Pub Date : 2026-02-01Epub Date: 2026-01-13DOI: 10.1016/j.compbiomed.2025.111429
Atakan Vatansever
Candidatus Neoehrlichia mikurensis, an emerging tick-borne pathogen linked to systemic inflammatory syndromes, poses significant risk to immunocompromised individuals due to its intracellular nature, diagnostic limitations, and lack of targeted vaccines. In this study, immunoinformatics-based methods were applied to design a multi-epitope subunit vaccine targeting surface and conserved immunogenic proteins of N. mikurensis. Virtual screening of 237 proteins identified 377 T-cell and 177 B-cell high-affinity epitopes, prioritized based on antigenicity, non-allergenicity, non-toxicity, and global HLA coverage. T4SS and Pdr-DsbD proteins demonstrated the highest immunological relevance, with T4SS epitopes achieving 100 % global population coverage. Structural modeling revealed stable protein folds, accessible epitopes, and functional ligand-binding pockets, supporting vaccine design reliability. Inclusion of globally effective, high-affinity epitopes is a useful strategy for the creation of subunit vaccines against N. mikurensis. These findings revealed the value of reverse vaccinology and structural bioinformatics for accelerating vaccine development for intracellular bacteria. In conclusion, this in silico approach to vaccine design provides a promising method for guiding subsequent experimental validation and preventive action against neoehrlichiosis.
{"title":"Multi-subunit vaccine design against Neoehrlichia mikurensis by applying structure-based in silico approach","authors":"Atakan Vatansever","doi":"10.1016/j.compbiomed.2025.111429","DOIUrl":"10.1016/j.compbiomed.2025.111429","url":null,"abstract":"<div><div><em>Candidatus Neoehrlichia mikurensis</em>, an emerging tick-borne pathogen linked to systemic inflammatory syndromes, poses significant risk to immunocompromised individuals due to its intracellular nature, diagnostic limitations, and lack of targeted vaccines. In this study, immunoinformatics-based methods were applied to design a multi-epitope subunit vaccine targeting surface and conserved immunogenic proteins of <em>N. mikurensis</em>. Virtual screening of 237 proteins identified 377 T-cell and 177 B-cell high-affinity epitopes, prioritized based on antigenicity, non-allergenicity, non-toxicity, and global HLA coverage. T4SS and Pdr-DsbD proteins demonstrated the highest immunological relevance, with T4SS epitopes achieving 100 % global population coverage. Structural modeling revealed stable protein folds, accessible epitopes, and functional ligand-binding pockets, supporting vaccine design reliability. Inclusion of globally effective, high-affinity epitopes is a useful strategy for the creation of subunit vaccines against <em>N. mikurensis</em>. These findings revealed the value of reverse vaccinology and structural bioinformatics for accelerating vaccine development for intracellular bacteria. In conclusion, this <em>in silico</em> approach to vaccine design provides a promising method for guiding subsequent experimental validation and preventive action against neoehrlichiosis.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111429"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974293","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}
Pub Date : 2026-02-01Epub Date: 2026-01-02DOI: 10.1016/j.compbiomed.2025.111439
Yong Xia , Jingxuan Li , Yeteng Sun , Jiarui Bu , Kuanquan Wang
Large Language Models (LLMs) hold significant promise for electrocardiogram (ECG) analysis, yet challenges remain regarding transferability, time-scale information learning, and interpretability. Current methods suffer from model-specific ECG encoders, hindering transfer across LLMs. Furthermore, LLMs struggle to capture crucial time-scale information inherent in ECGs due to Transformer limitations. And their black-box nature limits clinical adoption. To address these limitations, we introduce ECG-aBcDe, a novel ECG encoding method that transforms ECG signals into a universal ECG language readily interpretable by any LLM. By constructing a hybrid dataset of ECG language and natural language, ECG-aBcDe enables direct fine-tuning of pre-trained LLMs without architectural modifications, achieving "construct once, use anywhere" capability. Moreover, the bidirectional convertibility between ECG and ECG language of ECG-aBcDe allows for extracting attention heatmaps from ECG signals, significantly enhancing interpretability. Finally, ECG-aBcDe explicitly represents time-scale information, mitigating Transformer limitations. This work presents a new paradigm for integrating ECG analysis with LLMs. Compared with existing methods, our approach achieves competitive Rouge-L and Meteor scores and significantly outperforms them on Bleu-4, reaching 42.58 and 30.76, which demonstrates the effectiveness and feasibility of the proposed paradigm. The proposed ECG-aBcDe method enhances the temporal modeling capability and interpretability of LLMs in ECG analysis, providing a robust foundation for future clinical decision support systems.
{"title":"ECG-aBcDe: Overcoming model dependence, encoding ECG into a universal language for any large language model","authors":"Yong Xia , Jingxuan Li , Yeteng Sun , Jiarui Bu , Kuanquan Wang","doi":"10.1016/j.compbiomed.2025.111439","DOIUrl":"10.1016/j.compbiomed.2025.111439","url":null,"abstract":"<div><div>Large Language Models (LLMs) hold significant promise for electrocardiogram (ECG) analysis, yet challenges remain regarding transferability, time-scale information learning, and interpretability. Current methods suffer from model-specific ECG encoders, hindering transfer across LLMs. Furthermore, LLMs struggle to capture crucial time-scale information inherent in ECGs due to Transformer limitations. And their black-box nature limits clinical adoption. To address these limitations, we introduce ECG-aBcDe, a novel ECG encoding method that transforms ECG signals into a universal ECG language readily interpretable by any LLM. By constructing a hybrid dataset of ECG language and natural language, ECG-aBcDe enables direct fine-tuning of pre-trained LLMs without architectural modifications, achieving \"construct once, use anywhere\" capability. Moreover, the bidirectional convertibility between ECG and ECG language of ECG-aBcDe allows for extracting attention heatmaps from ECG signals, significantly enhancing interpretability. Finally, ECG-aBcDe explicitly represents time-scale information, mitigating Transformer limitations. This work presents a new paradigm for integrating ECG analysis with LLMs. Compared with existing methods, our approach achieves competitive Rouge-L and Meteor scores and significantly outperforms them on Bleu-4, reaching 42.58 and 30.76, which demonstrates the effectiveness and feasibility of the proposed paradigm. The proposed ECG-aBcDe method enhances the temporal modeling capability and interpretability of LLMs in ECG analysis, providing a robust foundation for future clinical decision support systems.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111439"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145882873","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}
Pub Date : 2026-02-01Epub Date: 2026-01-06DOI: 10.1016/j.compbiomed.2025.111425
Nazanin Sayad Mojdehbar , Babak Mohammadzadeh Asl , Asghar Zarei
Background and Objective: Dreams can reflect our profound needs and desires, intrinsically linked to emotional processes. In recent years, research on dream emotions has been made possible by capturing electroencephalogram (EEG) signals from the Rapid Eye Movement (REM) sleep stage. This study aims to develop an automated framework for classifying dreams with positive, neutral, and negative emotions using the publicly available Dream Emotion Evaluation Dataset (DEED). Methods: The proposed methodology of this research involves an initial decomposition of the EEG signal into various subbands using the Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD). Subsequently, the single nonlinear feature of Fuzzy Entropy (FuzzEn) is extracted from each subband, followed by the selection of the most discriminative features through the ReliefF algorithm. The resulting feature matrix is then fed into four classifiers: K-Nearest Neighbors (KNN), Support Vector Machine, Extreme Gradient Boosting, and Random Forest for classification. Results: Superior classification performance was achieved using the EMD-FuzzEn method combined with the KNN classifier over the 20s segments. This combination yielded 92.33 0.82 % accuracy for multi-class, 96.47 0.83 % for the neutral versus non-neutral, and 90.69 1.51 % for the positive versus negative dream emotion classification. The results of ReliefF feature selection further highlighted the distinctive importance of temporal and frontal EEG regions, particularly the T7 and T8 channels. Conclusions: Consequently, this methodology demonstrates strong potential for identifying dream emotions, offering insights into signal decomposition and feature extraction efficacy, and representing substantial advancement in classification over prior studies.
{"title":"Automatic EEG-based dream-related emotion recognition using fuzzy entropy and efficient signal decomposition methods","authors":"Nazanin Sayad Mojdehbar , Babak Mohammadzadeh Asl , Asghar Zarei","doi":"10.1016/j.compbiomed.2025.111425","DOIUrl":"10.1016/j.compbiomed.2025.111425","url":null,"abstract":"<div><div><strong>Background and Objective:</strong> Dreams can reflect our profound needs and desires, intrinsically linked to emotional processes. In recent years, research on dream emotions has been made possible by capturing electroencephalogram (EEG) signals from the Rapid Eye Movement (REM) sleep stage. This study aims to develop an automated framework for classifying dreams with positive, neutral, and negative emotions using the publicly available Dream Emotion Evaluation Dataset (DEED). <strong>Methods:</strong> The proposed methodology of this research involves an initial decomposition of the EEG signal into various subbands using the Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD). Subsequently, the single nonlinear feature of Fuzzy Entropy (FuzzEn) is extracted from each subband, followed by the selection of the most discriminative features through the ReliefF algorithm. The resulting feature matrix is then fed into four classifiers: K-Nearest Neighbors (KNN), Support Vector Machine, Extreme Gradient Boosting, and Random Forest for classification. <strong>Results:</strong> Superior classification performance was achieved using the EMD-FuzzEn method combined with the KNN classifier over the 20s segments. This combination yielded 92.33 <span><math><mo>±</mo></math></span> 0.82 % accuracy for multi-class, 96.47 <span><math><mo>±</mo></math></span> 0.83 % for the neutral versus non-neutral, and 90.69 <span><math><mo>±</mo></math></span> 1.51 % for the positive versus negative dream emotion classification. The results of ReliefF feature selection further highlighted the distinctive importance of temporal and frontal EEG regions, particularly the T7 and T8 channels. <strong>Conclusions:</strong> Consequently, this methodology demonstrates strong potential for identifying dream emotions, offering insights into signal decomposition and feature extraction efficacy, and representing substantial advancement in classification over prior studies.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111425"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145916847","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}
Pub Date : 2026-02-01Epub Date: 2026-01-05DOI: 10.1016/j.compbiomed.2025.111418
R. Ritmaleni , K. Kuswandi , M. Ikawati , C.N. Apsari , T.M. Fakih , M. Thamim , K. Thirumoorthy
Breast cancer (BC) remains one of the leading cause of mortality among women worldwide, with no universally effective treatment available despite the development of various therapeutic approaches. This study sought to address this gap by synthesizing potential anticancer agents derived from natural phenolic compounds. These compounds were reacted with 3,4,5-trimethoxybenzoyl chloride to generate novel derivatives, termed natural phenolic-3,4,5-trimethoxybenzoates. To evaluate their therapeutic potential, molecular docking, molecular dynamics simulations, and MM/PBSA free energy binding calculations were performed. Among the synthesized derivatives, sesamol-3,4,5-trimethoxybenzoate, thymol-3,4,5-trimethoxybenzoate, carvacrol-3,4,5-trimethoxybenzoate, and umbelliferone-3,4,5-trimethoxybenzoate demonstrated the most promising binding affinities, with MM/PBSA free energy values of −151.377 kJ/mol, −137.344 kJ/mol, −136.645 kJ/mol, and −131.628 kJ/mol, respectively. These results indicate strong and specific interactions with cancer cell receptors, suggesting their potential as effective therapeutic agents. Furthermore, molecular dynamics analyses including RMSD, RMSF, SASA, Rg, and RDF confirmed the stability of these compounds, further enhancing their candidacy as viable drug leads. This study underscores the critical role of computational techniques in drug discovery, offering valuable insights into molecular interactions and stability prior to experimental validation. By identifying promising natural compound derivatives, specifically natural phenolic-3,4,5-trimethoxybenzoates, this research establishes a foundation for developing targeted and effective treatments for BC. Overall, these findings highlight the potential of computational approaches in oncology drug development and pave the way for future in vitro and in vivo studies to confirm therapeutic efficacy.
{"title":"Computational investigation of natural phenolic-3,4,5-trimethoxybenzoates as potential anticancer agent targeting estrogen receptor alpha","authors":"R. Ritmaleni , K. Kuswandi , M. Ikawati , C.N. Apsari , T.M. Fakih , M. Thamim , K. Thirumoorthy","doi":"10.1016/j.compbiomed.2025.111418","DOIUrl":"10.1016/j.compbiomed.2025.111418","url":null,"abstract":"<div><div>Breast cancer (BC) remains one of the leading cause of mortality among women worldwide, with no universally effective treatment available despite the development of various therapeutic approaches. This study sought to address this gap by synthesizing potential anticancer agents derived from natural phenolic compounds. These compounds were reacted with 3,4,5-trimethoxybenzoyl chloride to generate novel derivatives, termed natural phenolic-3,4,5-trimethoxybenzoates. To evaluate their therapeutic potential, molecular docking, molecular dynamics simulations, and MM/PBSA free energy binding calculations were performed. Among the synthesized derivatives, sesamol-3,4,5-trimethoxybenzoate, thymol-3,4,5-trimethoxybenzoate, carvacrol-3,4,5-trimethoxybenzoate, and umbelliferone-3,4,5-trimethoxybenzoate demonstrated the most promising binding affinities, with MM/PBSA free energy values of −151.377 kJ/mol, −137.344 kJ/mol, −136.645 kJ/mol, and −131.628 kJ/mol, respectively. These results indicate strong and specific interactions with cancer cell receptors, suggesting their potential as effective therapeutic agents. Furthermore, molecular dynamics analyses including RMSD, RMSF, SASA, Rg, and RDF confirmed the stability of these compounds, further enhancing their candidacy as viable drug leads. This study underscores the critical role of computational techniques in drug discovery, offering valuable insights into molecular interactions and stability prior to experimental validation. By identifying promising natural compound derivatives, specifically natural phenolic-3,4,5-trimethoxybenzoates, this research establishes a foundation for developing targeted and effective treatments for BC. Overall, these findings highlight the potential of computational approaches in oncology drug development and pave the way for future in vitro and in vivo studies to confirm therapeutic efficacy.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111418"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145910791","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}
Pub Date : 2026-02-01Epub Date: 2026-01-14DOI: 10.1016/j.compbiomed.2026.111459
V. Shamala , S. Preethi , V. Hemamalini , S. Asha Devi
Alteration of a nucleotide within a triplet codon results in substitution of a different amino acid in the protein sequence, collectively termed as missense or non-synonymous Single-Nucleotide Polymorphisms (nsSNPs). Cytoplasmic T Lymphocytes Antigen-4 (CTLA-4) gene encodes a transmembrane protein expressed on activated T cells. CTLA-4 receptor acts as an immunoregulatory molecule that prompts immunological self-tolerance by rapidly inhibiting T cell-mediated immune responses, via inactivation and elimination of T cells. Polymorphism within CTLA-4 coding region could efficiently disrupt trans-endocytosis process by decreasing its interaction towards B7 ligands (B7-1: CD80 and B7-2: CD86) molecules expressed on Antigen Presenting Cells (APCs). In the present study, we utilized several computational techniques to predict the highly disease-susceptible nsSNPs that potentially impact on structure and function of CTLA-4 protein. Followed by computational docking and Molecular Dynamics (MD) simulations for CTLA-4/CD80 protein complex were conducted. Our research findings reveal that seventeen nsSNPs were found to be highly pathogenic and structurally destabilizing CTLA-4 protein. Subsequently, an evolutionary ConSurf profile reveals that nine nsSNPs were highly conserved and also affect bio-physicochemical properties, three-dimensional RNA structure, post-translational modification sites, secondary and tertiary structure of CTLA-4 protein. Molecular docking of CTLA-4/CD80 protein complex indicates that rs1553657429-P137L and rs1356678649-N145H nsSNPs have efficiently decreased the binding affinity towards B7-1 protein. The MD simulation also reveal CTLA-4 P137L, located within ligand-binding domain (MYPPPY motif) and N145H at N-glycosylation site, were significantly considered to be high-risk nsSNPs that interfere association with B7-1 protein by decreasing structural stability and flexibility of CTLA-4 protein.
{"title":"Demystifying the implications of disease-susceptible missense SNPs within CTLA-4 ligand binding domain and its interaction towards B7-1 protein complex: Bioinformatics-driven evidence","authors":"V. Shamala , S. Preethi , V. Hemamalini , S. Asha Devi","doi":"10.1016/j.compbiomed.2026.111459","DOIUrl":"10.1016/j.compbiomed.2026.111459","url":null,"abstract":"<div><div>Alteration of a nucleotide within a triplet codon results in substitution of a different amino acid in the protein sequence, collectively termed as missense or non-synonymous Single-Nucleotide Polymorphisms (nsSNPs). Cytoplasmic T Lymphocytes Antigen-4 (CTLA-4) gene encodes a transmembrane protein expressed on activated T cells. CTLA-4 receptor acts as an immunoregulatory molecule that prompts immunological self-tolerance by rapidly inhibiting T cell-mediated immune responses, via inactivation and elimination of T cells. Polymorphism within CTLA-4 coding region could efficiently disrupt trans-endocytosis process by decreasing its interaction towards B7 ligands (B7-1: CD80 and B7-2: CD86) molecules expressed on Antigen Presenting Cells (APCs). In the present study, we utilized several computational techniques to predict the highly disease-susceptible nsSNPs that potentially impact on structure and function of CTLA-4 protein. Followed by computational docking and Molecular Dynamics (MD) simulations for CTLA-4/CD80 protein complex were conducted. Our research findings reveal that seventeen nsSNPs were found to be highly pathogenic and structurally destabilizing CTLA-4 protein. Subsequently, an evolutionary ConSurf profile reveals that nine nsSNPs were highly conserved and also affect bio-physicochemical properties, three-dimensional RNA structure, post-translational modification sites, secondary and tertiary structure of CTLA-4 protein. Molecular docking of CTLA-4/CD80 protein complex indicates that rs1553657429-P137L and rs1356678649-N145H nsSNPs have efficiently decreased the binding affinity towards B7-1 protein. The MD simulation also reveal CTLA-4 P137L, located within ligand-binding domain (MYPPPY motif) and N145H at N-glycosylation site, were significantly considered to be high-risk nsSNPs that interfere association with B7-1 protein by decreasing structural stability and flexibility of CTLA-4 protein.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111459"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974216","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}
Pub Date : 2026-02-01Epub Date: 2026-01-02DOI: 10.1016/j.compbiomed.2025.111437
Sungwook Ryu , Mohammad Hassan Baig , Umesh Panwar , Jae-June Dong , Yun Seong Jo , Chang Joong Kim , Sagar Dattatraya Nale , JiMin Park , JaIn Ha , ByoungGon Moon , Sangyoon Yi
Oncogenic RAS mutations, which are common in human tumors and occur in about 30 % of cancer cases, present significant challenges to effective cancer treatment. Among the KRAS family, the KRAS-G12D mutation is a promising target for treating different types of cancer. Current approaches to inhibit the KRAS-G12D mutation have shown limited success, highlighting the urgent need for innovative therapies. In this study, we employed machine learning, followed by scaffold and core hopping fragmentation, to design, synthesize, and biologically test several 1-oxa-3,7-diazaspirodecane-2-one compounds, ultimately identifying two new KRAS-G12D inhibitors. Multiple in silico evaluations were performed to explore the potential of these inhibitors and to gain a deeper structural understanding of how these compounds bind within the KRAS-G12D active site. Additionally, protein binding assays and other biological tests demonstrated that these compounds exhibit a strong protein binding affinity (Kd of 28.29, 48.17, and 85.17 nM) and high selectivity for KRAS-G12D. Subsequent cellular assays further prioritized HDB-2 and HDB-3 as potent KRAS-G12D inhibitors, each showing nanomolar IC50 values. These results suggest that these compounds could become highly effective and selective anticancer agents for targeting KRAS-G12D-driven tumors.
{"title":"1-oxa-3,7-diazaspiro[4.5]decan-2-one derivatives as potent KRAS-G12D inhibitors: A multidisciplinary approach integrating machine learning, synthesis, and biological evaluation","authors":"Sungwook Ryu , Mohammad Hassan Baig , Umesh Panwar , Jae-June Dong , Yun Seong Jo , Chang Joong Kim , Sagar Dattatraya Nale , JiMin Park , JaIn Ha , ByoungGon Moon , Sangyoon Yi","doi":"10.1016/j.compbiomed.2025.111437","DOIUrl":"10.1016/j.compbiomed.2025.111437","url":null,"abstract":"<div><div>Oncogenic RAS mutations, which are common in human tumors and occur in about 30 % of cancer cases, present significant challenges to effective cancer treatment. Among the KRAS family, the KRAS-G12D mutation is a promising target for treating different types of cancer. Current approaches to inhibit the KRAS-G12D mutation have shown limited success, highlighting the urgent need for innovative therapies. In this study, we employed machine learning, followed by scaffold and core hopping fragmentation, to design, synthesize, and biologically test several 1-oxa-3,7-diazaspirodecane-2-one compounds, ultimately identifying two new KRAS-G12D inhibitors. Multiple <em>in silico</em> evaluations were performed to explore the potential of these inhibitors and to gain a deeper structural understanding of how these compounds bind within the KRAS-G12D active site. Additionally, protein binding assays and other biological tests demonstrated that these compounds exhibit a strong protein binding affinity (Kd of 28.29, 48.17, and 85.17 nM) and high selectivity for KRAS-G12D. Subsequent cellular assays further prioritized HDB-2 and HDB-3 as potent KRAS-G12D inhibitors, each showing nanomolar IC<sub>50</sub> values. These results suggest that these compounds could become highly effective and selective anticancer agents for targeting KRAS-G12D-driven tumors.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111437"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145882972","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}
Pub Date : 2026-02-01Epub Date: 2026-01-05DOI: 10.1016/j.compbiomed.2026.111452
Kirstie Wong Chee Ching , Noor Fatmawati Mokhtar , Gee Jun Tye
{"title":"Erratum to “Identification of significant hub genes and pathways associated with metastatic breast cancer and tolerogenic dendritic cell via bioinformatics analysis” [Comput. Biol. Med. 184, (January 2025), 109396]","authors":"Kirstie Wong Chee Ching , Noor Fatmawati Mokhtar , Gee Jun Tye","doi":"10.1016/j.compbiomed.2026.111452","DOIUrl":"10.1016/j.compbiomed.2026.111452","url":null,"abstract":"","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111452"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145910829","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}
Pub Date : 2026-02-01Epub Date: 2026-01-09DOI: 10.1016/j.compbiomed.2026.111449
Saeed Jahromi , Glykeria Sdoukopoulou , Rupesh Kumar Chikara , Steven M. Stufflebeam , Mark P. Ottensmeyer , Gianluca De Novi , Christos Papadelis
Objective
Assessing the localization accuracy of electric and magnetic source imaging (ESI/MSI) for deep brain sources using a 3D-printed head phantom.
Methods
We developed a realistic pediatric head phantom preserving brain, skull, and scalp properties with implanted sources in clinically relevant deep brain locations. Localization accuracy of ESI/MSI was assessed across varying noise levels using dipole fitting and dynamic statistical parametric mapping (dSPM).
Results
The phantom generated realistic MEG and EEG data resembling actual epilepsy patient recordings. MSI showed superior accuracy to ESI for the deep tangential insular source (dipole: ∼17 vs. ∼33 mm; dSPM: ∼24 vs. ∼32 mm). While ESI-ECD localized some radial sources well (e.g. ∼9 mm for brainstem), its dSPM struggled to localize deep sources (e.g. insula and amygdala). Both modalities found the radial thalamus source most challenging to localize.
Conclusions
MSI outperformed ESI for localizing deep tangential sources; yet, both techniques struggled to localize deep radial sources. For point-like sources, dipole fitting delivered the highest accuracy (∼9 mm, ESI for brainstem), whereas averaged dSPM was superior for sources with distributed-source behavior (∼13 mm, MSI for orbital gyrus).
Significance
3D Printed realistic head phantoms can aid assessing the accuracy of ESI/MSI and selecting appropriate methods for different clinical scenarios.
目的利用3d打印头模评估电、磁源成像（ESI/MSI）对脑深部源的定位精度。方法我们开发了一种真实的儿童头部假体，在临床相关的脑深部位置植入源，保留脑、颅骨和头皮的特性。利用偶极子拟合和动态统计参数映射（dSPM）评估了ESI/MSI在不同噪声水平下的定位精度。结果虚拟体生成的脑电信号和脑电信号与实际癫痫患者的记录相似。对于深切向岛状源，MSI表现出优于ESI的精度（偶极子：~ 17 vs ~ 33 mm； dSPM: ~ 24 vs ~ 32 mm）。虽然ESI-ECD可以很好地定位一些放射源（例如脑干~ 9毫米），但其dSPM难以定位深部源（例如脑岛和杏仁核）。两种方法都发现丘脑径向源最难以定位。结论smsi在深切源定位方面优于ESI；然而，这两种技术都难以定位深径向源。对于点状源，偶极子拟合提供了最高的精度（~ 9 mm，脑干ESI），而对于具有分布源行为的源（~ 13 mm，眶回MSI），平均dSPM优越。意义3d打印的逼真头部模型可以帮助评估ESI/MSI的准确性，并根据不同的临床情况选择合适的方法。
{"title":"3D printed pediatric head phantom for assessing deep epileptic sources localization","authors":"Saeed Jahromi , Glykeria Sdoukopoulou , Rupesh Kumar Chikara , Steven M. Stufflebeam , Mark P. Ottensmeyer , Gianluca De Novi , Christos Papadelis","doi":"10.1016/j.compbiomed.2026.111449","DOIUrl":"10.1016/j.compbiomed.2026.111449","url":null,"abstract":"<div><h3>Objective</h3><div>Assessing the localization accuracy of electric and magnetic source imaging (ESI/MSI) for deep brain sources using a 3D-printed head phantom.</div></div><div><h3>Methods</h3><div>We developed a realistic pediatric head phantom preserving brain, skull, and scalp properties with implanted sources in clinically relevant deep brain locations. Localization accuracy of ESI/MSI was assessed across varying noise levels using dipole fitting and dynamic statistical parametric mapping (dSPM).</div></div><div><h3>Results</h3><div>The phantom generated realistic MEG and EEG data resembling actual epilepsy patient recordings. MSI showed superior accuracy to ESI for the deep tangential insular source (dipole: ∼17 vs. ∼33 mm; dSPM: ∼24 vs. ∼32 mm). While ESI-ECD localized some radial sources well (e.g. ∼9 mm for brainstem), its dSPM struggled to localize deep sources (e.g. insula and amygdala). Both modalities found the radial thalamus source most challenging to localize.</div></div><div><h3>Conclusions</h3><div>MSI outperformed ESI for localizing deep tangential sources; yet, both techniques struggled to localize deep radial sources. For point-like sources, dipole fitting delivered the highest accuracy (∼9 mm, ESI for brainstem), whereas averaged dSPM was superior for sources with distributed-source behavior (∼13 mm, MSI for orbital gyrus).</div></div><div><h3>Significance</h3><div>3D Printed realistic head phantoms can aid assessing the accuracy of ESI/MSI and selecting appropriate methods for different clinical scenarios.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111449"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923033","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}
Pub Date : 2026-02-01Epub Date: 2026-01-09DOI: 10.1016/j.compbiomed.2026.111441
Amabrilha Virgínia Souza Guimarães , Alberto Monteiro dos Santos , Jéssica de Oliveira Araújo , Daniela Melo Resende , Paul Anderson Souza Guimarães , Jerônimo Lameira , Maria Gabriela Reis Carvalho , Jeronimo Conceição Ruiz
Helicobacter pylori is a pathogen known to cause persistent inflammation, leading to a wide spectrum of gastrointestinal diseases and gastric lesions. The chronic nature of this infection critically depends on host immune recognition and antigen presentation mechanisms, particularly those mediated by the major histocompatibility complex (MHC), a cluster of genes encoding cell-surfaces proteins that enable the immune system to detect and respond to foreign antigens. In this study, computational approaches were employed to identify and evaluate potential H. pylori targets for vaccine development, with a particular focus on antigenic and immunogenic epitopes associated with MHC class I and II molecules. We analyzed 12 complete genome strains of H. pylori, associated with different gastrointestinal diseases, encompassing a total of 30,775 predicted proteins. All data and predictions were integrated into a relational database, enabling systematic screening for extracellular or outer membrane epitopes that are both antigenic and safe. This process led to the identification of 27 candidate epitopes. To further characterize their immunological potential, molecular dynamics simulations were employed to evaluate the binding affinity between the predicted epitopes and MHC molecules. Multi-epitope complexes comprising B cells, CD4+ T cells, and CD8+ T cells, were ranked based on their binding affinity values. Among these, HPpep500, HPpep386, and HPpep283 emerged as promising candidates for subsequent in vitro and in vivo validation in the context of vaccine development. Overall, this study provides valuable insights into the rational design of an effective H. pylori vaccine.
{"title":"Unveiling potential Helicobacter pylori vaccine candidates: A comprehensive multi-epitope approach","authors":"Amabrilha Virgínia Souza Guimarães , Alberto Monteiro dos Santos , Jéssica de Oliveira Araújo , Daniela Melo Resende , Paul Anderson Souza Guimarães , Jerônimo Lameira , Maria Gabriela Reis Carvalho , Jeronimo Conceição Ruiz","doi":"10.1016/j.compbiomed.2026.111441","DOIUrl":"10.1016/j.compbiomed.2026.111441","url":null,"abstract":"<div><div><em>Helicobacter pylori</em> is a pathogen known to cause persistent inflammation, leading to a wide spectrum of gastrointestinal diseases and gastric lesions. The chronic nature of this infection critically depends on host immune recognition and antigen presentation mechanisms, particularly those mediated by the major histocompatibility complex (MHC), a cluster of genes encoding cell-surfaces proteins that enable the immune system to detect and respond to foreign antigens. In this study, computational approaches were employed to identify and evaluate potential <em>H. pylori</em> targets for vaccine development, with a particular focus on antigenic and immunogenic epitopes associated with MHC class I and II molecules. We analyzed 12 complete genome strains of <em>H. pylori</em>, associated with different gastrointestinal diseases, encompassing a total of 30,775 predicted proteins. All data and predictions were integrated into a relational database, enabling systematic screening for extracellular or outer membrane epitopes that are both antigenic and safe. This process led to the identification of 27 candidate epitopes. To further characterize their immunological potential, molecular dynamics simulations were employed to evaluate the binding affinity between the predicted epitopes and MHC molecules. Multi-epitope complexes comprising B cells, CD4<sup>+</sup> T cells, and CD8<sup>+</sup> T cells, were ranked based on their binding affinity values. Among these, HPpep500, HPpep386, and HPpep283 emerged as promising candidates for subsequent <em>in vitro</em> and <em>in vivo</em> validation in the context of vaccine development. Overall, this study provides valuable insights into the rational design of an effective <em>H. pylori</em> vaccine.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111441"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923038","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}
Pub Date : 2026-02-01Epub Date: 2026-01-11DOI: 10.1016/j.compbiomed.2026.111448
Ahmed A. Emam , Mohamed Y. Foda , Manar Refaat , Salwa M. Abo El-khair , Sara Elfarrash , Omali Y. El-khawaga
Background
Metastasis drives mortality in breast invasive carcinoma. We sought miRNA biomarkers that (i) discriminate metastatic potential, (ii) stratify prognosis, and (iii) translate into a clinically useful PFI predictor.
Methods
We analyzed 858 TCGA-BRCA primary tumors (20 M1, 838 M0). After filtering low-expression miRNAs, DESeq2 identified 10 miRNAs downregulated in M1. Class imbalance was addressed with ADASYN; Random Forest and XGBoost feature importance over 50 iterations converged on four candidates (hsa-miR-150, -5694, −6510, −7156). Ten ML models (single learners and ensembles) were trained with nested tuning and evaluated on balanced test sets and the original cohort. Prognostic value was tested by Kaplan–Meier and Cox regression across OS, DSS, DFI, and PFI. Endpoint-specific Cox β-coefficients yielded miRNA risk scores; a PFI nomogram combined the PFI score with N and M stage. We profiled miR-150, its isoforms (3p/5p), and the three additional candidates in cell lines (MCF-7, MDA-MB-231) and in a 4T1 murine model with histologic confirmation of lung metastasis. Circulating/metastasis-related biomarkers (LDH/PDH ratio, VEGF, Angiopoietin-2, MMP-2) were assayed in serum.
Results
Ensembles showed near-perfect discrimination on balanced data and strong transfer to the original cohort (test AUCs: Bagging ≥0.979, Random Forest 0.981; original-cohort XGBoost 0.973 ± 0.008). High expression of miR-150 and miR-6510 associated with longer OS and DSS; for PFI, miR-150, miR-6510, and miR-5694 were favorable. The three-miRNA PFI score independently predicted progression (multivariable HR = 1.85; 95 % CI: 1.14–3.01) and, integrated with N and M stage, improved 3- and 5-year PFI discrimination (AUC 0.68 and 0.70) with robust calibration. Experimentally, miR-150 (3p/5p) declined in metastatic tissues and blood, while metastatic mice showed elevated LDH/PDH, VEGF, Ang-2, and MMP-2, supporting a mechanistic axis linking miRNA suppression, metabolic rewiring, angiogenesis, and matrix remodeling.
Conclusions
An integrative pipeline identifies a four-miRNA signature associated with lung metastasis and delivers a translational PFI nomogram. Concordant experimental data and serum biomarkers reinforce biological plausibility and clinical potential, including liquid-biopsy applications.
{"title":"Machine learning–guided identification of metastasis-associated miRNAs and their integration into a PFI-based cox risk score and nomogram","authors":"Ahmed A. Emam , Mohamed Y. Foda , Manar Refaat , Salwa M. Abo El-khair , Sara Elfarrash , Omali Y. El-khawaga","doi":"10.1016/j.compbiomed.2026.111448","DOIUrl":"10.1016/j.compbiomed.2026.111448","url":null,"abstract":"<div><h3>Background</h3><div>Metastasis drives mortality in breast invasive carcinoma. We sought miRNA biomarkers that (i) discriminate metastatic potential, (ii) stratify prognosis, and (iii) translate into a clinically useful PFI predictor.</div></div><div><h3>Methods</h3><div>We analyzed 858 TCGA-BRCA primary tumors (20 M1, 838 M0). After filtering low-expression miRNAs, DESeq2 identified 10 miRNAs downregulated in M1. Class imbalance was addressed with ADASYN; Random Forest and XGBoost feature importance over 50 iterations converged on four candidates (hsa-miR-150, -5694, −6510, −7156). Ten ML models (single learners and ensembles) were trained with nested tuning and evaluated on balanced test sets and the original cohort. Prognostic value was tested by Kaplan–Meier and Cox regression across OS, DSS, DFI, and PFI. Endpoint-specific Cox β-coefficients yielded miRNA risk scores; a PFI nomogram combined the PFI score with N and M stage. We profiled miR-150, its isoforms (3p/5p), and the three additional candidates in cell lines (MCF-7, MDA-MB-231) and in a 4T1 murine model with histologic confirmation of lung metastasis. Circulating/metastasis-related biomarkers (LDH/PDH ratio, VEGF, Angiopoietin-2, MMP-2) were assayed in serum.</div></div><div><h3>Results</h3><div>Ensembles showed near-perfect discrimination on balanced data and strong transfer to the original cohort (test AUCs: Bagging ≥0.979, Random Forest 0.981; original-cohort XGBoost 0.973 ± 0.008). High expression of miR-150 and miR-6510 associated with longer OS and DSS; for PFI, miR-150, miR-6510, and miR-5694 were favorable. The three-miRNA PFI score independently predicted progression (multivariable HR = 1.85; 95 % CI: 1.14–3.01) and, integrated with N and M stage, improved 3- and 5-year PFI discrimination (AUC 0.68 and 0.70) with robust calibration. Experimentally, miR-150 (3p/5p) declined in metastatic tissues and blood, while metastatic mice showed elevated LDH/PDH, VEGF, Ang-2, and MMP-2, supporting a mechanistic axis linking miRNA suppression, metabolic rewiring, angiogenesis, and matrix remodeling.</div></div><div><h3>Conclusions</h3><div>An integrative pipeline identifies a four-miRNA signature associated with lung metastasis and delivers a translational PFI nomogram. Concordant experimental data and serum biomarkers reinforce biological plausibility and clinical potential, including liquid-biopsy applications.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111448"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145958667","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}
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