detection system was trained and tested using 70% and 30% respectively of labelled synthetic dataset of 100,000 records containing both single and multiple disorders. Results: The detection system was able to detect and predict underlying disorders with accuracy of 97.48%, sensitivity of 96.80%, and specificity of 97.50%. Conclusion: The good performance suggests that this solution is robust enough and can be implemented for screening patients for intervention measures. This is a much-needed solution in environments with limited specialists, as the solution promotes task-shifting from the specialist level to the primary healthcare physicians.
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(This article belongs to the Special Issue Editor's Choice Series for the Applied Biomedical Data Science Section)
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