Advances in biomarker sciences and technology最新文献_第4页

Machine learning and IoT in healthcare: Recent advancements, challenges & future direction 医疗保健中的机器学习和物联网：最新进展、挑战和未来方向

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.08.006

Md Zonayed , Rumana Tasnim , Sayma Sultana Jhara , Mariam Akter Mimona , Molla Rashied Hussein , Md Hosne Mobarak , Umme Salma

Background

The integration of Machine Learning and Deep Learning with IoT-enabled devices for real-time health monitoring has significantly revolutionized healthcare. These technologies facilitate the analysis of intricate medical datasets, yielding actionable insights that promote evidence-based clinical decision-making. Although significant advancements have been made, there is still an absence of a thorough synthesis regarding current applications, primary challenges, and prospective research directions. This review aims to synthesize recent applications, identify significant gaps, and propose clear direction for future research.

Methodology

A comprehensive narrative review was performed where a systematic literature search was conducted in PubMed and Scopus for studies published between 2020 and 2025. A total of 300 pertinent papers on ML and IoT's applications in healthcare were selected and analyzed to synthesize technological advancements, trade-offs, practical implications, challenges, and potential directions for future research.

Key findings

Neural network models, such as CNNs and ANNs, along with ensemble methods like Random Forest and XGBoost, often attain predictive accuracies ranging from 85 % to 95 %. Advanced technique, like generative imaging models, reinforcement learning, and transformer-based architectures, improve diagnostics, chronic disease management, robotic-assisted surgery, and predictive analytics, while explainable AI promotes clinical trust. Cloud-edge integration utilizing lightweight machine learning models enables real-time, energy-efficient applications, enhancing diagnosis, decision support, personalization, and cost-effectiveness, notwithstanding current challenges.

Conclusion

To conclude, the integration of ML and IoT is transforming healthcare through enhanced monitoring, improved predictive capabilities, and tailored treatment approaches. Addressing persistent limitations is crucial for fully realizing its potential and directing future research in this evolving field.

机器学习和深度学习与支持物联网的设备的集成，用于实时健康监测，极大地改变了医疗保健。这些技术有助于分析复杂的医疗数据集，产生可操作的见解，促进循证临床决策。尽管已经取得了重大进展，但仍然缺乏对当前应用，主要挑战和未来研究方向的全面综合。本文综述了该技术的最新应用，指出了存在的不足，并为今后的研究提出了明确的方向。方法在PubMed和Scopus中进行系统文献检索，对2020年至2025年间发表的研究进行全面的叙述性回顾。本文选取并分析了300篇关于机器学习和物联网在医疗保健领域应用的相关论文，以综合技术进步、权衡、实际影响、挑战和未来研究的潜在方向。神经网络模型，如cnn和ann，以及像随机森林和XGBoost这样的集成方法，通常可以达到85%到95%的预测精度。先进的技术，如生成成像模型、强化学习和基于变压器的架构，改善了诊断、慢性疾病管理、机器人辅助手术和预测分析，而可解释的人工智能促进了临床信任。尽管目前面临挑战，但利用轻量级机器学习模型的云边缘集成可以实现实时、节能的应用程序，增强诊断、决策支持、个性化和成本效益。总之，机器学习和物联网的整合通过增强监测、提高预测能力和定制治疗方法正在改变医疗保健。解决持续存在的局限性对于充分发挥其潜力和指导这一不断发展的领域的未来研究至关重要。

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引用次数: 0

Corrigendum regarding “Declaration of Competing Interest statements in previously published articles” 关于“先前发表的文章中的竞争利益声明”的勘误表

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.11.006

引用次数: 0

Bringing lab to the field: Exploring innovations in point-of-care diagnostics for the rapid detection and management of tropical diseases in resource-limited settings 将实验室带到实地：探索在资源有限的环境中快速检测和管理热带病的即时诊断创新

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.01.001

Abdullahi Tunde Aborode , Ridwan Olamilekan Adesola , Godfred Yawson Scott , Emele Arthur-Hayford , Oche Joseph Otorkpa , Somuah Daniel Kwaku , Emmanuel Ebuka Elebesunu , Eghaghe Osadebamwen Nibokun , Ibude Jane Aruorivwooghene , Adetolase A. Bakre , Oluwaseun Adeolu Ogundijo , Olamilekan Gabriel Banwo , Oluwatobiloba Ige , Ibrahim O. Adelakun , Isreal Ayobami Onifade , Segun E. Ogungbemi , Boluwatife T. Dosunmu , Oluwaseunayo Deborah Ayando , Nike Idowu , Grace A. Adegoye , Olusegun Oluwaseun Jimoh

Tropical diseases present major health challenges in regions with limited resources, where access to advanced laboratory facilities is often scarce. This study explores the innovative techniques emerging in point-of-care (POC) diagnostics that are transforming the detection and treatment of tropical diseases. These advancements aim to provide faster diagnosis, better medical care, and ongoing monitoring in areas where traditional diagnostic methods are not practical, combining the precision of laboratory testing with the accessibility of field-based solutions. The review focuses on rapid diagnostic tests, molecular diagnostic tools, and smartphone-based applications, analyzing their advantages, limitations, and potential impact on healthcare delivery. It also addresses the challenges and opportunities involved in deploying these technologies in resource-constrained environments. Key to their success is the need for interdisciplinary collaboration, sustainable funding models, and strong regulatory frameworks to ensure their effective integration into healthcare systems. The review emphasizes how point-of-care diagnostics can play a crucial role in reducing the burden of tropical diseases and advancing health equity on a global scale.

在资源有限的地区，热带病是重大的卫生挑战，在这些地区，获得先进实验室设施的机会往往很少。本研究探讨了正在改变热带病检测和治疗的即时诊断（POC）中出现的创新技术。这些进步的目的是在传统诊断方法不实用的领域提供更快的诊断、更好的医疗护理和持续监测，将实验室检测的精确性与实地解决方案的可及性相结合。这篇综述的重点是快速诊断测试、分子诊断工具和基于智能手机的应用，分析了它们的优势、局限性和对医疗保健服务的潜在影响。它还解决了在资源受限的环境中部署这些技术所涉及的挑战和机遇。它们成功的关键是需要跨学科合作、可持续的筹资模式和强有力的监管框架，以确保它们有效地融入卫生保健系统。该审查强调了即时诊断如何能够在全球范围内减轻热带病负担和促进卫生公平方面发挥关键作用。

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引用次数: 0

Etiological connections between initial COVID-19 and two rare infectious diseases 初始COVID-19与两种罕见传染病之间的病因学联系

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2024.12.001

Zhengjun Zhang

The origin of COVID-19 remains unclear despite extensive research. Theoretical models can simplify complex epigenetic landscapes by reducing vast methylation sites into manageable sets, revealing fundamental pathogen interactions that leap medical advances for the first time in tracing virus origin in the literature and practices. In our study, a max-logistic intelligence classifier analyzed 865,859 Infinium MethylationEPIC sites (CpGs), identifying eight CpGs that achieved 100 % accuracy in distinguishing COVID-19 patients from other respiratory disease patients and healthy controls. One CpG, cg07126281, linked to the SAMM50 gene, shares genetic ties with rare infectious diseases like Sennetsu fever and glanders, suggesting a potential connection between COVID-19 and these diseases, possibly transmitted through contaminated seafood or glanders-infected individuals. Identifying such links among 865,859 CpG sites is challenging, with a random correlation probability of less than one in ten million. However, the likelihood of finding meaningful associations with rare diseases lowers this probability to one in one hundred million, reinforcing the credibility of our findings. These results highlight the importance of investigating seafood markets and global supply chains in tracing COVID-19's origins and emphasize the need for ongoing biosafety and biosecurity measures to prevent future outbreaks.

尽管进行了广泛的研究，COVID-19的起源仍不清楚。理论模型可以通过将大量甲基化位点减少到可管理的集合来简化复杂的表观遗传景观，揭示了在文献和实践中首次在追踪病毒起源方面飞跃医学进步的基本病原体相互作用。在我们的研究中，一个最大逻辑智能分类器分析了865,859个Infinium MethylationEPIC位点（CpGs），确定了8个CpGs，在区分COVID-19患者与其他呼吸系统疾病患者和健康对照方面达到100%的准确率。与SAMM50基因相关的CpG cg07126281与Sennetsu热和腺体等罕见传染病具有遗传联系，这表明COVID-19与这些疾病之间存在潜在联系，可能通过受污染的海鲜或腺体感染的个体传播。在865,859个CpG位点中识别这样的链接是具有挑战性的，随机相关概率不到千万分之一。然而，发现与罕见疾病有意义的关联的可能性将这种可能性降低到一亿分之一，从而加强了我们研究结果的可信度。这些结果突出了调查海鲜市场和全球供应链对于追踪COVID-19起源的重要性，并强调需要采取持续的生物安全和生物保障措施，以防止未来爆发疫情。

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引用次数: 0

Biomarkers in immunology: Their impact on immune function and response 免疫学中的生物标志物：它们对免疫功能和应答的影响

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.03.001

Deepika Kaushik , Baojun Xu , Mukul Kumar

The immune system is a complex network of organs, tissues, and cells that plays a critical role in defending the body against life-threatening diseases such as infections, cancer, Alzheimer's, and Crohn's disease. Biomarkers serve as valuable tools for assessing immune responses to these threats and evaluating the efficacy of interventions such as vaccines and immunotherapies. They are particularly useful in monitoring immune function in individuals with autoimmune disorders, where the immune system attacks the body's own tissues, or in immunodeficiencies, where immune responses are inadequate. Biomarkers provide a dynamic and comprehensive means of understanding disease mechanisms through observational and analytical epidemiology, randomized clinical trials, screening, diagnosis, and prognosis. However, despite their potential, the clinical application of biomarkers faces challenges, including sensitivity, reproducibility, and the complexity of multi-biomarker panels. Standardization of analytical techniques remains a critical hurdle, as variability in methodologies can impact the reliability and comparability of biomarker data. Addressing these challenges through improved analytical characterization, validation protocols, and integration of advanced technologies is essential to enhance the clinical utility of biomarkers in immune system assessment and disease management. Moreover, biomarkers offer critical insights into disease progression, from early onset to advanced stages, though their sensitivity and specificity may be influenced by various factors. In this review, we focus on the effect of biomarkers on the immune system.

免疫系统是一个由器官、组织和细胞组成的复杂网络，在保护身体免受感染、癌症、阿尔茨海默病和克罗恩病等威胁生命的疾病的侵害方面起着至关重要的作用。生物标志物是评估对这些威胁的免疫反应和评估疫苗和免疫疗法等干预措施有效性的宝贵工具。它们在监测自身免疫性疾病患者（免疫系统攻击人体自身组织）或免疫缺陷患者（免疫反应不足）的免疫功能方面特别有用。生物标志物通过观察和分析流行病学、随机临床试验、筛查、诊断和预后，为了解疾病机制提供了动态和全面的手段。然而，尽管生物标志物具有潜力，但其临床应用仍面临挑战，包括敏感性、可重复性和多生物标志物面板的复杂性。分析技术的标准化仍然是一个关键障碍，因为方法的可变性会影响生物标志物数据的可靠性和可比性。通过改进分析表征、验证方案和整合先进技术来解决这些挑战，对于提高生物标志物在免疫系统评估和疾病管理中的临床应用至关重要。此外，生物标志物提供了从早期发病到晚期疾病进展的关键见解，尽管它们的敏感性和特异性可能受到各种因素的影响。本文就生物标志物对免疫系统的影响作一综述。

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引用次数: 0

Review on zebra fish as an alternative animal model for neurological studies 斑马鱼作为神经学研究替代动物模型的研究进展

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.08.005

Khode Aniket Prakash , Darshana Sunil Nagmoti , Manali Sanjayswami Borkar , Hiray Kuldeep Pannalal , Nagaraju Bandaru

Zebrafish (Danio rerio) have emerged as a powerful alternative animal model in neurological research due to their unique combination of genetic, anatomical, and physiological characteristics. Their transparent embryonic development, high genetic homology with humans, and well-characterized nervous system make them invaluable for studying neurological diseases and disorders. Zebrafish offer practical advantages such as rapid reproduction, cost-effectiveness, and suitability for high-throughput screening. They have been extensively utilized to investigate neurodevelopmental disorders, neurodegenerative diseases like Parkinson's and Alzheimer's, and epilepsy. Moreover, their amenability to genetic manipulation enables precise modeling of human neurological conditions. Behavioral assays in zebrafish provide insights into cognitive, motor, and emotional functions, which can be quantified to study disease phenotypes and therapeutic interventions. Recent advances in imaging techniques, such as live imaging of neuronal activity using calcium indicators, have further enhanced their utility. This review highlights the advantages of zebrafish as an alternative model system, discusses key findings from zebrafish-based neurological studies, and outlines challenges such as translating findings to mammalian systems. By consolidating current knowledge, this article emphasizes the pivotal role of zebrafish in advancing our understanding of neurological mechanisms and in developing novel treatments for brain disorders.

斑马鱼（Danio rerio）由于其独特的遗传、解剖和生理特征组合而成为神经学研究中强有力的替代动物模型。它们透明的胚胎发育，与人类高度的基因同源性，以及良好的神经系统特征，使它们在研究神经系统疾病和障碍方面具有宝贵的价值。斑马鱼具有繁殖迅速、成本效益高、适合高通量筛选等实际优势。它们已被广泛用于研究神经发育障碍，神经退行性疾病，如帕金森病和阿尔茨海默病，以及癫痫。此外，它们对基因操作的适应性使人类神经系统状况的精确建模成为可能。斑马鱼的行为分析提供了对认知、运动和情绪功能的见解，可以量化研究疾病表型和治疗干预。成像技术的最新进展，如利用钙指示剂对神经元活动进行实时成像，进一步增强了它们的实用性。这篇综述强调了斑马鱼作为替代模型系统的优势，讨论了基于斑马鱼的神经学研究的主要发现，并概述了将发现转化为哺乳动物系统等挑战。通过巩固现有知识，本文强调斑马鱼在促进我们对神经机制的理解和开发新的脑部疾病治疗方法方面的关键作用。

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引用次数: 0

Bridging traditional risk factors and genetic insights: A review on polygenic risk scores in cardiovascular diseases 桥接传统危险因素和遗传见解：心血管疾病多基因风险评分综述

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.09.002

Abhishek Gupta , Komal Shah , Aakansha Shukla

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, with traditional risk models often falling short in predicting individual susceptibility-especially among diverse populations. Recent advances in genomics have led to the development of polygenic risk scores (PRS), which aggregate the effects of multiple single nucleotide polymorphisms (SNPs) to estimate genetic predisposition to CVD. This review explores the scientific evolution, clinical relevance, and limitations of PRS in CVD prediction. Evidence shows that integrating PRS with conventional risk factors significantly improves risk stratification, aiding in early detection and personalized prevention strategies. Notably, ethnicity-specific PRS models are being developed to enhance predictive accuracy for non-European populations, including South Asians. Despite its promise, PRS implementation faces challenges, such as Eurocentric bias in genome-wide association studies (GWAS), limited accessibility in low- and middle-income countries, and ethical concerns regarding equity and data privacy. Future research should emphasize multi-ethnic datasets, integration with clinical and lifestyle data, and development of equitable policies. As PRS continues to be effective in refining cardiovascular risk stratification, its integration into public health frameworks could revolutionize risk assessment and drive the shift toward precision medicine.

心血管疾病（cvd）仍然是全球发病率和死亡率的主要原因，传统的风险模型在预测个体易感性方面往往不足，特别是在不同人群中。基因组学的最新进展导致了多基因风险评分（PRS）的发展，它汇总了多个单核苷酸多态性（snp）的影响，以估计心血管疾病的遗传易感性。本文综述了PRS在CVD预测中的科学进展、临床相关性和局限性。有证据表明，将PRS与常规风险因素相结合可显著改善风险分层，有助于早期发现和个性化预防策略。值得注意的是，正在开发针对特定种族的PRS模型，以提高对包括南亚人在内的非欧洲人口的预测准确性。尽管有希望，PRS的实施面临着挑战，例如全基因组关联研究（GWAS）中的欧洲中心偏见，低收入和中等收入国家的可及性有限，以及关于公平和数据隐私的伦理问题。未来的研究应强调多民族数据集，与临床和生活方式数据的整合，以及制定公平的政策。由于PRS在完善心血管风险分层方面继续有效，将其纳入公共卫生框架可能会彻底改变风险评估并推动向精准医学的转变。

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引用次数: 0

Network-based discovery of autophagy-regulating miRNA signatures in ovarian carcinoma 基于网络的卵巢癌自噬调节miRNA特征的发现

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.10.001

A. Shriraksha, V.R. Devaraj

Ovarian cancer (OC) remains a leading cause of gynaecologic mortality due to lack of reliable early detection biomarkers. Given the emerging role of autophagy in tumor progression, this study explored the contribution of autophagy-associated miRNAs in OC using integrative bioinformatic approaches. Differential expression analysis of the E-TABM-975 dataset (3 normal, 125 tumor samples) identified 69 significantly altered miRNAs. Functional enrichment revealed their targets were involved in key pathways such as PI3K–AKT, MAPK, endocytosis, and autophagy regulation. Network analysis highlighted hub miRNAs (miR-340-5p, miR-106b-5p, miR-144-5p) interacting with autophagy-related genes (PTEN, MAP1B). A Random Forest model trained on E-TABM-975 achieved 99.22 % accuracy, and independent validation using E-TABM-343 (15 normal, 69 tumor) confirmed strong generalization (100 % accuracy). While most miRNAs exhibited consistent expression trends across datasets, a few discordant cases likely reflect dataset-specific variation. Limited availability of large cohorts with matched normal tissues remains a major constraint. The study provides a computational framework for identifying autophagy-related miRNAs with diagnostic relevance and outlines a phased experimental validation strategy to advance these findings toward translational applicability.

由于缺乏可靠的早期检测生物标志物，卵巢癌（OC）仍然是妇科死亡的主要原因。鉴于自噬在肿瘤进展中的新作用，本研究利用综合生物信息学方法探讨了自噬相关mirna在OC中的作用。E-TABM-975数据集（3个正常样本，125个肿瘤样本）的差异表达分析鉴定出69个显著改变的mirna。功能富集表明它们的靶点参与关键通路，如PI3K-AKT、MAPK、内吞作用和自噬调节。网络分析强调枢纽mirna （miR-340-5p, miR-106b-5p, miR-144-5p）与自噬相关基因（PTEN, MAP1B）相互作用。使用E-TABM-975训练的随机森林模型准确率达到99.22%，使用E-TABM-343（15例正常，69例肿瘤）的独立验证证实了强泛化（100%准确率）。虽然大多数mirna在数据集中表现出一致的表达趋势，但少数不一致的情况可能反映了数据集的特定差异。具有匹配正常组织的大队列的有限可用性仍然是一个主要限制。该研究为识别具有诊断相关性的自噬相关mirna提供了一个计算框架，并概述了一个分阶段的实验验证策略，以推进这些发现的翻译适用性。

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引用次数: 0

Identification of bipolar disorder related biomarkers, signaling pathways and potential therapeutic compounds based on bioinformatics methods and molecular docking technology 基于生物信息学方法和分子对接技术的双相情感障碍相关生物标志物、信号通路和潜在治疗化合物的鉴定

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.08.004

Basavaraj Vastrad , Shivaling Pattanashetti , Chanabasayya Vastrad

Bipolar disorder (BD), also known as psychiatric disorder, affects millions of people all over the world. The aim of this investigation was to screen and verify hub genes involved in BD as well as to explore potential molecular mechanisms. The next generation sequencing (NGS) dataset GSE124326 was downloaded from the Gene Expression Omnibus (GEO) database, which contained 480 samples, including 240 BD and 240 normal controls. Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. A Protein-Protein Interaction (PPI) network and modules were constructed and analyzed. We predicted regulatory miRNAs and TFs of hub-genes through miRNet and NetworkAnalyst online database. Drug predicted for BD treatment was screened out from the DrugBank through NetworkAnalyst. Molecular docking studies were carried out for predicting novel drug molecules. Receiver operating characteristic curve (ROC) curves was drawn to elucidate the diagnostic value of hub genes. In this investigation, total of 957 DEGs, including 477 up regulated and 480 down regulated genes. The GO and pathway enrichment analyses of the DEGs showed that the up regulated genes were enriched in the neutrophil degranulation, immune system, transport, cytoplasm and enzyme regulator activity, and the down regulated genes were enriched in extracellular matrix organization, diseases of metabolism, multicellular organismal process, cell periphery and metal ion binding. We screened hub genes include UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11. We also predicted miRNAs, TFs and drugs include hsa-mir-8085, hsa-mir-4514, HMG20B, STAT3, phenserine and roflumilast. Molecular docking technology screened out three small molecule compounds, including Kakkalide, Divaricatol and Brucine small molecule compounds. The current investigation illustrates a characteristic NGS data in BD, which might contribute to the interpretation of the progression of BD and provide novel biomarkers and therapeutic targets for BD.

双相情感障碍（BD），也被称为精神障碍，影响着全世界数百万人。本研究的目的是筛选和验证参与双相障碍的枢纽基因，并探讨其潜在的分子机制。从Gene Expression Omnibus （GEO）数据库下载下一代测序（NGS）数据集GSE124326，该数据集包含480个样本，其中BD 240个，正常对照240个。对差异表达基因（DEGs）进行筛选，并进行基因本体（GO）和途径富集分析。构建并分析了蛋白质-蛋白质相互作用（PPI）网络和模块。我们通过miRNet和NetworkAnalyst在线数据库预测中心基因的调控mirna和TFs。预测治疗双相障碍的药物是通过NetworkAnalyst从DrugBank中筛选出来的。分子对接研究用于预测新药分子。绘制受试者工作特征曲线（ROC），阐明枢纽基因的诊断价值。共检测到957个基因，其中上调基因477个，下调基因480个。GO和途径富集分析显示，上调基因富集于中性粒细胞脱颗粒、免疫系统、运输、细胞质和酶调节活性，下调基因富集于细胞外基质组织、代谢疾病、多细胞有机体过程、细胞外周和金属离子结合。我们筛选的枢纽基因包括UBB、UBE2D1、TUBA1A、RPL11、RPS24、NOTCH3、CAV1、CNBD2、CCNA1和MYH11。我们还预测了mirna、tf和药物包括hsa-mir-8085、hsa-mir-4514、HMG20B、STAT3、phenserine和roflumilast。分子对接技术筛选出Kakkalide、Divaricatol和马钱子碱三种小分子化合物。目前的研究揭示了双相障碍的特征NGS数据，这可能有助于解释双相障碍的进展，并为双相障碍提供新的生物标志物和治疗靶点。

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引用次数: 0

Application of Generative Adversarial Networks on RNASeq data to uncover COVID-19 severity biomarkers 生成对抗网络在RNASeq数据上的应用揭示COVID-19严重程度生物标志物

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.01.002

Yvette K. Kalimumbalo , Rosaline W. Macharia , Peter W. Wagacha

Background

The COVID-19 pandemic has highlighted the need for reliable biomarkers to predict disease severity and guide treatment strategies. However, the analysis of RNASeq data for biomarker discovery using machine learning is constrained by limited sample sizes, primarily due to cost and privacy considerations. In this study, we applied Generative Adversarial Networks (GANs) to RNASeq data in the process of identifying biomarkers associated with COVID-19 severity.

Methods

RNASeq data from COVID-19 patients, along with severity metadata, were collected from the GEO database. Differential expression analysis was conducted and GAN models were trained to augment the original dataset. This enhanced subsequent machine learning models’ robustness and accuracy for biomarker discovery. Feature selection using Recursive Feature Elimination with Cross-Validation (RFECV) identified key biomarkers on cGAN- and cWGAN-augmented datasets.

Results

Several key biomarkers significantly associated with disease severity were identified. Gene Ontology Enrichment analysis revealed upregulation of neutrophil degranulation and downregulation of T-cell activity, consistent with previous findings. The ROC analysis using a Random Forest machine learning model and the five most important biomarkers (CCDC65, ZNF239, OTUD7A, CEP126, and TCTN2) achieved high accuracy (AUC: 0.98, Acc: 0.94) in predicting disease severity. These genes are associated with processes such as cilium assembly, IFN activation, and NF-kB pathway suppression.

Conclusions

Our results demonstrate that GANs can effectively augment RNASeq data, leading to consistent findings that align with known mechanisms and providing new insights into severe COVID-19 transcriptional responses. Further experimental validation is needed to confirm the applicability of these biomarkers in diverse populations.

2019冠状病毒病大流行凸显了需要可靠的生物标志物来预测疾病严重程度和指导治疗策略。然而，使用机器学习对RNASeq数据进行生物标记物发现分析受到样本量有限的限制，主要是由于成本和隐私方面的考虑。在这项研究中，我们将生成对抗网络（GANs）应用于RNASeq数据，以识别与COVID-19严重程度相关的生物标志物。方法从GEO数据库中收集COVID-19患者的srnaseq数据以及严重程度元数据。进行差异表达分析，并训练GAN模型来增强原始数据集。这增强了后续机器学习模型在生物标志物发现方面的鲁棒性和准确性。使用递归特征消除交叉验证（RFECV）进行特征选择，确定了cGAN和cwgan增强数据集上的关键生物标志物。结果确定了几个与疾病严重程度显著相关的关键生物标志物。基因本体富集分析显示中性粒细胞脱颗粒上调和t细胞活性下调，与先前的研究结果一致。使用随机森林机器学习模型和五个最重要的生物标志物（CCDC65、ZNF239、OTUD7A、CEP126和TCTN2）进行ROC分析，在预测疾病严重程度方面取得了很高的准确性（AUC: 0.98, Acc: 0.94）。这些基因与纤毛组装、IFN激活和NF-kB通路抑制等过程有关。研究结果表明，GANs可以有效地增强RNASeq数据，从而获得与已知机制一致的结果，并为严重的COVID-19转录反应提供新的见解。需要进一步的实验验证来确认这些生物标志物在不同人群中的适用性。

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引用次数: 0