JMIR bioinformatics and biotechnology最新文献

Unpacking Genomic Biomarkers for Programmed Cell Death Receptor-1 Immunotherapy Success in Non-Small Cell Lung Cancer Using Deep Neural Networks: Quantitative Study. 利用深度神经网络对非小细胞肺癌的程序性细胞死亡受体-1免疫治疗成功进行基因组生物标记：定量研究。

JMIR bioinformatics and biotechnology

Pub Date : 2026-01-13 DOI: 10.2196/70553

Rayan Mubarak, Fahim Islam Anik, Jean T Rodriguez, Nazmus Sakib, Mohammad A Rahman

Background: Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer-related mortality. Programmed cell death receptor-1 (PD-1) immunotherapy has shown results in the treatment of NSCLC; however, not all patients respond effectively to it. Identifying predictive biomarkers for PD-1 therapy response is critical to improving patient outcomes and treatment strategies. Traditional methods of biomarker discovery often fall short in terms of accuracy and comprehensiveness. Recent advancements in deep learning provide a powerful approach to analyze complex genomic data to resolve this issue.Objective: This study aims to leverage deep neural networks (DNNs) to identify genomic biomarkers predictive of patient responses to PD-1 immunotherapy in NSCLC. DeepImmunoGene is a model designed using a reduced feature set to identify the most critical biomarkers. We use feature selection to reduce the space and apply deep learning to identify the highly predictive gene subset.Methods: Differentially expressed genes were identified in RNA-seq data from 355 patients with NSCLC using the LIMMA package in R, followed by preprocessing with log2 transformation, removing outliers, and detecting easily identified genes. Machine learning models, including support vector machines, extreme gradient boosting (XGBoost), and DNNs, were applied to gene expression data to predict patient responses to immunotherapy. Key predictive genes were identified through model interpretation techniques, and differences in model performance were assessed for statistical significance. Primarily, the metric used identifies which genes serve as key biomarkers in regard to immunotherapy detection.Results: Initially, we identified 1093 differentially expressed genes from RNA-seq data of 355 patients. We then trained models using SVM, XGBoost, and DNN to predict immunotherapy response. The DNN model outperformed both SVM and XGBoost with an accuracy of 82%, an area under the curve of 90%, and recall of 85%. To identify key biomarkers, we performed a permutation importance analysis, narrowing down the gene set to 98 genes. DeepImmunoGene, trained on these 98 genes, showed superior results, with an accuracy of 87% and an area under the curve of 95%. The top 36 upregulated genes in responders and 62 upregulated genes in nonresponders were identified, which could serve as potential biomarkers for predicting response to PD-1 inhibitors. These findings suggest that DeepImmunoGene can reliably forecast immunotherapy outcomes and aid in biomarker discovery, supporting the development of more personalized treatment strategies in NSCLC.Conclusions: The DeepImmunoGene predictive model identified 36 upregulated genes that may represent candidate genomic biomarkers associated with response to PD-1 immunotherapy in patients with NSCLC. Notably, the 10 most significant genes offer v

背景：非小细胞肺癌（NSCLC）是癌症相关死亡的主要原因之一。程序性细胞死亡受体-1 （PD-1）免疫疗法已显示出治疗非小细胞肺癌的效果；然而，并不是所有的病人都对它有有效的反应。确定PD-1治疗反应的预测性生物标志物对于改善患者预后和治疗策略至关重要。传统的生物标志物发现方法往往在准确性和全面性方面存在不足。深度学习的最新进展为分析复杂的基因组数据提供了一种强大的方法来解决这个问题。目的：本研究旨在利用深度神经网络（dnn）识别预测非小细胞肺癌患者对PD-1免疫治疗反应的基因组生物标志物。DeepImmunoGene是一个使用简化特征集来识别最关键生物标志物的模型。我们使用特征选择来减少空间，并应用深度学习来识别高预测性的基因子集。方法：采用R中的LIMMA软件包，从355例NSCLC患者的RNA-seq数据中鉴定差异表达基因，然后进行log2转化预处理，去除异常值，检测易鉴定基因。机器学习模型，包括支持向量机、极端梯度增强（XGBoost）和dnn，被应用于基因表达数据，以预测患者对免疫治疗的反应。通过模型解释技术确定了关键的预测基因，并评估了模型性能的差异是否具有统计学意义。首先，使用的度量确定哪些基因作为免疫治疗检测的关键生物标志物。结果：最初，我们从355例患者的RNA-seq数据中鉴定出1093个差异表达基因。然后，我们使用SVM、XGBoost和DNN训练模型来预测免疫治疗反应。DNN模型的准确率为82%，曲线下面积为90%，召回率为85%，优于SVM和XGBoost。为了确定关键的生物标志物，我们进行了排列重要性分析，将基因集缩小到98个基因。对这98个基因进行训练的DeepImmunoGene显示出优异的结果，准确率为87%，曲线下面积为95%。在应答者中鉴定出前36个上调基因，在无应答者中鉴定出前62个上调基因，这些基因可以作为预测PD-1抑制剂应答的潜在生物标志物。这些发现表明，DeepImmunoGene可以可靠地预测免疫治疗结果，并有助于生物标志物的发现，支持NSCLC更个性化的治疗策略的发展。结论：DeepImmunoGene预测模型确定了36个上调基因，这些基因可能代表与非小细胞肺癌患者对PD-1免疫治疗反应相关的候选基因组生物标志物。值得注意的是，10个最重要的基因为治疗反应的潜在机制提供了有价值的见解。这些生物标志物可能不仅有助于预测哪些患者更有可能对PD-1免疫治疗产生反应，而且还提供了与无反应相关的分子差异的见解。

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引用次数: 0

Systematic Mining of Bioactive Compounds for Wound Healing From Cayratia Japonica Exosome-Like Nanovesicles: A Workflow Combining LC-MS and DeepSeek Models. 应用LC-MS和DeepSeek模型系统地挖掘日本野参外泌体样纳米囊泡伤口愈合的生物活性化合物

JMIR bioinformatics and biotechnology

Pub Date : 2026-01-08 DOI: 10.2196/80539

Qiang Fu, Wei Ji, Yu-Ping Fan, Jian Yao, Ming-Xia Song, Qiao-Jing Yan

Background: Plant-derived exosome-like nanovesicles (P-ELNs) effectively deliver bioactive compounds due to their high biocompatibility and low immunogenicity. While liquid chromatography-mass spectrometry (LC-MS) profiles compounds in complex samples, its analysis of large datasets remains limited by traditional methods. Recent advances in large language models (LLMs) and domain-specific systems have enhanced Chinese biomedical data processing and cross-modal pharmaceutical research.

Objective: This study aimed to create a multimodal framework of LC-MS combined with DeepSeek models for data mining of compounds with wound-healing properties from exosome-like nanovesicles derived from Cayratia japonica (CJ-ELNs).

Methods: LC-MS identified compounds enriched in CJ (n=3) and CJ-ELNs (n=3), and then compounds specifically enriched in CJ-ELNs were filtered via a four-step filtering workflow. The CJ-ELNs-specific compounds were processed by DeepSeek models for screening naturally active compounds with targeted functions of antioxidation, anti-inflammation, anticellular damage, antiapoptosis, wound healing and tissue regeneration, and cell proliferation.

Results: A multimodal framework of LC-MS combined with the DeepSeek-DF model was created. With the assistance of artificial intelligence (AI), a total of 46 naturally active compounds derived from CJ-ELNs with targeted functions were identified.

Conclusions: A self-designed multimodal framework of LC-MS, combined with DeepSeek models, rapidly and accurately identifies naturally active compounds from CJ-ELNs. This AI-powered system innovatively integrates the traditional analytical technique with modern LLMs, thus greatly favoring data mining of active ingredients in traditional Chinese medicine herbs.

背景：植物源性外泌体样纳米囊泡（P-ELNs）由于其高生物相容性和低免疫原性而有效地递送生物活性化合物。虽然液相色谱-质谱（LC-MS）分析了复杂样品中的化合物，但其对大数据集的分析仍然受到传统方法的限制。大语言模型（llm）和特定领域系统的最新进展促进了中国生物医学数据处理和跨模式药物研究。目的：本研究旨在建立LC-MS结合DeepSeek模型的多模态框架，用于从Cayratia japonica （j - elns）衍生的外泌体样纳米囊泡中提取具有伤口愈合特性的化合物的数据挖掘。方法：LC-MS鉴定富含CJ （n=3）和CJ- elns （n=3）的化合物，然后通过四步过滤流程过滤特异性富集CJ- elns的化合物。通过DeepSeek模型筛选具有抗氧化、抗炎症、抗细胞损伤、抗细胞凋亡、伤口愈合和组织再生、细胞增殖等靶向功能的天然活性化合物。结果：建立了结合DeepSeek-DF模型的LC-MS多模态框架。在人工智能（AI）的辅助下，共鉴定出46个从cj - eln中衍生的具有靶向功能的天然活性化合物。结论：自行设计的LC-MS多模态框架，结合DeepSeek模型，可以快速准确地鉴定出cj - eln中的天然活性化合物。该系统创新地将传统分析技术与现代法学硕士相结合，极大地促进了中药有效成分的数据挖掘。

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引用次数: 0

Development and Validation of a Generative Artificial Intelligence-Based Pipeline for Automated Clinical Data Extraction From Electronic Health Records: Technical Implementation Study. 从电子健康记录中自动提取临床数据的生成式人工智能管道的开发和验证：技术实施研究。

JMIR bioinformatics and biotechnology

Pub Date : 2026-01-06 DOI: 10.2196/70708

Marvin N Carlisle, William A Pace, Andrew W Liu, Robert Krumm, Janet E Cowan, Peter R Carroll, Matthew R Cooperberg, Anobel Y Odisho

Background: The manual abstraction of unstructured clinical data is often necessary for granular clinical outcomes research but is time consuming and can be of variable quality. Large language models (LLMs) show promise in medical data extraction yet integrating them into research workflows remains challenging and poorly described.

Objective: This study aimed to develop and integrate an LLM-based system for automated data extraction from unstructured electronic health record (EHR) text reports within an established clinical outcomes database.

Methods: We implemented a generative artificial intelligence pipeline (UODBLLM) utilizing a flexible language model interface that supports various LLM implementations, including Health Insurance Portability and Accountability Act-compliant cloud services and local open-source models. We used extensible markup language (XML)-structured prompts and integrated using an open database connectivity interface to generate structured data from clinical documentation in the EHR. We evaluated the UODBLLM's performance on the completion rate, processing time, and extraction capabilities across multiple clinical data elements, including quantitative measurements, categorical assessments, and anatomical descriptions, using sample magnetic resonance imaging (MRI) reports as test cases. System reliability was tested across multiple batches to assess scalability and consistency.

Results: Piloted against MRI reports, UODBLLM processed 1800 clinical documents with a 100% completion rate and an average processing time of 8.90 seconds per report. The token utilization averaged 2692 tokens per report, with an input-to-output ratio of approximately 13:2, resulting in a processing cost of US $0.009 per report. UODBLLM had consistent performance across 18 batches of 100 reports each and completed all processing in 4.45 hours. From each report, UODBLLM extracted 16 structured clinical elements, including prostate volume, prostate-specific antigen values, Prostate Imaging Reporting and Data System scores, clinical staging, and anatomical assessments. All extracted data were automatically validated against predefined schemas and stored in standardized JSON format.

Conclusions: We demonstrated the successful integration of an LLM-based extraction system within an existing clinical outcomes database, achieving rapid, comprehensive data extraction at minimal cost. UODBLLM provides a scalable, efficient solution for automating clinical data extraction while maintaining protected health information security. This approach could significantly accelerate research timelines and expand feasible clinical studies, particularly for large-scale database projects.

背景：手工提取非结构化临床数据对于粒度临床结果研究通常是必要的，但耗时且质量参差不齐。大型语言模型（llm）在医疗数据提取中显示出前景，但将它们集成到研究工作流程中仍然具有挑战性，并且描述不足。目的：本研究旨在开发和集成一个基于法学硕士的系统，用于从已建立的临床结果数据库中的非结构化电子健康记录（EHR）文本报告中自动提取数据。方法：我们利用灵活的语言模型接口实现了一个生成式人工智能管道（UODBLLM），该接口支持各种LLM实现，包括符合《健康保险可移植性和责任法案》的云服务和本地开源模型。我们使用可扩展标记语言（XML）结构化提示，并使用开放数据库连接接口集成，从EHR中的临床文档生成结构化数据。我们评估了UODBLLM在多个临床数据元素（包括定量测量、分类评估和解剖描述）的完成率、处理时间和提取能力方面的性能，使用样本磁共振成像（MRI）报告作为测试用例。系统可靠性在多个批次中进行测试，以评估可伸缩性和一致性。结果：以MRI报告为试点，UODBLLM处理1800份临床文件，完成率为100%，平均每份报告处理时间为8.90秒。令牌使用率平均为每个报告2692个令牌，输入输出比约为13:2，导致每个报告的处理成本为0.009美元。UODBLLM在18批（每批100个报告）中具有一致的性能，并在4.45小时内完成了所有处理。从每份报告中，UODBLLM提取了16个结构化的临床要素，包括前列腺体积、前列腺特异性抗原值、前列腺影像学报告和数据系统评分、临床分期和解剖评估。根据预定义的模式自动验证所有提取的数据，并以标准化的JSON格式存储。结论：我们成功地将基于llm的提取系统整合到现有的临床结果数据库中，以最小的成本实现了快速、全面的数据提取。UODBLLM提供了一个可扩展的、高效的解决方案，用于自动化临床数据提取，同时维护受保护的健康信息安全。这种方法可以显著加快研究进度，扩大可行的临床研究，特别是对于大型数据库项目。

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引用次数: 0

Correction: Structural and Functional Impacts of SARS-CoV-2 Spike Protein Mutations: Insights From Predictive Modeling and Analytics. 修正：SARS-CoV-2刺突蛋白突变的结构和功能影响：来自预测建模和分析的见解。

JMIR bioinformatics and biotechnology

Pub Date : 2025-12-29 DOI: 10.2196/89673

Edem K Netsey, Samuel M Naandam, Joseph Asante Jnr, Kuukua E Abraham, Aayire C Yadem, Gabriel Owusu, Jeffrey G Shaffer, Sudesh K Srivastav, Seydou Doumbia, Ellis Owusu-Dabo, Chris E Morkle, Desmond Yemeh, Stephen Manortey, Ernest Yankson, Mamadou Sangare, Samuel Kakraba

引用次数: 0

Structural and Functional Impacts of SARS-CoV-2 Spike Protein Mutations: Insights From Predictive Modeling and Analytics. SARS-CoV-2刺突蛋白突变的结构和功能影响：来自预测建模和分析的见解

JMIR bioinformatics and biotechnology

Pub Date : 2025-12-08 DOI: 10.2196/73637

Edem K Netsey, Samuel M Naandam, Joseph Asante Jnr, Kuukua E Abraham, Aayire C Yadem, Gabriel Owusu, Jeffrey G Shaffer, Sudesh K Srivastav, Seydou Doumbia, Ellis Owusu-Dabo, Chris E Morkle, Desmond Yemeh, Stephen Manortey, Ernest Yankson, Mamadou Sangare, Samuel Kakraba

Background: The COVID-19 pandemic requires a deep understanding of SARS-CoV-2, particularly how mutations in the spike receptor-binding domain (RBD) chain E affect its structure and function. Current methods lack comprehensive analysis of these mutations at different structural levels.

Objective: This study aims to analyze the impact of specific COVID-19-associated point mutations (N501Y, L452R, N440K, K417N, and E484A) on the SARS-CoV-2 spike RBD structure and function using predictive modeling, including a graph-theoretic model, protein modeling techniques, and molecular dynamics simulations.

Methods: The study used a multitiered graph-theoretic framework to represent protein structure across 3 interconnected levels. This model incorporated 19 top-level vertices, connected to intermediate graphs based on 6-angstrom proximity within the protein's 3D structure. Graph-theoretic molecular descriptors or invariants were applied to weigh vertices and edges at all levels. The study also used Iterative Threading Assembly Refinement (I-TASSER) to model mutated sequences and molecular dynamics simulation tools to evaluate changes in protein folding and stability compared to the wildtype.

Results: A total of 3 distinct predictive modeling and analytical approaches successfully identified structural and functional changes in the SARS-CoV-2 spike RBD (chain E) resulting from point mutations. The novel graph-theoretic model detected notable structural changes, with N501Y and L452R showing the most pronounced effects on conformation and stability compared to the wildtype. K147N and E484A mutations demonstrated less significant impacts compared to the severe mutations, N501Y and L452R. Ab initio modeling and molecular simulation dynamics findings corroborated the results from graph-theoretic analysis. The multilevel analytical approach provided a comprehensive visualization of mutation effects, deepening our understanding of their functional consequences.

Conclusions: This study advanced our understanding of SARS-CoV-2 spike RBD mutations and their implications. The multifaceted approach characterized the effects of various mutations, identifying N501Y and L452R as having the most substantial impact on RBD conformation and stability. The findings have important implications for vaccine development, therapeutic design, and variant monitoring. Our research underscores the power of combining multiple predictive analytical approaches in virology, contributing valuable knowledge to ongoing efforts against the COVID-19 pandemic and providing a framework for future studies on viral mutations and their impacts on protein structure and function.

背景：COVID-19大流行需要深入了解SARS-CoV-2，特别是刺突受体结合域（RBD）链E的突变如何影响其结构和功能。目前的方法缺乏对这些突变在不同结构水平上的全面分析。目的：利用预测模型，包括图论模型、蛋白质建模技术和分子动力学模拟，分析covid -19相关特异性点突变（N501Y、L452R、N440K、K417N和E484A）对SARS-CoV-2刺突RBD结构和功能的影响。方法：采用多层图论框架，在3个相互联系的层次上表示蛋白质结构。该模型包含19个顶层顶点，连接到基于蛋白质3D结构中6埃接近度的中间图。应用图论分子描述符或不变量对所有级别的顶点和边进行加权。该研究还使用迭代线程组装改进（I-TASSER）来模拟突变序列和分子动力学模拟工具来评估与野生型相比蛋白质折叠和稳定性的变化。结果：共有3种不同的预测建模和分析方法成功地确定了SARS-CoV-2刺突RBD （E链）由点突变引起的结构和功能变化。新的图理论模型检测到显著的结构变化，与野生型相比，N501Y和L452R对构象和稳定性的影响最为显著。与严重突变N501Y和L452R相比，K147N和E484A突变的影响不显著。从头算模型和分子模拟动力学的发现证实了图论分析的结果。多层分析方法提供了突变效应的全面可视化，加深了我们对其功能后果的理解。结论：本研究提高了我们对SARS-CoV-2刺突RBD突变及其意义的认识。多方面的方法表征了各种突变的影响，确定N501Y和L452R对RBD构象和稳定性的影响最大。这些发现对疫苗开发、治疗设计和变异监测具有重要意义。我们的研究强调了病毒学中多种预测分析方法相结合的力量，为正在进行的对抗COVID-19大流行的工作提供了宝贵的知识，并为未来研究病毒突变及其对蛋白质结构和功能的影响提供了框架。

{"title":"Structural and Functional Impacts of SARS-CoV-2 Spike Protein Mutations: Insights From Predictive Modeling and Analytics.","authors":"Edem K Netsey, Samuel M Naandam, Joseph Asante Jnr, Kuukua E Abraham, Aayire C Yadem, Gabriel Owusu, Jeffrey G Shaffer, Sudesh K Srivastav, Seydou Doumbia, Ellis Owusu-Dabo, Chris E Morkle, Desmond Yemeh, Stephen Manortey, Ernest Yankson, Mamadou Sangare, Samuel Kakraba","doi":"10.2196/73637","DOIUrl":"10.2196/73637","url":null,"abstract":"Background: The COVID-19 pandemic requires a deep understanding of SARS-CoV-2, particularly how mutations in the spike receptor-binding domain (RBD) chain E affect its structure and function. Current methods lack comprehensive analysis of these mutations at different structural levels.Objective: This study aims to analyze the impact of specific COVID-19-associated point mutations (N501Y, L452R, N440K, K417N, and E484A) on the SARS-CoV-2 spike RBD structure and function using predictive modeling, including a graph-theoretic model, protein modeling techniques, and molecular dynamics simulations.Methods: The study used a multitiered graph-theoretic framework to represent protein structure across 3 interconnected levels. This model incorporated 19 top-level vertices, connected to intermediate graphs based on 6-angstrom proximity within the protein's 3D structure. Graph-theoretic molecular descriptors or invariants were applied to weigh vertices and edges at all levels. The study also used Iterative Threading Assembly Refinement (I-TASSER) to model mutated sequences and molecular dynamics simulation tools to evaluate changes in protein folding and stability compared to the wildtype.Results: A total of 3 distinct predictive modeling and analytical approaches successfully identified structural and functional changes in the SARS-CoV-2 spike RBD (chain E) resulting from point mutations. The novel graph-theoretic model detected notable structural changes, with N501Y and L452R showing the most pronounced effects on conformation and stability compared to the wildtype. K147N and E484A mutations demonstrated less significant impacts compared to the severe mutations, N501Y and L452R. Ab initio modeling and molecular simulation dynamics findings corroborated the results from graph-theoretic analysis. The multilevel analytical approach provided a comprehensive visualization of mutation effects, deepening our understanding of their functional consequences.Conclusions: This study advanced our understanding of SARS-CoV-2 spike RBD mutations and their implications. The multifaceted approach characterized the effects of various mutations, identifying N501Y and L452R as having the most substantial impact on RBD conformation and stability. The findings have important implications for vaccine development, therapeutic design, and variant monitoring. Our research underscores the power of combining multiple predictive analytical approaches in virology, contributing valuable knowledge to ongoing efforts against the COVID-19 pandemic and providing a framework for future studies on viral mutations and their impacts on protein structure and function.","PeriodicalId":73552,"journal":{"name":"JMIR bioinformatics and biotechnology","volume":"6 ","pages":"e73637"},"PeriodicalIF":0.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12685290/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145710314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Immunogenicity of Adalimumab in Bacterial Molecular Mimicry: In Silico Analysis. 阿达木单抗在细菌分子模拟中的免疫原性：计算机分析。

JMIR bioinformatics and biotechnology

Pub Date : 2025-12-08 DOI: 10.2196/83872

Diana Isabel Pachón-Suárez, Germán Mejía-Salgado, Oscar Correa, Andrés Sánchez, Marlon Munera, Alejandra de-la-Torre

Background: Adalimumab, a monoclonal antibody targeting tumor necrosis factor α, treats autoimmune diseases but induces antidrug antibodies in 30% to 60% of patients, reducing its efficacy.

Objective: This study aims to investigate molecular mimicry as a mechanism behind this immunogenicity, where bacterial immunoglobulin domains structurally resemble adalimumab's light chain, triggering immune responses.

Methods: Using PSI-BLASTp (National Center for Biotechnology Information) and PRALINE (Center for Integrative Bioinformatics), there are 40 bacterial antigens homologous to adalimumab, with 8 clinically relevant strains.

Results: Structural analysis revealed 94% amino acid identity between the immunoglobulin domain of Escherichia coli strain B1 and adalimumab's light chain, and 89.67% similarity with Corynebacterium pyruviciproducens. Root mean square deviation values confirmed strong structural homology. Additionally, 5 cross-reactive B-cell epitopes were predicted, suggesting overlapping surfaces that may promote immune cross-reactivity and antidrug antibody development.

Conclusions: This study represents a first step toward identifying a potential microbial factor driving antiadalimumab antibody formation. The predicted cross-reactive regions provide specific candidates for further in vitro validation to confirm molecular mimicry and refine epitope mapping. Understanding these mechanisms may ultimately inform the design of less immunogenic biologics and guide clinical strategies to predict and prevent antidrug antibody formation.

背景：阿达木单抗是一种靶向肿瘤坏死因子α的单克隆抗体，可治疗自身免疫性疾病，但在30%至60%的患者中诱发抗药抗体，降低其疗效。目的：本研究旨在研究分子模仿作为这种免疫原性背后的机制，其中细菌免疫球蛋白结构域在结构上类似于阿达木单抗的轻链，从而引发免疫反应。方法：利用PSI-BLASTp（美国国家生物技术信息中心）和PRALINE（美国综合生物信息学中心），获得40种与阿达木单抗同源的细菌抗原，其中8株临床相关菌株。结果：结构分析显示，大肠杆菌B1的免疫球蛋白结构域与阿达木单抗轻链的氨基酸相似性为94%，与产丙酮棒杆菌的相似性为89.67%。均方根偏差值证实了较强的结构同源性。此外，预测了5个交叉反应性b细胞表位，提示重叠表面可能促进免疫交叉反应性和抗药物抗体的产生。结论：这项研究是鉴定驱动抗阿达木单抗抗体形成的潜在微生物因素的第一步。预测的交叉反应区域为进一步的体外验证提供了特定的候选物，以确认分子模仿和完善表位定位。了解这些机制可能最终为设计免疫原性较低的生物制剂提供信息，并指导临床策略来预测和预防抗药抗体的形成。

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引用次数: 0

Protein-Protein Interactions in Papillary and Nonpapillary Urothelial Carcinoma Architectures: Comparative Study. 乳头状和非乳头状尿路上皮癌结构中的蛋白-蛋白相互作用：比较研究。

JMIR bioinformatics and biotechnology

Pub Date : 2025-11-27 DOI: 10.2196/76736

Charissa Chou, Yiğit Baykara, Sean Hacking, Ali Amin, Liang Cheng, Alper Uzun, Ece Dilber Gamsiz Uzun

Background: Bladder cancer is a disease characterized by complex perturbations in gene networks and is heterogeneous in terms of histology, mutations, and prognosis. Advances in high-throughput sequencing technologies, genome-wide association studies, and bioinformatics methods have revealed greater insights into the pathogenesis of complex diseases. Network biology-based approaches have been used to identify complex protein-protein interactions (PPIs) that can lead to potential drug targets. There is a need to better understand PPIs specific to urothelial carcinoma.

Objective: This study aimed to elucidate PPIs specific to papillary and nonpapillary urothelial carcinoma and identify the most connected or "hub" proteins, as these are potential drug targets.

Methods: A novel PPI analysis tool, Proteinarium, was used to analyze RNA sequencing data from 132 patients with papillary and 270 patients with nonpapillary urothelial carcinoma from the TCGA Cell 2017 dataset and 39 patients with papillary and 88 patients with nonpapillary urothelial carcinoma from the TCGA Nature 2014 dataset. Hub proteins were identified in distinct PPI networks specific to papillary and nonpapillary urothelial carcinoma. Statistical significance of clusters was assessed using the Fisher exact test (P<.001), and network separation was quantified using the interactome-based separation score.

Results: RPS27A, UBA52, and VAMP8 were the most connected or "hub" proteins identified in the network specific to the papillary urothelial carcinoma. In the network specific to the nonpapillary carcinoma, GNB1, RHOA, UBC, and FPR2 were found to be the hub proteins. Notably, GNB1 and FPR2 were among the proteins that have existing drugs targeting them.

Conclusions: We identified distinct PPI networks and the hub proteins specific to papillary and nonpapillary urothelial carcinomas. However, these findings are limited by the use of transcriptomic data and require experimental validation to confirm the functional relevance of the identified targets.

背景：膀胱癌是一种以基因网络复杂扰动为特征的疾病，在组织学、突变和预后方面具有异质性。高通量测序技术、全基因组关联研究和生物信息学方法的进步，使人们对复杂疾病的发病机制有了更深入的了解。基于网络生物学的方法已被用于识别可能导致潜在药物靶点的复杂蛋白质-蛋白质相互作用（PPIs）。有必要更好地了解尿路上皮癌特异性PPIs。目的：本研究旨在阐明乳头状和非乳头状尿路上皮癌特异性PPIs，并确定最相关或“枢纽”蛋白，因为这些蛋白是潜在的药物靶点。方法：使用新型PPI分析工具Proteinarium分析来自TCGA Cell 2017数据集的132例乳头状和270例非乳头状尿路上皮癌患者的RNA测序数据，以及来自TCGA Nature 2014数据集的39例乳头状和88例非乳头状尿路上皮癌患者的RNA测序数据。Hub蛋白在乳头状和非乳头状尿路上皮癌特异性的不同PPI网络中被鉴定出来。使用Fisher精确检验评估簇的统计学意义（结果：RPS27A， UBA52和VAMP8是在乳头状尿路上皮癌特异性网络中发现的最紧密连接或“枢纽”蛋白）。在非乳头状癌的特异性网络中，发现GNB1、RHOA、UBC和FPR2是中心蛋白。值得注意的是，GNB1和FPR2是现有药物靶向的蛋白质之一。结论：我们确定了乳头状和非乳头状尿路上皮癌特异性的不同PPI网络和枢纽蛋白。然而，这些发现受到转录组学数据使用的限制，并且需要实验验证来确认所识别的靶标的功能相关性。

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引用次数: 0

Estimating Antigen Test Sensitivity via Target Distribution Balancing: Development and Validation Study. 通过靶分布平衡估计抗原检测敏感性：开发和验证研究。

JMIR bioinformatics and biotechnology

Pub Date : 2025-10-20 DOI: 10.2196/68476

Miguel Bosch, Adriana Moreno, Raul Colmenares, Jose Arocha, Sina Hoche, Auris Garcia, Daniela Hall, Dawlyn Garcia, Lindsey Rudtner, Nol Salcedo, Irene Bosch

Background: Sensitivity-expressed as percent positive agreement (PPA) with a reference assay-is a primary metric for evaluating lateral-flow antigen tests (ATs), typically benchmarked against a quantitative reverse transcription polymerase chain reaction (qRT-PCR). In SARS-CoV-2 diagnostics, ATs detect nucleocapsid protein, whereas qRT-PCR detects viral RNA copy numbers. Since observed PPA depends on the underlying viral load distribution (proxied by the number of cycle thresholds [Cts], which is inversely related to load), study-specific sampling can bias sensitivity estimates. Cohort differences-such as enrichment for high- or low-Ct specimens-therefore complicate cross-test comparisons, and real-world datasets often deviate from regulatory guidance to sample across the full concentration range. Although logistic models relating test positivity to Ct are well described, they are seldom used to reweight results to a standardized reference viral load distribution. As a result, reported sensitivities remain difficult to compare across studies, limiting both accuracy and generalizability.Objective: The aim of this study was to develop and validate a statistical methodology that estimates the sensitivity of ATs by recalibrating clinical performance data-originally obtained from uncontrolled viral load distributions-against a standardized reference distribution of target concentrations, thereby enabling more accurate and comparable assessments of diagnostic test performance.Methods: AT sensitivity is estimated by modeling the PPA as a function of qRT-PCR Ct values (PPA function) using logistic regression on paired test results. Raw sensitivity is the proportion of AT positives among PCR-positive samples. Adjusted sensitivity is calculated by applying the PPA function to a reference Ct distribution, correcting for viral load variability. This enables standardized comparisons across tests. The method was validated using clinical data from a community study in Chelsea, Massachusetts, demonstrating its effectiveness in reducing sampling bias.Results: Over a 2-year period, paired ATs and qRT-PCR-positive samples were collected from 4 suppliers: A (n=211), B (n=156), C (n=85), and D (n=43). Ct value distributions varied substantially, with suppliers A and D showing lower Ct (high viral load) values in the samples, and supplier C skewed toward higher Ct values (low viral load). These differences led to inconsistent raw sensitivity estimates. To correct for this, we used logistic regression to model the PPA as a function of Cts and applied these models to a standardized reference Ct distribution. This adjustment reduced bias and enabled more accurate comparisons of test performance across suppliers.Conclusions: We present a distribution-aware framework that models PPA as a logistic function of Ct and reweights results to a standardized referenc

背景：敏感性-以与参考测定的阳性一致性百分比（PPA）表示-是评估侧流抗原检测（ATs）的主要指标，通常以定量逆转录聚合酶链反应（qRT-PCR）为基准。在SARS-CoV-2诊断中，ATs检测核衣壳蛋白，而qRT-PCR检测病毒RNA拷贝数。由于观察到的PPA取决于潜在的病毒载量分布（由周期阈值[Cts]的数量表示，这与负荷呈负相关），因此研究特定的采样可能会影响灵敏度估计。因此，队列差异（如高或低ct标本的富集）使交叉测试比较复杂化，并且真实世界的数据集经常偏离整个浓度范围内样本的监管指导。尽管将测试阳性与Ct相关的逻辑模型描述得很好，但它们很少用于将结果重新加权为标准化参考病毒载量分布。因此，报告的敏感性仍然难以在研究之间进行比较，限制了准确性和普遍性。目的：本研究的目的是开发和验证一种统计方法，该方法通过重新校准临床表现数据（最初从未控制的病毒载量分布中获得）来估计ATs的敏感性，以对照靶标浓度的标准化参考分布，从而实现更准确和可比较的诊断测试性能评估。方法：通过将PPA建模为qRT-PCR Ct值的函数（PPA函数），对配对测试结果进行逻辑回归，估计AT敏感性。原始灵敏度是指在pcr阳性样品中AT阳性的比例。通过将PPA函数应用于参考Ct分布，校正病毒载量变异性来计算调整后的灵敏度。这使得跨测试的标准化比较成为可能。该方法使用来自马萨诸塞州切尔西社区研究的临床数据进行了验证，证明了其在减少抽样偏差方面的有效性。结果：在2年的时间里，从4个供应商收集了配对的ATs和qrt - pcr阳性样本：a (n=211), B (n=156), C （n=85）和D （n=43）。Ct值分布变化很大，供应商A和D在样品中显示较低的Ct值（高病毒载量），而供应商C倾向于较高的Ct值（低病毒载量）。这些差异导致了不一致的原始灵敏度估计。为了纠正这一点，我们使用逻辑回归将PPA作为Cts的函数建模，并将这些模型应用于标准化参考Ct分布。这种调整减少了偏差，使供应商之间的测试性能比较更加准确。结论：我们提出了一个分布感知框架，该框架将PPA建模为Ct的逻辑函数，并将结果重新加权为标准化参考Ct分布，以产生偏差校正的灵敏度估计。这将在AT供应商和研究之间产生更公平、更一致的比较，加强质量控制，并支持监管审查。总的来说，我们的结果为重新校准报告的敏感性提供了坚实的基础，并强调了分布感知评估在诊断测试评估中的重要性。

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引用次数: 0

Conversational Artificial Intelligence for Integrating Social Determinants, Genomics, and Clinical Data in Precision Medicine: Development and Implementation Study of the AI-HOPE-PM System. 会话人工智能整合社会决定因素，基因组学和临床数据在精准医学：开发和实施研究AI-HOPE-PM系统。

JMIR bioinformatics and biotechnology

Pub Date : 2025-10-10 DOI: 10.2196/76553

Ei-Wen Yang, Brigette Waldrup, Enrique Velazquez-Villarreal

Background: Integrating clinical, genomic, and social determinants of health (SDOH) data is essential for advancing precision medicine and addressing cancer health disparities. However, existing bioinformatics tools often lack the flexibility to perform equity-driven analyses or require significant programming expertise.

Objective: We developed AI-HOPE-PM (Artificial Intelligence Agent for High-Optimization and Precision Medicine in Population Metrics), a conversational artificial intelligence system designed to enable natural language-driven, multidimensional cancer analysis. This study describes the development, implementation, and application of AI-HOPE-PM to support hypothesis testing that integrates genomic, clinical, and SDOH data.

Methods: AI-HOPE-PM leverages large language models and Python-based statistical scripts to convert user-defined natural language queries into executable workflows. It was evaluated using curated colorectal cancer datasets from The Cancer Genome Atlas and cBioPortal, enriched with harmonized SDOH variables. Accuracy of natural language interpretation, run time efficiency, and usability were benchmarked against cBioPortal and UCSC Xena.

Results: AI-HOPE-PM successfully supported case-control stratification, survival modeling, and odds ratio analysis using natural language prompts. In colorectal cancer case studies, the system revealed significant disparities in progression-free survival and treatment access based on financial strain, health care access, food insecurity, and social support, demonstrating the importance of integrating SDOH in cancer research. Benchmark testing showed faster task execution compared to existing platforms, and the system achieved 92.5% accuracy in parsing biomedical queries.

Conclusions: AI-HOPE-PM lowers technical barriers to integrative cancer research by enabling real-time, user-friendly exploration of clinical, genomic, and SDOH data. It expands on prior work by incorporating equity metrics into precision oncology workflows and offers a scalable tool for supporting disparities-focused translational research. Five videos are included as multimedia appendices to demonstrate platform functionality in real-world scenarios.

背景：整合临床、基因组和健康的社会决定因素（SDOH）数据对于推进精准医疗和解决癌症健康差异至关重要。然而，现有的生物信息学工具往往缺乏灵活性来执行权益驱动的分析或需要重要的编程专业知识。目的：我们开发了AI-HOPE-PM（人口计量中高度优化和精准医疗的人工智能代理），这是一个会话人工智能系统，旨在实现自然语言驱动的多维癌症分析。本研究描述了AI-HOPE-PM的开发、实施和应用，以支持整合基因组、临床和SDOH数据的假设检验。方法：AI-HOPE-PM利用大型语言模型和基于python的统计脚本，将用户定义的自然语言查询转换为可执行的工作流。使用来自The cancer Genome Atlas和cbiopportal的结直肠癌数据集进行评估，这些数据集富含协调的SDOH变量。自然语言解释的准确性、运行时效率和可用性以cBioPortal和UCSC Xena为基准。结果：AI-HOPE-PM成功支持病例对照分层、生存建模和使用自然语言提示的优势比分析。在结直肠癌病例研究中，该系统揭示了基于经济压力、医疗保健可及性、食品不安全和社会支持的无进展生存和治疗可及性方面的显著差异，证明了将SDOH纳入癌症研究的重要性。基准测试表明，与现有平台相比，该系统的任务执行速度更快，在解析生物医学查询方面的准确率达到92.5%。结论：AI-HOPE-PM通过实时、用户友好地探索临床、基因组和SDOH数据，降低了癌症综合研究的技术障碍。它通过将公平指标纳入精确肿瘤学工作流程来扩展先前的工作，并为支持以差异为重点的转化研究提供了可扩展的工具。包括五个视频作为多媒体附录，以演示平台在实际场景中的功能。

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引用次数: 0

Paired-Sample and Pathway-Anchored MLOps Framework for Robust Transcriptomic Machine Learning in Small Cohorts: Model Classification Study. 配对样本和途径锚定的MLOps框架在小队列中健壮的转录组机器学习：模型分类研究。

JMIR bioinformatics and biotechnology

Pub Date : 2025-10-08 DOI: 10.2196/80735

Mahdieh Shabanian, Nima Pouladi, Liam Wilson, Mattia Prosperi, Yves A Lussier

Background: Approximately 90% of the 65,000 human diseases are infrequent, collectively affecting ~400 million people, substantially limiting cohort accrual. This low prevalence constrains the development of robust transcriptome-based machine learning (ML) classifiers. Standard data-driven classifiers typically require cohorts of more than 100 participants per group to achieve clinical accuracy while managing high-dimensional input (~25,000 transcripts). These requirements are infeasible for microcohorts of ~20 individuals, where overfitting becomes pervasive.Objective: To overcome these constraints, we developed a classification method that integrates three enabling strategies: (i) paired-sample transcriptome dynamics, (ii) N-of-1 pathway-based analytics, and (iii) reproducible machine learning operations (MLOps) for continuous model refinement.Methods: Unlike ML approaches relying on a single transcriptome per subject, within-subject paired-sample designs-such as pre- versus post-treatment or diseased versus adjacent-normal tissue-effectively control intraindividual variability under isogenic conditions and within-subject environmental exposures (eg, smoking history, other medications, etc), improve signal-to-noise ratios, and, when pre-processed as single- studies (N-of-1), can achieve statistical power comparable with that obtained in animal models. Pathway-level N-of-1 analytics further reduces each sample's high-dimensional profile into ~4000 biologically interpretable features, annotated with effect sizes, dispersion, and significance. Complementary MLOp practices-automated versioning, continuous monitoring, and adaptive hyperparameter tuning-improve model reproducibility and generalization.Results: In two case studies of distinct diseases, human rhinovirus infection (HRV) versus matched healthy controls (n=16 training; n=3 test) and breast cancer tissues harboring TP53 or PIK3CA mutations versus adjacent normal tissue (n=27 training; n=9 test)-this approach achieved 90% precision and recall on an unseen breast cancer test set and 92% precision with 90% recall in rhinovirus fivefold cross-validation. Incorporating paired-sample dynamics boosted precision by up to 12% and recall by 13% in breast cancer and by 5% each in HRV. MLOps workflows yielded an additional ~14.5% accuracy improvement compared to traditional pipelines. Moreover, our method identified 42 critical gene sets (pathways) for rhinovirus response and 21 for breast cancer mutation status, selected as the most important features (mean decrease impurity) of the best-performing model, with retroactive ablation of top 20 features reducing accuracy by ~25%.Conclusions: These proof-of-concept results support the utility of integrating intrasubject dynamics, "biological knowledge"-based feature reduction (pathway-level feature reduction grounded in prior biological know

背景：在65,000种人类疾病中，约90%是不常见的，总共影响约4亿人，大大限制了队列累积。这种低患病率限制了健壮的基于转录组的机器学习（ML）分类器的发展。标准的数据驱动分类器通常需要每组超过100名参与者的队列来实现临床准确性，同时管理高维输入（~25,000个转录本）。这些要求对于约20个个体的微队列是不可行的，因为过度拟合变得普遍。为了克服这些限制，我们开发了一种分类方法，该方法集成了三种启用策略：(i)成对样本转录组动力学，（ii）基于N-of-1途径的分析，以及（iii）用于连续模型改进的可重复机器学习操作（MLOps）。方法:与ML方法依赖于每个受试者的单个转录组不同，受试者内配对样本设计-例如治疗前与治疗后或患病组织与邻近正常组织-有效地控制了等基因条件下的个体内部变异性和受试者内环境暴露（例如，吸烟史，其他药物等），提高了信噪比，并且，当预处理为单个研究（N-of-1）时，可以达到与动物模型相当的统计能力。途径水平的N-of-1分析进一步将每个样本的高维轮廓减少到约4000个生物可解释特征，并标注了效应大小、离散度和显著性。互补的MLOp实践——自动版本控制、连续监控和自适应超参数调优——提高了模型的可重复性和泛化性。结果：在两个不同疾病的病例研究中，人类鼻病毒感染（HRV）与匹配的健康对照（n=16训练，n=3测试）以及携带TP53或PIK3CA突变的乳腺癌组织与邻近正常组织（n=27训练，n=9测试），该方法在未见过的乳腺癌检测集上达到90%的准确率和召回率，在鼻病毒五倍交叉验证中达到92%的准确率和90%的召回率。结合配对样本动态，乳腺癌的准确率提高了12%，召回率提高了13%，HRV的召回率分别提高了5%。与传统管道相比，MLOps工作流程的精度提高了约14.5%。此外，我们的方法确定了42个与鼻病毒反应有关的关键基因集（途径）和21个与乳腺癌突变状态有关的关键基因集（途径），作为表现最好的模型的最重要特征（平均减少杂质），对前20个特征的回顾性消融使准确性降低了约25%。结论：这些概念验证结果支持整合主体内动力学、基于“生物学知识”的特征约简（基于先前生物学知识的路径级特征约简，例如，n -of-1通路分析）和可重复的MLOp工作流程的实用价值，可以克服罕见疾病的队列规模限制，为高维转录组分类提供可扩展、可解释的解决方案。未来的工作将把这些进展扩展到各种治疗和小队列设计中。

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引用次数: 0