Computers in biology and medicine最新文献

{"title":"Automatic cough detection via a multi-sensor smart garment using machine learning","authors":"Philippe C. Dixon ,&nbsp;Simon Dubeau ,&nbsp;Jean-François Roy ,&nbsp;Pierre-Alexandre Fournier","doi":"10.1016/j.compbiomed.2025.110192","DOIUrl":"10.1016/j.compbiomed.2025.110192","url":null,"abstract":"<div><div>Coughing behavior is associated with conditions such as sleep apnea, asthma, and chronic obstructive pulmonary disorder and can severely affect quality of life in those affected. In this context, coughing quantification is often important, but routinely performed via questionnaires. This approach is dependent on patient compliance or recall, which may affect validity and be especially difficult for nocturnal coughs. Manual review of audio recordings is potentially more accurate, but raises privacy concerns due to the collection and review of sensitive audio-data by a human annotator. Today, machine learning approaches are increasingly used to quantify coughs; however, algorithms often rely on microphone recordings, resulting in the same privacy issues, especially if data are sent to a remote server for analysis. The aims of this study are to determine if (1) a suite of sensors, excluding microphone recordings, can accurately detect coughs unobtrusively and (2) what the relative importance of each sensor-type on model performance may be. Data from 44 healthy young adult participants performing on-demand coughs and other tasks (breathing, talking, throat clearing, laughing, sniffing) in supine and sitting conditions were collected for this observational, cross-sectional study using a multi-sensor smart-garment device. Synchronized video was used to annotate tasks. Three-dimension acceleration, respiration (inductance plethysmography), and electrical activity (electrocardiography) signals were extracted into 1 s strips and binarized into coughs and non-coughs. Data were split into train and test sets using an inter-subject 80:20 split, ensuring that data from a particular participant are found in a single set. This procedure was repeated 10 times with different random inter-subject splits to assess the variability of results. Statistical and frequency-based features were computed and used as inputs to a Random Forest Classifier to predict classes (cough vs not-cough). Model hyperparameters were tuned to maximize F1-score using five-fold cross validation of the training set. Final model performance was assessed using F1-score, precision, and recall (sensitivity) on the test sets with mean (standard deviation) reported. Single sensor models based on acceleration, respiration, or electrocardiography revealed F1 scores of 92.6 (1.2)%, 88.9 (3.2)%, and 77.5 (3.4)%, respectively. Overall, the dual (acceleration, respiration) sensor model achieved the highest performance (F1-score 93.0 (1.1)%, precision 84.2 (4.2)%, and recall 95.5 (1.6)%). The multi-modal wearable device was able to distinguish coughs from other respiratory maneuvers, with acceleration and respiration sensors providing the most valuable information. Future studies could implement this approach for remote monitoring of coughs in patients suffering from coughing symptoms.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110192"},"PeriodicalIF":7.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost-effectiveness of stress echocardiography and exercise tolerance tests as screening in asymptomatic adults before starting physical activity","authors":"Morris Mosseri ,&nbsp;Jacob Glazer ,&nbsp;Elinor Mosseri Briskin ,&nbsp;Moshe Leshno","doi":"10.1016/j.compbiomed.2025.110175","DOIUrl":"10.1016/j.compbiomed.2025.110175","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Aims&lt;/h3&gt;&lt;div&gt;Previous studies on exercise tolerance screening in asymptomatic individuals before starting physical activity were not cost-effective due to low specificity. However, given progress in diagnosing and treating coronary artery disease (CAD), a reevaluation of this approach is justified. We aimed to examine whether stress echocardiography (SE) would be cost-effective.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods and results&lt;/h3&gt;&lt;div&gt;The study was conducted on asymptomatic individuals with no known coronary disease. The decision tree had two arms: in one arm, the subjects underwent stress echocardiography (SE) as a screening test before starting physical activity, and in the other, they did not. The probabilities and utilities of variables in the decision tree were taken from medical literature, and the costs of treatments were obtained from the Israeli Ministry of Health Tarif (HealthCare in Israel is universal, participation in one of four official health insurance organizations is compulsory, and “supplementary insurance” is optional). A 5-year Markov model and Monte Carlo simulation with 1000 iterations were used to assess cost-effectiveness from the insurer's perspective.&lt;/div&gt;&lt;div&gt;The variables that had the most significant impact on cost-effectiveness were the prior risk of coronary disease and the frequency of physical activity in the population under study. When cost-effectiveness assessment of SE was conducted in subjects receiving optimal medical therapy (OMT) and revascularization either transcutaneously or with bypass surgery, both groups had almost identical benefits, with a slight advantage for those who did not undergo SE. However, the cost was higher for subjects who underwent SE, and the Incremental Cost-Effectiveness Ratio (ICER) favored the No-SE group. On the other hand, when subjects only received OMT without therapeutic catheterization or bypass surgery, a cost-effectiveness assessment of SE demonstrated a lower cost and higher benefit in the group that underwent SE. In fact, SE was found to be absolutely dominant, with a negative ICER of $(−)27,644, which means that performing SE not only adds effectiveness but also saves expenses. Finally, a cost-effectiveness evaluation was conducted to compare the benefits of performing exercise tolerance testing (ETT) without stress echocardiography in subjects receiving OMT without therapeutic catheterization or bypass surgery. The results showed that the group that underwent ETT had a slightly higher benefit at a higher cost, with an ICER of $1804. This value is much lower than a WTP (willingness-to-pay) of $50,000 per year.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;Performing SE as a screening test before starting physical activity in asymptomatic individuals is not cost-effective when the therapeutic options include revascularization. However, when the therapeutic policy is medical therapy without revascularization - as recommended in current guidelines - performing SE screening ","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110175"},"PeriodicalIF":7.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-scale convolutional LSTM-dense network for robust cardiac arrhythmia classification from ECG signals","authors":"Kishor Kumar Reddy C. ,&nbsp;Advaitha Daduvy ,&nbsp;Vijaya Sindhoori Kaza ,&nbsp;Mohammed Shuaib ,&nbsp;Muhammad Mohzary ,&nbsp;Shadab Alam ,&nbsp;Abdullah Sheneamer","doi":"10.1016/j.compbiomed.2025.110121","DOIUrl":"10.1016/j.compbiomed.2025.110121","url":null,"abstract":"<div><div>Cardiac arrhythmias are irregular heart rhythms that, if undetected, can lead to severe cardiovascular conditions. Detecting these anomalies early through electrocardiogram (ECG) signal analysis is critical for preventive healthcare and effective treatment. However, the automatic classification of arrhythmias poses significant challenges, including class imbalance and noise interference in ECG signals. This paper introduces the Multi-Scale Convolutional LSTM Dense Network (MS-CLDNet) model, an advanced deep-learning model specifically designed to address these issues and improve arrhythmia classification accuracy and other relevant metrics. This paper aims to develop an efficient deep-learning model, MS-CLDNet, for accurately classifying cardiac arrhythmias from electrocardiogram (ECG) signals. Addressing challenges like class imbalance and noise interference, the model integrates bidirectional long short-term memory (LSTM) networks for temporal pattern recognition, Dense Blocks for feature refinement, and Multi-Scale Convolutional Neural Networks (CNNs) for robust feature extraction. To achieve accurate classification of different types of arrhythmias, the Classification Head refines these extracted features even further. Utilizing the MIT-BIH arrhythmia dataset, key pre-processing techniques such as wavelet-based denoising were employed to enhance signal clarity. Results indicate that the MS-CLDNet model achieves a classification accuracy of 98.22 %, outperforming baseline models with low average loss values (0.084). This research highlights how crucial it is to combine sophisticated neural network architectures with efficient pre-processing techniques to improve the precision and accuracy of automated cardiovascular diagnostic systems, which could have important healthcare applications for early and accurate arrhythmia detection.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110121"},"PeriodicalIF":7.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-level feature fusion network for kidney disease detection","authors":"Saif Ur Rehman Khan","doi":"10.1016/j.compbiomed.2025.110214","DOIUrl":"10.1016/j.compbiomed.2025.110214","url":null,"abstract":"<div><div>Kidney irregularities pose a significant public health challenge, often leading to severe complications, yet the limited availability of nephrologists makes early detection costly and time-consuming. To address this issue, we propose a deep learning framework for automated kidney disease detection, leveraging feature fusion and sequential modeling techniques to enhance diagnostic accuracy. Our study thoroughly evaluates six pretrained models under identical experimental conditions, identifying ResNet50 and VGG19 as the highly efficient models for feature extraction due to their deep residual learning and hierarchical representations. Our proposed methodology integrates feature fusion with an inception block to extract diverse feature representations while maintaining imbalance dataset overhead. To enhance sequential learning and capture long-term dependencies in disease progression, ConvLSTM is incorporated after feature fusion. Additionally, Inception block is employed after ConvLSTM to refine hierarchical feature extraction, further strengthening the proposed model ability to leverage both spatial and temporal patterns. To validate our approach, we introduce a new named Multiple Hospital Collected (MHC-CT) dataset, consisting of 1860 tumor and 1024 normal kidney CT scans, meticulously annotated by medical experts. Our model achieves 99.60 % accuracy on this dataset, demonstrating its robustness in binary classification. Furthermore, to assess its generalization capability, we evaluate the model on a publicly available benchmark multiclass CT scan dataset, achieving 91.31 % accuracy. The superior performance is attributed to the effective feature fusion using inception blocks and the sequential learning capabilities of ConvLSTM, which together enhance spatial and temporal feature representations. These results highlight the efficacy of the proposed framework in automating kidney disease detection, providing a reliable, and efficient solution for clinical decision-making. <span><span>https://github.com/VS-EYE/KidneyDiseaseDetection.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110214"},"PeriodicalIF":7.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Rad51 inhibition mechanisms of B02 and IBR2 and identifying prospective drug candidates for Rad51: A computational investigation","authors":"Yue Pan ,&nbsp;Qianhe Zhang ,&nbsp;Chaojian Xu ,&nbsp;Yang Sun ,&nbsp;Qingchuan Zheng ,&nbsp;Shuo Yang ,&nbsp;Shaowu Lv","doi":"10.1016/j.compbiomed.2025.110105","DOIUrl":"10.1016/j.compbiomed.2025.110105","url":null,"abstract":"<div><div>Rad51 recombinase is a crucial mediator in homologous recombination, upregulation of Rad51 expression is associated with adverse prognostic outcomes in various types of cancers, rendering it an attractive therapeutic target. Several inhibitors targeting Rad51 have been developed, but their precise interactions with Rad51 at the molecular level and the specific mechanisms by which they inhibit Rad51 function remain largely unexplored. Herein, we employ atomistic molecular simulations, advanced sampling techniques and computational methodologies to elucidate the mechanisms underlying the inhibitory effects of Rad51 inhibitors B02 and IBR2 on Rad51 protein dynamics. Moreover, we leverage multilevel virtual screening strategies to identify potential Rad51 inhibitors from the ChemBL database, emphasizing the pivotal role of key residues within the inhibitor binding pocket for effective inhibitor-protein interaction. Our findings provide insights into the effects of B02 and IBR2 on the molecular dynamics of Rad51 and the alteration of the residue communication network. At the same time, we identified that Cmp-4 and Cmp-9 exhibit dynamics properties similar to Rad51 inhibitors B02 and IBR2, suggesting their potential as candidate therapeutic agents. Our study provides valuable insights into the inhibitory mechanisms of Rad51 inhibitors, offering important theoretical insights for the future development of drugs targeting the Rad51.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110105"},"PeriodicalIF":7.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized heart disease prediction model using a meta-heuristic feature selection with improved binary salp swarm algorithm and stacking classifier","authors":"M. Sowmiya ,&nbsp;B. Banu Rekha ,&nbsp;E. Malar","doi":"10.1016/j.compbiomed.2025.110171","DOIUrl":"10.1016/j.compbiomed.2025.110171","url":null,"abstract":"<div><div>Despite technological advancements, heart disease continues to be a major global health challenge, emphasizing the importance of developing accurate predictive models for early detection and timely intervention. This study proposes a heart disease prediction model integrating a stacking classifier with a nature-inspired meta-heuristic algorithm. It employs an improved Binary Salp Swarm Algorithm (BSSA) by incorporating a wolf optimizer and opposition-based learning for optimal feature selection. The proposed Stacking Classifier (SC) architecture features a two-tier ensemble: heterogeneous base classifiers at level 0 and a meta-learner at level 1. The BSSA is used to identify optimal features, which are then utilized to construct the stacking classifier. Experimental results demonstrate superior performance, achieving 95 % accuracy, 0.92 sensitivity, 0.97 specificity, 0.96 precision, and an F1 score of 0.95, with notably low false positive and false negative rates. Further, validation on larger datasets yielded an accuracy of 87.46 %. The feature selection process adopts a multi-objective strategy which enhances the classification accuracy and outperforms conventional techniques. The proposed method demonstrates significant potential for improving the predictive modelling in clinical settings for diagnosing heart diseases.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110171"},"PeriodicalIF":7.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modified Robust Proportional Overlapping Score for feature selection in high-dimensional micro-array data","authors":"Muhammad Hamraz ,&nbsp;Tahir Abbas ,&nbsp;Fawad Ali ,&nbsp;Dost Muhammad Khan ,&nbsp;Muhammad Aamir","doi":"10.1016/j.compbiomed.2025.110165","DOIUrl":"10.1016/j.compbiomed.2025.110165","url":null,"abstract":"<div><div>High-dimensional microarray datasets often contain tens of thousands of genes but only a small number of samples, typically ranging from tens to a few hundred. This imbalance, known as the curse of dimensionality or the <em>n</em> ≪ <em>p</em> problem, hampers the learning process. To address this issue, this study introduces the Modified Robust Proportional Overlapping Score (MRPOS), an enhanced feature selection method based on robust measures of dispersion, specifically the <em>Sn</em> and <em>Qn</em> statistics by Rousseeuw and Croux. MRPOS identifies discriminative genes in binary class problems by evaluating gene expression overlap. This study considers the four gene expression datasets, each divided into two parts: a training subset covering 70 % of the data and a testing subset holding the remaining 30 %. The MRPOS eliminates genes with high inter-class similarity while retaining those differentiating classes. The method's performance is assessed against four established feature selection techniques using classification error rates from four gene expression datasets. Three classifiers, random forest, k-nearest neighbor (k-NN), and support vector machine (SVM), are employed, with results visualized through bar plots of classification errors. The findings highlight the distinctiveness and effectiveness of the proposed method.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110165"},"PeriodicalIF":7.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational screening of natural products as tryptophan 2,3-dioxygenase inhibitors: Insights from CNN-based QSAR, molecular docking, ADMET, and molecular dynamics simulations","authors":"Yassir Boulaamane ,&nbsp;Santiago Bolivar Avila Jr. ,&nbsp;Juan Rosales Hurtado ,&nbsp;Iman Touati ,&nbsp;Badr-Edine Sadoq ,&nbsp;Aamal A. Al-Mutairi ,&nbsp;Ali Irfan ,&nbsp;Sami A. Al-Hussain ,&nbsp;Amal Maurady ,&nbsp;Magdi E.A. Zaki","doi":"10.1016/j.compbiomed.2025.110199","DOIUrl":"10.1016/j.compbiomed.2025.110199","url":null,"abstract":"<div><div>Parkinson's disease (PD) is characterised by a complex array of motor, psychiatric, and gastrointestinal symptoms, many of which are linked to disruptions in neuroactive metabolites. Dysregulated activity of tryptophan 2,3-dioxygenase (TDO), a key enzyme in the kynurenine pathway (KP), has been implicated in these disturbances. TDO's regulation of tryptophan metabolism outside the central nervous system (CNS) plays a critical role in maintaining the balance between serotonin and kynurenine-derived metabolites, with its dysfunction contributing to the worsening of PD symptoms. Recent studies suggest that targeting TDO may help alleviate non-motor symptoms of PD, providing an alternative approach to conventional dopamine replacement therapies.</div><div>In this study, a data-driven computational pipeline was employed to identify natural products as potential TDO inhibitors. Machine learning and convolutional neural network-based QSAR models were developed to predict TDO inhibitory activity. Molecular docking revealed strong binding affinities for several compounds, with docking scores ranging from −9.6 to −10.71 kcal/mol, surpassing that of tryptophan (−6.86 kcal/mol), and indicating favourable interactions. ADMET profiling assessed pharmacokinetic properties, confirming that the selected compounds could cross the blood–brain barrier (BBB), suggesting potential CNS activity. Molecular dynamics (MD) simulations provided further insight into the binding stability and dynamic behaviour of the top candidates within the TDO active site under physiological conditions. Notably, Peniciherquamide C maintained stronger and more stable interactions than the native substrate tryptophan throughout the simulation. MM/PBSA decomposition analysis highlighted the energetic contributions of van der Waals, electrostatic, and solvation forces, supporting the binding stability of key compounds.</div><div>This integrated computational approach highlights the potential of natural products as TDO inhibitors, identifying promising leads that address PD symptoms beyond traditional dopamine-centric therapies. Nonetheless, experimental validation is necessary to confirm these findings.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110199"},"PeriodicalIF":7.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal validation of machine learning models for pre-eclampsia prediction using routinely collected maternal characteristics: A validation study","authors":"Sofonyas Abebaw Tiruneh ,&nbsp;Daniel Lorber Rolnik ,&nbsp;Helena Teede ,&nbsp;Joanne Enticott","doi":"10.1016/j.compbiomed.2025.110183","DOIUrl":"10.1016/j.compbiomed.2025.110183","url":null,"abstract":"<div><h3>Background</h3><div>Pre-eclampsia (PE) contributes to more than one-fourth of all maternal deaths and half a million newborn deaths worldwide every year. Early screening and interventions can reduce PE incidence and related complications. We aim to 1) temporally validate three existing models (two machine learning (ML) and one logistic regression) developed in the same region and 2) compare the performances of the validated ML models with the logistic regression model in PE prediction. This work addresses a gap in the literature by undertaking a comprehensive evaluation of existing risk prediction models, which is an important step to advancing this field.</div></div><div><h3>Methods</h3><div>We obtained a dataset of routinely collected antenatal data from three maternity hospitals in South-East Melbourne, Australia, extracted between July 2021 and December 2022. We temporally validated three existing models: extreme gradient boosting (XGBoost, ‘model 1’), random forest (‘model 2’) ML models, and a logistic regression model (‘model 3’). Area under the receiver-operating characteristic (ROC) curve (AUC) was evaluated discrimination performance, and calibration was assessed. The AUCs were compared using the ‘bootstrapping’ test.</div></div><div><h3>Results</h3><div>The temporal evaluation dataset consisted of 12,549 singleton pregnancies, of which 431 (3.43 %, 95 % confidence interval (CI) 3.13–3.77) developed PE. The characteristics of the temporal evaluation dataset were similar to the original development dataset. The XGBoost ‘model 1’ and the logistic regression ‘model 3’ exhibited similar discrimination performance with an AUC of 0.75 (95 % CI 0.73–0.78) and 0.76 (95 % CI 0.74–0.78), respectively. The random forest ‘model 2’ showed a discrimination performance of AUC 0.71 (95 % CI 0.69–0.74). Model 3 showed perfect calibration performance with a slope of 1.02 (95 % CI 0.92–1.12). Models 1 and 2 showed a calibration slope of 1.15 (95 % CI 1.03–1.28) and 0.62 (95 % CI 0.54–0.70), respectively. Compared to the original development models, the temporally validated models 1 and 3 showed stable discrimination performance, whereas model 2 showed significantly lower discrimination performance. Models 1 and 3 showed better clinical net benefits between 3 % and 22 % threshold probabilities than default strategies.</div></div><div><h3>Conclusions</h3><div>During temporal validation of PE prediction models, logistic regression and XGBoost models exhibited stable prediction performance; however, both ML models did not outperform the logistic regression model. To facilitate insights into interpretability and deployment, the logistic regression model could be integrated into routine practice as a first-step in a two-stage screening approach to identify a higher-risk woman for further second stage screening with a more accurate test.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110183"},"PeriodicalIF":7.0,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BCOR-rearranged sarcomas: In silico insights into altered domains and BCOR interactions","authors":"Kristóf Madarász ,&nbsp;János András Mótyán ,&nbsp;Yi-Che Chang Chien ,&nbsp;Judit Bedekovics ,&nbsp;Szilvia Lilla Csoma ,&nbsp;Gábor Méhes ,&nbsp;Attila Mokánszki","doi":"10.1016/j.compbiomed.2025.110144","DOIUrl":"10.1016/j.compbiomed.2025.110144","url":null,"abstract":"<div><div>BCOR (BCL-6 corepressor) rearranged small round cell sarcoma (BRS) represents an uncommon soft tissue malignancy, frequently characterized by the <em>BCOR</em>::<em>CCNB3</em> fusion. Other noteworthy fusions include <em>BCOR</em>::<em>MAML3</em>, <em>BCOR</em>::<em>CLGN</em>, <em>BCOR</em>::<em>MAML1</em>, <em>ZC3H7B</em>::<em>BCOR</em>, <em>KMT2D</em>::<em>BCOR</em>, <em>CIITA</em>::<em>BCOR</em>, <em>RTL9</em>::<em>BCOR</em>, and <em>AHR</em>::<em>BCOR</em>. The <em>BCOR</em> gene plays a pivotal role in the Polycomb Repressive Complex 1 (PRC1), essential for histone modification and gene silencing. It interfaces with the Polycomb group RING finger homolog (PCGF1). This study employed comprehensive <em>in silico</em> methodologies to investigate the structural and functional effects of <em>BCOR</em> fusion events in BRS. The analysis revealed significant alterations in the domain architecture of BCOR, which resulted in the loss of <em>BCL6</em>-regulated transcriptional repression. Furthermore, IUPred3 prediction indicated a significant increase in disorder in the C-terminal regions of the BCOR in the fusion proteins. A detailed analysis of the physicochemical properties by ProtParam revealed a decrease in isoelectric point, stability, and hydrophobicity. The analysis of protein structures predicted by AlphaFold3 using the PRODIGY algorithm revealed statistically significant deviations in binding affinities for BCOR-PCGF1 dimers and a non-canonical PRC1 variant tetramer compared to the wild-type BCOR. The findings provide a comprehensive summary and elucidation of the fusion proteome associated with BRS, suggesting a substantial impact on the stability and functionality of the fusion proteins, thereby contributing to the oncogenic mechanisms underlying BRS. In this study, we provide the first compilation and comparative analysis of the known BCOR fusions of BRS and introduce a new <em>in silico</em> approach to enhance a better understanding of the molecular basis of BRS.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110144"},"PeriodicalIF":7.0,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}