Methods最新文献

{"title":"Development and validation of a machine learning model for predicting drug-drug interactions with oral diabetes medications","authors":"","doi":"10.1016/j.ymeth.2024.10.012","DOIUrl":"10.1016/j.ymeth.2024.10.012","url":null,"abstract":"<div><div>Diabetes management is often complicated by comorbidities, requiring complex medication regimens that increase the risk of drug-drug interactions (DDIs), potentially compromising treatment outcomes or causing toxicity. Although machine learning (ML) models have made strides in DDI prediction, existing approaches lack specificity for oral diabetes medications and face challenges in interpretability. To address these limitations, we propose a novel ML-based framework utilizing the Simplified Molecular Input Line Entry System (SMILES) to encode structural information of oral diabetes drugs. Using this representation, we developed an XGBoost model, selecting molecular features through LASSO. Our dataset, sourced from DrugBank, included 42 oral diabetes drugs and 1,884 interacting drugs, divided into training, validation, and testing sets. The model identified 606 optimal features, achieving an F1-score of 0.8182. SHAP analysis was employed for feature interpretation, enhancing model transparency and clinical relevance. By predicting adverse DDIs, our model offers a valuable tool for clinical decision-making, aiding safer prescription practices. The 606 critical features provide insights into atomic-level interactions, linking computational predictions with biological experiments. We present a classification model specifically designed for predicting DDIs associated with oral diabetes medications, with an openly accessible web application to support diabetes management in multi-drug regimens and comorbidity settings.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142566761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of novel digital PCR assays for the rapid quantification of Gram-negative bacteria biomarkers using RUCS algorithm","authors":"","doi":"10.1016/j.ymeth.2024.10.011","DOIUrl":"10.1016/j.ymeth.2024.10.011","url":null,"abstract":"<div><div>Rapid and accurate identification of bacterial pathogens is crucial for effective treatment and infection control, particularly in hospital settings. Conventional methods like culture techniques and MALDI-TOF mass spectrometry are often time-consuming and less sensitive. This study addresses the need for faster and more precise diagnostic methods by developing novel digital PCR (dPCR) assays for the rapid quantification of biomarkers from three Gram-negative bacteria: <em>Acinetobacter baumannii</em>, <em>Klebsiella pneumoniae</em>, and <em>Pseudomonas aeruginosa</em>.</div><div>Utilizing publicly available genomes and the <em>rapid identification of PCR primers for unique core sequences</em> or RUCS algorithm, we designed highly specific dPCR assays. These assays were validated using synthetic DNA, bacterial genomic DNA, and DNA extracted from clinical samples. The developed dPCR methods demonstrated wide linearity, a low limit of detection (∼30 copies per reaction), and robust analytical performance with measurement uncertainty below 25 %. The assays showed high repeatability and intermediate precision, with no cross-reactivity observed. Comparison with MALDI-TOF mass spectrometry revealed substantial concordance, highlighting the methods’ suitability for clinical diagnostics.</div><div>This study underscores the potential of dPCR for rapid and precise quantification of Gram-negative bacterial biomarkers. The developed methods offer significant improvements over existing techniques, providing faster, more accurate, and SI-traceable measurements. These advancements could enhance clinical diagnostics and infection control practices.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142556807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imaging flow cytometry reveals LPS-induced changes to intracellular intensity and distribution of α-synuclein in a TLR4-dependent manner in STC-1 cells.","authors":"Anastazja M Gorecki, Chidozie C Anyaegbu, Melinda Fitzgerald, Kathryn A Fuller, Ryan S Anderton","doi":"10.1016/j.ymeth.2024.10.009","DOIUrl":"https://doi.org/10.1016/j.ymeth.2024.10.009","url":null,"abstract":"<p><strong>Background: </strong>Parkinson's disease is a chronic neurodegenerative disorder, where pathological protein aggregates largely composed of phosphorylated α-synuclein are implicated in disease pathogenesis and progression. Emerging evidence suggests that the interaction between pro-inflammatory microbial factors and the gut epithelium contributes to α-synuclein aggregation in the enteric nervous system. However, the cellular sources and mechanisms for α-synuclein pathology in the gut are still unclear.</p><p><strong>Methods: </strong>The STC-1 cell line, which models an enteroendocrine population capable of communicating with the microbiota, immune and nervous systems, was treated with a TLR4 inhibitor (TAK-242) prior to microbial lipopolysaccharide (LPS) exposure to investigate the role of TLR4 signalling in α-synuclein alterations. Antibodies targeting the full-length protein (α-synuclein) and the Serine-129 phosphorylated form (pS129) were used. Complex, multi-parametric image analysis was conducted through confocal microscopy (with Zen 3.8 analysis) and imaging flow cytometry (with IDEAS® analysis).</p><p><strong>Results: </strong>Confocal microscopy revealed heterogenous distribution of α-synuclein and pS129 in STC-1 cells, with prominent pS129 staining along cytoplasmic processes. Imaging flow cytometry further quantified the relationship between various α-synuclein morphometric features. Thereafter, imaging flow cytometry demonstrated a dose-specific effect of LPS, where the low (8 μg/mL), but not high dose (32 μg/mL), significantly altered measures related to α-synuclein intensity, distribution, and localisation. Pre-treatment with a TLR4 inhibitor TAK-242 alleviated some of these significant alterations.</p><p><strong>Conclusion: </strong>This study demonstrates that LPS-TLR4 signalling alters the intracellular localisation of α-synuclein in enteroendocrine cells in vitro and showcases the utility of combining imaging flow cytometry to investigate subtle protein changes that may not be apparent through confocal microscopy alone. Further investigation is required to understand the apparent dose-dependent effects of LPS on α-synuclein in the gut epithelium in healthy states as well as conditions such as Parkinson's disease.</p>","PeriodicalId":390,"journal":{"name":"Methods","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142563953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MLFA-UNet: A multi-level feature assembly UNet for medical image segmentation","authors":"","doi":"10.1016/j.ymeth.2024.10.010","DOIUrl":"10.1016/j.ymeth.2024.10.010","url":null,"abstract":"<div><div>Medical image segmentation is crucial for accurate diagnosis and treatment in medical image analysis. Among the various methods employed, fully convolutional networks (FCNs) have emerged as a prominent approach for segmenting medical images. Notably, the U-Net architecture and its variants have gained widespread adoption in this domain. This paper introduces MLFA-UNet, an innovative architectural framework aimed at advancing medical image segmentation. MLFA-UNet adopts a U-shaped architecture and integrates two pivotal modules: multi-level feature assembly (MLFA) and multi-scale information attention (MSIA), complemented by a pixel-vanishing (PV) attention mechanism. These modules synergistically contribute to the segmentation process enhancement, fostering both robustness and segmentation precision. MLFA operates within both the network encoder and decoder, facilitating the extraction of local information crucial for accurately segmenting lesions. Furthermore, the bottleneck MSIA module serves to replace stacking modules, thereby expanding the receptive field and augmenting feature diversity, fortified by the PV attention mechanism. These integrated mechanisms work together to boost segmentation performance by effectively capturing both detailed local features and a broader range of contextual information, enhancing both accuracy and resilience in identifying lesions. To assess the versatility of the network, we conducted evaluations of MFLA-UNet across a range of medical image segmentation datasets, encompassing diverse imaging modalities such as wireless capsule endoscopy (WCE), colonoscopy, and dermoscopic images. Our results consistently demonstrate that MFLA-UNet outperforms state-of-the-art algorithms, achieving dice coefficients of 91.42%, 82.43%, 90.8%, and 88.68% for the MICCAI 2017 (Red Lesion), ISIC 2017, PH2, and CVC-ClinicalDB datasets, respectively.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142556808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Arabidopsis thaliana ubiquitination site prediction through knowledge distillation and natural language processing","authors":"","doi":"10.1016/j.ymeth.2024.10.006","DOIUrl":"10.1016/j.ymeth.2024.10.006","url":null,"abstract":"<div><div>Protein ubiquitination is a critical post-translational modification (PTM) involved in diverse biological processes and plays a pivotal role in regulating physiological mechanisms and disease states. Despite various efforts to develop ubiquitination site prediction tools across species, these tools mainly rely on predefined sequence features and machine learning algorithms, with species-specific variations in ubiquitination patterns remaining poorly understood. This study introduces a novel approach for predicting <em>Arabidopsis thaliana</em> ubiquitination sites using a neural network model based on knowledge distillation and natural language processing (NLP) of protein sequences. Our framework employs a multi-species “Teacher model” to guide a more compact, species-specific “Student model”, with the “Teacher” generating pseudo-labels that enhance the “Student” learning and prediction robustness. Cross-validation results demonstrate that our model achieves superior performance, with an accuracy of 86.3 % and an area under the curve (AUC) of 0.926, while independent testing confirmed these results with an accuracy of 86.3 % and an AUC of 0.923. Comparative analysis with established predictors further highlights the model’s superiority, emphasizing the effectiveness of integrating knowledge distillation and NLP in ubiquitination prediction tasks. This study presents a promising and efficient approach for ubiquitination site prediction, offering valuable insights for researchers in related fields. The code and resources are available on GitHub: <span><span>https://github.com/nuinvtnu/KD_ArapUbi</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142492289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a new and rapid molecular diagnostic tool based on RT-LAMP for Hepatitis C virus detection at point-of-care","authors":"","doi":"10.1016/j.ymeth.2024.10.008","DOIUrl":"10.1016/j.ymeth.2024.10.008","url":null,"abstract":"<div><h3>Purpose</h3><div>Globally, it is estimated that 1.0 million individuals are newly infected by Hepatitis C virus (HCV) every year, and nearly 50 million people live with a chronic infection, according to World Health Organization. To overcome underdiagnosis of HCV infection among hard-to-reach populations, it is essential to develop new rapid and easy-to-use molecular diagnostic systems. In this work, we have developed a pangenotypic diagnostic tool based on Loop-Mediated Isothermal Amplification (LAMP), coupled to a direct sample lysis procedure for molecular detection of HCV at point-of-care (POC).</div></div><div><h3>Methods</h3><div>Procedure validation was performed using 129 different samples from HCV infected patients (116 serum samples, and 13 fresh blood samples), 27 individuals who tested negative for HCV but positive for HIV, and 11 healthy donors. Serum was collected, lysed for 10 min at room temperature, and assayed by RT-LAMP. To achieve this, a set of 9 LAMP-primers was used for the first time. Parallel RT-qPCR assays were conducted for HCV to both validate the procedure and quantify viral loads.</div></div><div><h3>Results</h3><div>HCV was detected by RT-LAMP in 109/116 HCV positive serum samples, and in 11/13 positive blood samples in less than 40 min. Compared to RT-qPCR results, our RT-LAMP procedure showed a sensitivity of 94 %, 100 % specificity, and a limit of detection of 3.26 log₁₀ IU/mL (10–20 copies per reaction).</div></div><div><h3>Conclusions</h3><div>We have developed an accurate system, more affordable than the current available rapid tests for HCV. Since no prior RNA purification step from capillary blood is required, we strongly recommend our RT-LAMP system as a valuable and rapid tool for the molecular detection of HCV at POC.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142492288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HLA-DR4Pred2: An improved method for predicting HLA-DRB1*04:01 binders","authors":"","doi":"10.1016/j.ymeth.2024.10.007","DOIUrl":"10.1016/j.ymeth.2024.10.007","url":null,"abstract":"<div><div>HLA-DRB1*04:01 is associated with numerous diseases, including sclerosis, arthritis, diabetes, and COVID-19, emphasizing the need to scan for binders in the antigens to develop immunotherapies and vaccines. Current prediction methods are often limited by their reliance on the small datasets. This study presents HLA-DR4Pred2, developed on a large dataset containing 12,676 binders and an equal number of non-binders. It’s an improved version of HLA-DR4Pred, which was trained on a small dataset, containing 576 binders and an equal number of non-binders. All models were trained, optimized, and tested on 80 % of the data using five-fold cross-validation and evaluated on the remaining 20 %. A range of machine learning techniques was employed, achieving maximum AUROC of 0.90 and 0.87, using composition and binary profile features, respectively. The performance of the composition-based model increased to 0.93, when combined with BLAST search. Additionally, models developed on the realistic dataset containing 12,676 binders and 86,300 non-binders, achieved a maximum AUROC of 0.99. Our proposed method outperformed existing methods when we compared the performance of our best model to that of existing methods on the independent dataset. Finally, we developed a standalone tool and a webserver for HLADR4Pred2, enabling the prediction, design, and virtual scanning of HLA-DRB1*04:01 binding peptides, and we also released a Python package available on the Python Package Index (<span><span>https://webs.iiitd.edu.in/raghava/hladr4pred2/</span><svg><path></path></svg></span>; <span><span>https://github.com/raghavagps/hladr4pred2</span><svg><path></path></svg></span>; <span><span>https://pypi.org/project/hladr4pred2/</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142455002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A heterogeneous graph transformer framework for accurate cancer driver gene prediction and downstream analysis","authors":"","doi":"10.1016/j.ymeth.2024.09.018","DOIUrl":"10.1016/j.ymeth.2024.09.018","url":null,"abstract":"<div><div>Accurately predicting cancer driver genes remains a formidable challenge amidst the burgeoning volume and intricacy of cancer genomic data. In this investigation, we propose HGTDG, an innovative heterogeneous graph transformer framework tailored for precisely predicting cancer driver genes and exploring downstream tasks. A heterogeneous graph construction module is central to the framework, which assembles a gene-protein heterogeneous network leveraging the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and protein-protein interactions sourced from the STRING (search tool for recurring instances of neighboring genes) database. Moreover, our framework introduces a pioneering heterogeneous graph transformer module, harnessing multi-head attention mechanisms for nuanced node embedding. This transformative module proficiently captures distinct representations for both nodes and edges, thereby enriching the model's predictive capacity. Subsequently, the generated node embeddings are seamlessly integrated into a classification module, facilitating the discrimination between driver and non-driver genes. Our experimental findings evince the superiority of HGTDG over existing methodologies, as evidenced by the enhanced performance metrics, including the area under the receiver operating characteristic curves (AUROC) and the area under the precision-recall curves (AUPRC). Furthermore, the downstream analysis utilizing the newly identified cancer driver genes underscores the efficacy and versatility of our proposed framework.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142455001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-view contrastive clustering for cancer subtyping using fully and weakly paired multi-omics data","authors":"","doi":"10.1016/j.ymeth.2024.09.016","DOIUrl":"10.1016/j.ymeth.2024.09.016","url":null,"abstract":"<div><div>The identification of cancer subtypes is crucial for advancing precision medicine, as it facilitates the development of more effective and personalized treatment and prevention strategies. With the development of high-throughput sequencing technologies, researchers now have access to a wealth of multi-omics data from cancer patients, making computational cancer subtyping increasingly feasible. One of the main challenges in integrating multi-omics data is handling missing data, since not all biomolecules are consistently measured across all samples. Current computational models based on multi-omics data for cancer subtyping often struggle with the challenge of weakly paired omics data. To address this challenge, we propose a novel unsupervised cancer subtyping model named Subtype-MVCC. This model leverages graph convolutional networks to extract and represent low-dimensional features from each omics data type, using intra-view and inter-view contrastive learning approaches. By incorporating a weighted average fusion strategy to unify the dimension of each sample, Subtype-MVCC effectively handles weakly paired multi-omics datasets. Comprehensive evaluations on established benchmark datasets demonstrate that Subtype-MVCC outperforms nine leading models in this domain. Additionally, simulations with varying levels of missing data highlight the model's robust performance in handling weakly paired omics data. The clinical relevance and survival outcomes associated with the identified subtypes further validate the interpretability and reliability of the clustering results produced by Subtype-MVCC.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142455004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DGSIST: Clustering spatial transcriptome data based on deep graph structure Infomax","authors":"","doi":"10.1016/j.ymeth.2024.10.002","DOIUrl":"10.1016/j.ymeth.2024.10.002","url":null,"abstract":"<div><div>Although spatial transcriptomics data provide valuable insights into gene expression profiles and the spatial structure of tissues, most studies rely solely on gene expression information, underutilizing the spatial data. To fully leverage the potential of spatial transcriptomics and graph neural networks, the DGSI (Deep Graph Structure Infomax) model is proposed. This innovative graph data processing model uses graph convolutional neural networks and employs an unsupervised learning approach. It maximizes the mutual information between graph-level and node-level representations, emphasizing flexible sampling and aggregation of nodes and their neighbors. This effectively captures and incorporates local information from nodes into the overall graph structure. Additionally, this paper developed the DGSIST framework, an unsupervised cell clustering method that integrates the DGSI model, SVD dimensionality reduction algorithm, and k-means++ clustering algorithm. This aims to identify cell types accurately. DGSIST fully uses spatial transcriptomics data and outperforms existing methods in accuracy. Demonstrations of DGSIST’s capability across various tissue types and technological platforms have shown its effectiveness in accurately identifying spatial domains in multiple tissue sections. Compared to other spatial clustering methods, DGSIST excels in cell clustering and effectively eliminates batch effects without needing batch correction. DGSIST excels in spatial clustering analysis, spatial variation identification, and differential gene expression detection and directly applies to graph analysis tasks, such as node classification, link prediction, or graph clustering. Anticipation lies in the contribution of the DGSIST framework to a deeper understanding of the spatial organizational structures of diseases such as cancer.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142455003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}