Computer methods and programs in biomedicine最新文献

{"title":"Harnessing angular geometry in deep learning for protein–ligand binding affinity prediction","authors":"Julia Rahman ,&nbsp;M.A. Hakim Newton ,&nbsp;Jiffriya Mohamed Abdul Cader ,&nbsp;Md Khaled Ben Islam ,&nbsp;Mohammed Eunus Ali ,&nbsp;Abdul Sattar","doi":"10.1016/j.cmpb.2026.109282","DOIUrl":"10.1016/j.cmpb.2026.109282","url":null,"abstract":"<div><h3>Background:</h3><div>Protein–ligand binding affinity prediction is essential in structure-based drug design, where binding scores guide the selection of promising candidate ligands. Existing deep learning models often use 3D grids, voxelized complexes, or molecular graphs. These representations are resource-intensive and may not capture specific directional interactions.</div></div><div><h3>Objective:</h3><div>This paper introduces angular geometric features as key descriptors of binding interactions.</div></div><div><h3>Methods:</h3><div>Seven types of dihedral angles between protein and ligand atoms are extracted to encode orientation and geometry. A fully connected ensemble network, called the Angle-Aware Predictor (AAP), integrates these features.</div></div><div><h3>Results:</h3><div>On CASF-2016, AAP achieves state-of-the-art results with correlation coefficient (R) of 0.872, root mean squared error (RMSE) of 1.072, mean absolute error (MAE) 0.817, standard deviation (SD) of 1.077, and concordance index (CI) of 0.845. On four additional benchmarks, AAP shows consistent improvements ranging from 0.3% to 36%.</div></div><div><h3>Conclusion:</h3><div>The angular features are effective, lightweight, and robust descriptors for binding affinity prediction. These results highlight angular geometry as a valuable direction for future structure-based drug discovery. The program and data of AAP are publicly available at <span><span>https://github.com/juliacse06/AAP</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"278 ","pages":"Article 109282"},"PeriodicalIF":4.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186281","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":"Correlative analysis between ocular surface features and carotid plaque : A multimodal machine learning framework","authors":"Shichen Zhang ,&nbsp;Dinghan Hu ,&nbsp;Le Luo ,&nbsp;Jiuwen Cao","doi":"10.1016/j.cmpb.2026.109265","DOIUrl":"10.1016/j.cmpb.2026.109265","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>The diagnosis of carotid plaques plays an important role in revealing cardiovascular and cerebrovascular diseases, thus attracting widespread research attention. However, most medical examinations rely heavily on specialists and carotid ultrasound images, which are time-consuming, radiative, expensive and limited in tracking disease progression. To alleviate these deficiency, inspired by the human blood supply sequence, a detailed study on the association between carotid plaque and ocular surface image features is proposed in the paper.</div></div><div><h3>Methods:</h3><div>This paper systematically verifies the correlation between carotid plaque and ocular surface image through a multi-dimensional feature analysis approach incorporating texture, frequency domain features, and color characteristics. The analysis combines feature selection, confidence evaluation, and distribution property studies to establish robust associations. Besides, multiple machine learning classifiers are used to evaluate the robustness of the extracted features, with subgroup validation conducted across different subsets, systematically assessing the influence of age and gender factors.</div></div><div><h3>Results:</h3><div>The proposed method achieves high prediction accuracy on 8875 individuals from Hangzhou Wuyunshan Hospital (Hangzhou Institute for Health Promotion), with electronic health record (EHR) features showing the strongest association (Odds Ratios [ORs]: 4.35 [3.90-4.86] in males; 2.92 [2.60-3.27] in females). Experimental results demonstrate that age, male gender, and ocular surface image features – including EHR, local binary patterns (LBP), gray-level gradient co-occurrence matrix (GLGCM), and gray-level co-occurrence matrix (GLCM) – show strong associations with carotid plaque, where LBP and EHR features are selected most frequently.</div></div><div><h3>Conclusions:</h3><div>Ocular surface image analysis offers a practical and non-invasive method for carotid plaque screening. The observed feature associations and strong predictive performance highlight its potential for clinical applications, especially in large-scale population screening.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"278 ","pages":"Article 109265"},"PeriodicalIF":4.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077211","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":"Identifying neurotrophic factor related genes at the crosstalk between glioblastoma and ischemic stroke","authors":"Ce Shi ,&nbsp;Lina Ding ,&nbsp;Dandan Wang ,&nbsp;Jing Zhu ,&nbsp;Xiaoli Zheng ,&nbsp;Lansheng Zhang ,&nbsp;Yun Zhou","doi":"10.1016/j.cmpb.2026.109283","DOIUrl":"10.1016/j.cmpb.2026.109283","url":null,"abstract":"<div><h3>Background</h3><div>Glioblastoma multiforme (GBM) and ischemic stroke (IS) are two major neurological disorders contributing substantially to global mortality and disability. GBM elevates IS risk via prothrombotic mechanisms, while IS may accelerate glioma progression through ischemia-driven neuroinflammation. Identifying shared molecular mediators is essential for understanding their bidirectional pathophysiology.</div></div><div><h3>Methods</h3><div>A systems biology approach was implemented to investigate shared neurotrophic factor-related genes (NFRGs) between GBM and IS. A total of 2871 NFRGs were screened from Genecards, with Caspase-3 (CASP3) and Protein Arginine N-Methyltransferase 6 (PRMT6) identified as core regulators. Multi-omics validation included: 1) Differential expression profiling across The Cancer Genome Atlas (TCGA)-GBM and Gene Expression Omnibus (GEO) stroke datasets; 2) Prognostic stratification using Kaplan-Meier (KM) survival curves with log-rank test and Cox proportional hazards regression; 3) Immune microenvironment analysis via CIBERSORT; 4) Experimental validation in middle cerebral artery occlusion (MCAO) mice and GBM cell lines (U87MG, T98G, A172) using Real-Time Quantitative Reverse Transcription PCR (qRT-PCR), Western blot (WB), and immunofluorescence (IF).</div></div><div><h3>Results</h3><div>CASP3 and PRMT6 were significantly upregulated in both GBM and IS (<em>P</em> &lt; 0.05). KM survival analysis with log-rank test showed that high expression of CASP3 and PRMT6 was strongly associated with poorer overall survival (OS) in GBM patients (<em>P</em> &lt; 0.001; Hazard Ratio (HR) = 4.375, 95% Confidence Interval (CI) = 3.336–5.738 for CASP3; HR = 4.547, 95% CI = 3.429–6.029 for PRMT6). Receiver operating characteristic (ROC) analysis confirmed robust diagnostic (Area Under the Curve (AUC) &gt; 0.7) and prognostic efficacy for both markers. IF validated their elevated expression in ischemic brain tissues of Middle Cerebral Artery Occlusion (MCAO) mice, while qRT-PCR and WB confirmed higher expression in GBM cells versus normal glial cells. Immune infiltration analysis indicated that CASP3 and PRMT6 are associated with immunosuppressive remodeling in GBM, suggesting their role as a molecular bridge between the two diseases.</div></div><div><h3>Conclusions</h3><div>Our findings identify CASP3 and PRMT6 as dual molecular mediators coordinating GBM progression and post-IS pathological processes. Targeting these genes may provide novel therapeutic avenues for preventing GBM-associated IS and improving neuro-oncological outcomes.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"278 ","pages":"Article 109283"},"PeriodicalIF":4.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186279","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":"Detecting optimal biomarkers in ovarian cancer cells from high-dimensional mRNA expression data using machine learning","authors":"Rama Krishna Thelagathoti,&nbsp;Chao Jiang,&nbsp;Dinesh S. Chandel,&nbsp;Wesley A. Tom,&nbsp;Cleo Sarmiento,&nbsp;Gary Krzyzanowski,&nbsp;Appolinaire Olou,&nbsp;M. Rohan Fernando","doi":"10.1016/j.cmpb.2026.109263","DOIUrl":"10.1016/j.cmpb.2026.109263","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Reliable detection of robust biomarkers from high-dimensional transcriptomic data remains a major challenge in computational oncology. Traditional approaches often suffer from overfitting and poor generalization due to the high dimensionality of genomic data and limited sample sizes. This study aims to identify an optimal, biologically meaningful subset of mRNA biomarkers capable of distinguishing ovarian cancer samples from healthy controls using an integrated machine learning–based feature selection framework.</div></div><div><h3>Methods</h3><div>We analyzed mRNA expression data encompassing approximately 63,000 transcripts from ovarian cancer and control samples derived from cell lines. A hybrid feature selection pipeline combining statistical filtering, recursive elimination, and regularization was implemented under stratified cross-validation to derive stable biomarkers. Model validation was performed using Logistic Regression, Random Forest, XGBoost, and Support Vector Machine classifiers, while experimental validation was conducted through droplet digital PCR (ddPCR). Statistical analyses included ANOVA, <em>t</em>-tests, and pathway enrichment.</div></div><div><h3>Results</h3><div>The pipeline identified 80 discriminative mRNA biomarkers with exceptionally high classification performance (accuracy = 1.00, sensitivity = 1.00, specificity = 1.00 for top models). ddPCR confirmed consistent expression patterns, with significant downregulation of ADAMTS12, FN1, and ABI3BP and overexpression of EPCAM, COX6C, and TMT1B in ovarian cancer. Pathway enrichment revealed involvement in DNA repair, RNA processing, protein translation, immune regulation, and metabolic reprogramming.</div></div><div><h3>Conclusions</h3><div>This hybrid feature selection framework applied to patient derived cell lines, effectively reduces dimensionality, enhances biomarker reliability, and uncovers biologically interpretable mRNA signatures associated with ovarian cancer, demonstrating potential for diagnostic and therapeutic applications.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"278 ","pages":"Article 109263"},"PeriodicalIF":4.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146124093","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":"Advancing the vision of “reliability metadata”: From conceptual refinement to clinical validation","authors":"Zekai Yu ,&nbsp;Weihao Cheng","doi":"10.1016/j.cmpb.2026.109267","DOIUrl":"10.1016/j.cmpb.2026.109267","url":null,"abstract":"","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"278 ","pages":"Article 109267"},"PeriodicalIF":4.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146156496","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":"Explainable reinforcement learning for glucose monitoring based on shapley value analysis","authors":"Arsene Adjevi ,&nbsp;Abdiwahab Mohamed Abdirashid ,&nbsp;Faruk Aktaş ,&nbsp;Mustafa Hikmet Bilgehan Ucar ,&nbsp;Serdar Solak","doi":"10.1016/j.cmpb.2026.109266","DOIUrl":"10.1016/j.cmpb.2026.109266","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Effective diabetes management requires continuous regulation of blood glucose in response to complex factors such as diet, activity, stress, and medication. Advances in continuous glucose monitoring and machine learning have improved short-term glucose prediction. However, preprocessing of signals like insulin, carbohydrate intake, heart rate, and activity to better capture metabolic dynamics remains underexplored. Similarly, the integration of predictive models with preventive strategies for guiding interventions is still limited.</div></div><div><h3>Methods:</h3><div>We propose a research-only decision-support framework combining signal preprocessing, CNN-based glucose prediction, Shapley Additive Explanations (SHAP) values attribution, and an Actor–Critic Reinforcement Learning (RL) agent. Exponential decay models preprocess inputs, a compact CNN forecasts short-term glucose levels, and SHAP values highlights the most influential input features; however, these attributions reflect associative patterns in the data and do not establish or map to causal clinical mechanisms. These SHAP-derived attributions guide the RL agent, which issues bounded one-step behavioral adjustments. Because SHAP-guided RL remains stochastic and uncertain, the proposed system is exploratory and not clinically safe, serving solely as a simulation framework.</div></div><div><h3>Results:</h3><div>Using the OhioT1DM dataset, the model achieved state-of-the-art RMSE across prediction horizons with a compact size of 7̃4 KB per patient and training under one minute for 1000 epochs. Over 98% of predictions fell within Clarke Error Grid Zones A and B, confirming safe 5–20 min forecasts. The preventive component corrected hyper- and hypoglycemia in 2̃5% of cases within 10 min when predictions were near 80–120 mg/dL (<span><math><mo>±</mo></math></span>10 mg/dL). When deviations exceed <span><math><mo>±</mo></math></span>10 mg/dL, the RL agent is unable to fully restore blood glucose to the target range within 10 min but can bring it as close as possible to the defined interval.</div></div><div><h3>Conclusions:</h3><div>This study presents a significant innovation by bridging predictive accuracy, adaptability, and transparency in diabetes management. The integration of a predictive model with Reinforcement Learning (RL) guided by SHAP values, which are typically used for interpretability but here are employed in the learning process, delivers a powerful decision support framework. This approach advances the field toward next-generation, personalized digital health tools.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"278 ","pages":"Article 109266"},"PeriodicalIF":4.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077212","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 computational approach for classification of HIV drug resistance based on the self-consistent extreme classifier","authors":"L.A. Stolbov ,&nbsp;A.V. Rudik ,&nbsp;E.A. Stolbova ,&nbsp;A.V. Pokrovskaya ,&nbsp;A.B. Shemshura ,&nbsp;D.E. Kireev ,&nbsp;A.A. Lagunin ,&nbsp;D.A. Filimonov ,&nbsp;V.V. Poroikov ,&nbsp;O.A. Tarasova","doi":"10.1016/j.cmpb.2026.109268","DOIUrl":"10.1016/j.cmpb.2026.109268","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>The development of viral resistance can significantly reduce the effectiveness of therapy. Human immunodeficiency virus type 1 is the cause of chronic immune dysfunction, leading to the development of co-infections and serious complications. Despite worldwide progress and consolidated efforts to overcome HIV drug resistance, the development of novel approaches for rational drug therapy of HIV infection is still needed for building models with high accuracy of prediction and that can be applied for evaluation of resistance against wide variety of inhibitors. Our study is dedicated to the development of a novel computational ML-driven approach for the ternary classification of HIV protease, reverse transcriptase, and integrase sequences. Binary classification approaches naturally are not applicable to capture clinically important intermediate resistance levels, motivating the use of a ternary classification model.</div></div><div><h3>Methods</h3><div>For the model development we used the Self-Consistent Extreme Classifier. One-versus-rest and one-versus-one ternary approaches were applied to sequences related resistance data from Stanford University HIV Drug Resistance Database (StDB).</div></div><div><h3>Results</h3><div>For the final classifiers we selected the most appropriate models with 0.913 sensitivity, 0.894 specificity, 0.741 precision and 0.953 area under ROC, all values provided in average. We tested our approach in a clinical task and performed prospective validation for eight sequences of HIV protease and reverse transcriptase obtained from treatment-naive HIV-positive male patients. We performed a prediction and compared the results with the therapeutic outcome, in particular, with the viral load decline at 24 weeks.</div></div><div><h3>Conclusions</h3><div>The results of the prospective validation are generally consistent with the results of the therapeutic outcome and confirm the possibility of using the developed approach for the selection of the most appropriate therapeutic regimens.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"278 ","pages":"Article 109268"},"PeriodicalIF":4.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146164048","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":"Patient-specific fluid-structure interaction modeling of cerebral aneurysm: influence of wall compliance, tissue prestress, and blood rheology","authors":"Amit Raj ,&nbsp;Raghvendra Gupta ,&nbsp;Anugrah Singh","doi":"10.1016/j.cmpb.2026.109284","DOIUrl":"10.1016/j.cmpb.2026.109284","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Cerebral aneurysms are pathological dilations of intracranial arteries that commonly develop at arterial bifurcations. At these locations, hemodynamic forces significantly affect structural properties of the vascular walls leading to focal weakening and vessel remodeling. This study aims to evaluate the influence of wall compliance and tissue prestress on aneurysmal hemodynamics and wall mechanics using a fluid–structure interaction (FSI) framework. The effect of shear thinning of blood is also studied.</div></div><div><h3>Methods</h3><div>The flow of blood and its effect on the vessel walls is modelled in a patient-specific cerebral aneurysm. Physiologically realistic inflow conditions derived from PC-MRI is used as the inlet boundary condition and three-element Windkessel model is used to specify the outlet boundary condition to account for the effect of downstream vasculature. Prestress is applied to the arterial wall to mimic the in-vivo stressed state of the vessel wall. Simulations are performed using the Arbitrary–Lagrangian–Eulerian (ALE) FSI approach under different considerations of wall compliance, blood rheology, and prestress, both individually and in-combination. The computational framework is validated against analytical and numerical solutions available in the literature.</div></div><div><h3>Results</h3><div>Accounting for wall compliance leads to increased inflow into the aneurysm sac and a reduced pressure drop between the inlet and outlet over a cardiac cycle. In the flexible wall model, a single, stable vortex core is observed in the dome instead of the multiple vortices which are observed in case of rigid wall. Further, consideration of flexible walls results in the reduction of peak time-averaged wall shear stress (TAWSS) by ∼20%, reduces the dome area exposed to low TAWSS and regions having high oscillatory shear index (OSI). Including the prestress in model proves critical, as it reduces wall displacement up to 72% and peak tensile stress up to 83% at peak systole. Consideration of shear thinning behaviour of blood further decreases peak TAWSS by up to 25% and reduces area having low TAWSS, but has minimal effect on wall displacement and tensile stress.</div></div><div><h3>Conclusions</h3><div>Wall compliance, blood rheology, and prestress substantially influence aneurysmal hemodynamics and wall mechanics, with prestress having the most dominant effect in reducing wall deformation and stress.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"278 ","pages":"Article 109284"},"PeriodicalIF":4.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186316","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":"HCAR1 antagonist screening based on boundary-selected negative sampling strategy and multi-level graph neural network","authors":"Mengmeng Fan ,&nbsp;Dakuo He ,&nbsp;Qian Liu ,&nbsp;Qing Liu ,&nbsp;Feng Wang ,&nbsp;He Li ,&nbsp;Hao Wang ,&nbsp;Siqi Shan ,&nbsp;Jinghao Zhang ,&nbsp;Yue Hou","doi":"10.1016/j.cmpb.2026.109262","DOIUrl":"10.1016/j.cmpb.2026.109262","url":null,"abstract":"<div><h3>Background and objective:</h3><div>Hydroxycarboxylic acid receptor 1 (HCAR1), also known as the lactate receptor, is closely associated with tumorigenesis and cancer progression due to its aberrant activation, making it an attractive therapeutic target for cancer treatment. Accurate prediction of HCAR1 antagonists is therefore crucial for tumor immunotherapy. However, traditional drug screening suffers from high costs and suboptimal performance caused by imbalanced datasets and incomplete molecular representations, contributing to the scarcity of clinically available HCAR1 antagonists.</div></div><div><h3>Methods:</h3><div>A balanced HCAR1 target activity dataset was constructed using a boundary-selected negative sampling strategy. Subsequently, a multi-level graph neural network (Multi-GNN) was proposed for HCAR1 target activity prediction, integrating multiple molecular representations, including fingerprints, molecular graphs, and fragment-level features.</div></div><div><h3>Results:</h3><div>Experimental results demonstrate that the proposed model outperforms eight state-of-the-art methods in comparative evaluations. Furthermore, approximately ten million compounds were screened using the trained Multi-GNN model in combination with physicochemical filtering and molecular docking, yielding five candidate compounds. Finally, in vitro cAMP antagonistic activity assays identified a promising HCAR1 inhibitor with an <span><math><msub><mrow><mtext>IC</mtext></mrow><mrow><mn>50</mn></mrow></msub></math></span> of 22.39 <span><math><mrow><mi>μ</mi><mi>M</mi></mrow></math></span>.</div></div><div><h3>Conclusions:</h3><div>This study introduces a novel artificial intelligence-based framework for HCAR1-targeted drug discovery and highlights potential lead compounds for further development.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"278 ","pages":"Article 109262"},"PeriodicalIF":4.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146112531","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":"DNA-Driven EEG monitoring for rapid seizure prediction in healthcare","authors":"Khalid Ansari,&nbsp;Unnati Chaurasia,&nbsp;Himanshu Kumar Pathak,&nbsp;Koushlendra Kumar Singh,&nbsp;Jitesh Pradhan","doi":"10.1016/j.cmpb.2026.109277","DOIUrl":"10.1016/j.cmpb.2026.109277","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Worldwide, over 50 million people suffer from epilepsy, a neurological disorder characterised by recurrent seizures due to abnormal electrical activity in the brain. These occur as a result of sudden electric surges and the symptoms vary based on the region of the brain being affected, including brief staring spells and confusion to convulsions and loss of consciousness. Physicians typically classify seizures into four main phases: Interictal, Preictal, Ictal, and Postictal. Accurate analysis of EEG signals around seizure onset is extremely critical for timely clinical intervention. However, the current methodologies majorly utilise complex Convolutional Neural Networks (CNNs) with millions of parameters. They require high computational power, and, hence, it is difficult to deploy them in wearable devices. The core idea of this work is to develop a computationally compact architecture for seizure onset discrimination that offers potential for future integration with wearable devices.</div></div><div><h3>Methods:</h3><div>To achieve this, this work proposes employing a DNA-based encoding framework for Electroencephalogram (EEG) signals. Existing DNA-based compression techniques have demonstrated significant potential in reducing data complexity. Multichannel EEG signals using 23 scalp electrodes are obtained from the CHB-MIT dataset and normalised using min–max scaling. The signals are then windowed to capture temporal dependencies and transformed into integer safe magnitudes before being converted to binary. This approach then involves genetic coding-based preprocessing: genetic transcription and translation (DNA <span><math><mo>→</mo></math></span> RNA <span><math><mo>→</mo></math></span> Codons <span><math><mo>→</mo></math></span> Amino Acids) occur. By converting EEG signal data to amino acid sequences, the proposed encoding scheme aims to capture underlying patterns in the data and provide a compact representation of temporal patterns. The encoded sequences are subsequently processed using a lightweight one-dimensional multi-level parallel CNN architecture.</div></div><div><h3>Results and Conclusion:</h3><div>These DNA-encoded EEG sequences are then used as input to the proposed 1D multi-level parallel CNN model, with drastically fewer parameters. After extensive testing, the proposed model achieves an accuracy of 96.22%. Additionally, the applicability of the proposed encoding framework on early seizure prediction tasks under a subject-wise protocol has been evaluated. An accuracy of 93.87% has been achieved. Overall, these findings indicate that the proposed approach provides a compact and effective representation for EEG-based seizure analysis across related onset and early prediction tasks.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"278 ","pages":"Article 109277"},"PeriodicalIF":4.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146177268","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}