IF 4.2 3区生物学

Methods Pub Date : 2024-06-29 DOI: 10.1016/j.ymeth.2024.06.010

Lin Zhang , Haiping Xiang , Feng Wang , Zepeng Chen , Mo Shen , Jiani Ma , Hui Liu , Hongdang Zheng

{"title":"scGAAC: A graph attention autoencoder for clustering single-cell RNA-sequencing data","authors":"Lin Zhang , Haiping Xiang , Feng Wang , Zepeng Chen , Mo Shen , Jiani Ma , Hui Liu , Hongdang Zheng","doi":"10.1016/j.ymeth.2024.06.010","DOIUrl":"10.1016/j.ymeth.2024.06.010","url":null,"abstract":"<div><p>Single-cell RNA-sequencing (scRNA-seq) enables the investigation of intricate mechanisms governing cell heterogeneity and diversity. Clustering analysis remains a pivotal tool in scRNA-seq for discerning cell types. However, persistent challenges arise from noise, high dimensionality, and dropout in single-cell data. Despite the proliferation of scRNA-seq clustering methods, these often focus on extracting representations from individual cell expression data, neglecting potential intercellular relationships. To overcome this limitation, we introduce scGAAC, a novel clustering method based on an attention-based graph convolutional autoencoder. By leveraging structural information between cells through a graph attention autoencoder, scGAAC uncovers latent relationships while extracting representation information from single-cell gene expression patterns. An attention fusion module amalgamates the learned features of the graph attention autoencoder and the autoencoder through attention weights. Ultimately, a self-supervised learning policy guides model optimization. scGAAC, a hypothesis-free framework, performs better on four real scRNA-seq datasets than most state-of-the-art methods. The scGAAC implementation is publicly available on Github at: <span>https://github.com/labiip/scGAAC</span><svg><path></path></svg>.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 115-124"},"PeriodicalIF":4.2,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141475598","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}

引用次数: 0

APLpred: A machine learning-based tool for accurate prediction and characterization of asparagine peptide lyases using sequence-derived optimal features APLpred：基于机器学习的天冬酰胺肽裂解酶准确预测和特征描述工具，使用序列衍生的最佳特征。

IF 4.2 3区生物学

Methods Pub Date : 2024-06-28 DOI: 10.1016/j.ymeth.2024.05.014

Adeel Malik , Majid Rasool Kamli , Jamal S.M. Sabir , Irfan A. Rather , Le Thi Phan , Chang-Bae Kim , Balachandran Manavalan

{"title":"APLpred: A machine learning-based tool for accurate prediction and characterization of asparagine peptide lyases using sequence-derived optimal features","authors":"Adeel Malik , Majid Rasool Kamli , Jamal S.M. Sabir , Irfan A. Rather , Le Thi Phan , Chang-Bae Kim , Balachandran Manavalan","doi":"10.1016/j.ymeth.2024.05.014","DOIUrl":"10.1016/j.ymeth.2024.05.014","url":null,"abstract":"<div><p>Asparagine peptide lyase (APL) is among the seven groups of proteases, also known as proteolytic enzymes, which are classified according to their catalytic residue. APLs are synthesized as precursors or propeptides that undergo self-cleavage through autoproteolytic reaction. At present, APLs are grouped into 10 families belonging to six different clans of proteases. Recognizing their critical roles in many biological processes including virus maturation, and virulence, accurate identification and characterization of APLs is indispensable. Experimental identification and characterization of APLs is laborious and time-consuming. Here, we developed APLpred, a novel support vector machine (SVM) based predictor that can predict APLs from the primary sequences. APLpred was developed using Boruta-based optimal features derived from seven encodings and subsequently trained using five machine learning algorithms. After evaluating each model on an independent dataset, we selected APLpred (an SVM-based model) due to its consistent performance during cross-validation and independent evaluation. We anticipate APLpred will be an effective tool for identifying APLs. This could aid in designing inhibitors against these enzymes and exploring their functions. The APLpred web server is freely available at <span>https://procarb.org/APLpred/</span><svg><path></path></svg>.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 133-146"},"PeriodicalIF":4.2,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141465130","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}

引用次数: 0

Efficient experimental method for purifying allergens from aqueous extracts 从水提取物中纯化过敏原的高效实验方法。

IF 4.2 3区生物学

Methods Pub Date : 2024-06-23 DOI: 10.1016/j.ymeth.2024.06.008

S. Sivill, S. Iborra, J.F. Cantillo

{"title":"Efficient experimental method for purifying allergens from aqueous extracts","authors":"S. Sivill, S. Iborra, J.F. Cantillo","doi":"10.1016/j.ymeth.2024.06.008","DOIUrl":"10.1016/j.ymeth.2024.06.008","url":null,"abstract":"<div><p>Studying the molecular and immunological basis of allergic diseases often requires purified native allergens. The methodologies for protein purification are usually difficult and may not be completely successful. The objective of this work was to describe a methodology to purify allergens from their natural source, while maintaining their native form. The purification strategy consists of a three-step protocol and was used for purifying five specific allergens, Ole e 1, Amb a 1, Alt a 1, Bet v 1 and Cup a 1.</p><p>Total proteins were extracted in PBS (pH 7.2). Then, the target allergens were pre-purified and enriched by salting-out using increasing concentrations of ammonium sulfate. The allergens were further purified by anion exchange chromatography. Purification of Amb a 1 required an extra step of cation exchange chromatography. The detection of the allergens in the fractions obtained were screened by SDS-PAGE, and Western blot when needed. Further characterization of purified Amb a 1 was performed by mass spectrometry. Ole e 1, Alt a 1, Bet v 1 and Cup a 1 were obtained at > 90 % purity. Amb a 1 was obtained at > 85 % purity.</p><p>Overall, we propose an easy-to-perform purification approach that allows obtaining highly pure allergens. Since it does not involve neither chaotropic nor organic reagents, we anticipate that the structural and biological functions of the purified molecule remain intact. This method provides a basis for native allergen purification that can be tailored according to specific needs.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 63-70"},"PeriodicalIF":4.2,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141449184","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}

引用次数: 0

Improved DiOlistic labelling technique for neurons in situ: Detailed visualisation of dendritic spines and concurrent histochemical-detection in fixed tissue 改进的原位神经元 DiOlistic 标记技术：树突棘的详细可视化和固定组织中的同步组织化学检测。

IF 4.2 3区生物学

Methods Pub Date : 2024-06-23 DOI: 10.1016/j.ymeth.2024.06.009

Ryan J. Bevan , Gloria Cimaglia , James E. Morgan , Philip R. Taylor

{"title":"Improved DiOlistic labelling technique for neurons in situ: Detailed visualisation of dendritic spines and concurrent histochemical-detection in fixed tissue","authors":"Ryan J. Bevan , Gloria Cimaglia , James E. Morgan , Philip R. Taylor","doi":"10.1016/j.ymeth.2024.06.009","DOIUrl":"10.1016/j.ymeth.2024.06.009","url":null,"abstract":"<div><p>DiOlistic labelling is a robust, unbiased ballistic method that utilises lipophilic dyes to morphologically label neurons. While its efficacy on freshly dissected tissue specimens is well-documented, applying DiOlistic labelling to stored, fixed brain tissue and its use in polychromatic multi-marker studies poses significant technical challenges. Here, we present an improved, step-by-step protocol for DiOlistic labelling of dendrites and dendritic spines in fixed mouse tissue. Our protocol encompasses the five key stages: Tissue Preparation, Dye Bullet Preparation, DiOlistic Labelling, Confocal Imaging, and Image Analysis. This method ensures reliable and consistent labelling of dendritic spines in fixed mouse tissue, combined with increased throughput of samples and multi-parameter staining and visualisation of tissue, thereby offering a valuable approach for neuroscientific research.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 82-93"},"PeriodicalIF":4.2,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1046202324001592/pdfft?md5=3fb2134aac8eec5f280834e3c88b7545&pid=1-s2.0-S1046202324001592-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141449185","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}

引用次数: 0

PGCNMDA: Learning node representations along paths with graph convolutional network for predicting miRNA-disease associations PGCNMDA：利用图卷积网络沿路径学习节点表征，预测 miRNA 与疾病的关联。

IF 4.2 3区生物学

Methods Pub Date : 2024-06-22 DOI: 10.1016/j.ymeth.2024.06.007

Shuang Chu , Guihua Duan , Cheng Yan

{"title":"PGCNMDA: Learning node representations along paths with graph convolutional network for predicting miRNA-disease associations","authors":"Shuang Chu , Guihua Duan , Cheng Yan","doi":"10.1016/j.ymeth.2024.06.007","DOIUrl":"10.1016/j.ymeth.2024.06.007","url":null,"abstract":"<div><p>Identifying miRNA-disease associations (MDAs) is crucial for improving the diagnosis and treatment of various diseases. However, biological experiments can be time-consuming and expensive. To overcome these challenges, computational approaches have been developed, with Graph Convolutional Network (GCN) showing promising results in MDA prediction. The success of GCN-based methods relies on learning a meaningful spatial operator to extract effective node feature representations. To enhance the inference of MDAs, we propose a novel method called PGCNMDA, which employs graph convolutional networks with a learning graph spatial operator from paths. This approach enables the generation of meaningful spatial convolutions from paths in GCN, leading to improved prediction performance. On HMDD v2.0, PGCNMDA obtains a mean AUC of 0.9229 and an AUPRC of 0.9206 under 5-fold cross-validation (5-CV), and a mean AUC of 0.9235 and an AUPRC of 0.9212 under 10-fold cross-validation (10-CV), respectively. Additionally, the AUC of PGCNMDA also reaches 0.9238 under global leave-one-out cross-validation (GLOOCV). On HMDD v3.2, PGCNMDA obtains a mean AUC of 0.9413 and an AUPRC of 0.9417 under 5-CV, and a mean AUC of 0.9419 and an AUPRC of 0.9425 under 10-CV, respectively. Furthermore, the AUC of PGCNMDA also reaches 0.9415 under GLOOCV. The results show that PGCNMDA is superior to other compared methods. In addition, the case studies on pancreatic neoplasms, thyroid neoplasms and leukemia show that 50, 50 and 48 of the top 50 predicted miRNAs linked to these diseases are confirmed, respectively. It further validates the effectiveness and feasibility of PGCNMDA in practical applications.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 71-81"},"PeriodicalIF":4.2,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141441884","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}

引用次数: 0

A comprehensive preanalytical protocol for fresh solid tumor biospecimens 新鲜实体瘤生物样本分析前综合方案。

IF 4.2 3区生物学

Methods Pub Date : 2024-06-21 DOI: 10.1016/j.ymeth.2024.06.005

Areesha A. Charania , Aman G. Pokal , Dana R. Zuaiter , Chelsea L. Crawford , Ashwini K. Esnakula , Mozaffarul Islam , Alex C. Kim , Karim I. Budhwani

引用次数: 0

Editorial for methods special issue: Big data in digital health: methods, analysis and prospects 方法》特刊编辑：数字健康中的大数据：方法、分析与前景。

IF 4.2 3区生物学

Methods Pub Date : 2024-06-17 DOI: 10.1016/j.ymeth.2024.06.006

Tapabrata Chakraborti, Subhadip Basu

引用次数: 0

Design of a 1 × 4 micro-magnetic stimulation device and its targeted, coordinated regulation on LTP of Schaffer-CA1 in the hippocampus of rats 1 × 4 微磁刺激装置的设计及其对大鼠海马 Schaffer-CA1 LTP 的定向协调调节。

IF 4.8 3区生物学

Methods Pub Date : 2024-06-15 DOI: 10.1016/j.ymeth.2024.06.004

Yu Zheng , Qiwen Liu , Yuhang Zhao , Yenan Qi , Lei Dong

{"title":"Design of a 1 × 4 micro-magnetic stimulation device and its targeted, coordinated regulation on LTP of Schaffer-CA1 in the hippocampus of rats","authors":"Yu Zheng , Qiwen Liu , Yuhang Zhao , Yenan Qi , Lei Dong","doi":"10.1016/j.ymeth.2024.06.004","DOIUrl":"10.1016/j.ymeth.2024.06.004","url":null,"abstract":"<div><p>Magnetic technology has been a hotspot of neuromodulation research in recent years. However, magnetic coil is limited by their size, and it is impossible to realize precise targeted magnetic stimulation to the target area at the cellular scale. To this end, this study designs a 1 × 4 array micro-magnetic stimulation (<em>μ</em>MS) device with four sub-millimeter-sized elements, enabling precise magnetic stimulation of the CA1-CA3-DG tri-synaptic positions in the rat hippocampal region. First, it is determined that 70 KHz/2 mT/1 min magnetic stimulation parameter has a modulatory effect on the long-term potentiation (LTP) of Schaffer-CA1 in rat hippocampus. Then, a 1 × 4 array <em>μ</em>MS device is used to perform magnetic stimulation at 70 KHz/2 mT/1 min, targeting the CA1, CA3, and DG regions individually with single-point magnetic stimulation; and multi-region magnetic stimulation is applied to the double-point targeting regions of CA1-CA3, CA1-DG, and CA3-DG, as well as the triple-point targeting region of CA1-CA3-DG, so as to investigate the regulation of LTP by single-region magnetic stimulation and multi-region magnetic stimulation. The experimental results indicate that, in the case of single-region magnetic stimulation, the magnitude of the increase in LTP in the CA1 region is the greatest, followed by the CA3 region, while the effect of magnetic stimulation on the DG region is less pronounced. In multi-region magnetic stimulation, synergistic magnetic stimulation of the three-point CA1-CA3-DG results in a greater increase in LTP compared to stimulation of two individual areas, and the enhancement of LTP induction with multi-region magnetic stimulation surpasses that of single-region stimulation. This study has implications for the collaborative targeted magnetic stimulation application of arrayed micro-magnetic devices.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 49-60"},"PeriodicalIF":4.8,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141329974","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}

引用次数: 0

Optimisation of Levilactobacillus brevis-fermented finger millet (Eleusine coracana) and evaluation of its effects on cancer cells (HCT116 and MDA-MB-231) 优化面包乳杆菌（Levilactacillus brevis）发酵小米（Eleusine coracana）并评估其对癌细胞（HCT116 和 MDA-MB-231）的影响。

IF 4.8 3区生物学

Methods Pub Date : 2024-06-14 DOI: 10.1016/j.ymeth.2024.06.002

Sachin Kumar Mahanta , Priyadarshini Pratikshya Nayak , Kartik Muduli , Selvakumar Elangovan , Sethuraman Sivakumar Paramasivan , Pradeep Kumar Mallick , Saumendra Kumar Mohapatra , Sandeep Kumar Panda

{"title":"Optimisation of Levilactobacillus brevis-fermented finger millet (Eleusine coracana) and evaluation of its effects on cancer cells (HCT116 and MDA-MB-231)","authors":"Sachin Kumar Mahanta , Priyadarshini Pratikshya Nayak , Kartik Muduli , Selvakumar Elangovan , Sethuraman Sivakumar Paramasivan , Pradeep Kumar Mallick , Saumendra Kumar Mohapatra , Sandeep Kumar Panda","doi":"10.1016/j.ymeth.2024.06.002","DOIUrl":"10.1016/j.ymeth.2024.06.002","url":null,"abstract":"<div><p>The objective of this study was to optimise the millet formulation using <em>Levilactobacillus brevis</em> and to evaluate its anticarcinogenic potential <em>in vitro</em>. The formula was developed in the course of the fermentation of finger millet (<em>Eleusine coracana</em>) using <em>L. brevis</em> MTTC 4460 and optimised by response surface methodology and validation by artificial neural networking (ANN). The optimised millet formulation could be obtained using 2 % of bacterial inoculum, 2 % of glucose, and a fermentation duration of 3.3 days with a yield of 5.98 mg/mL lactic acid and 3.38 log<sub>10</sub> (CFU/mL) viable <em>L. brevis</em> with overall desirability value of 1. The fermented millet formulation exhibited antiproliferative and antimigratory effects on MDA-MB-231 and HCT116 cancer cell lines. In addition, the outcomes observed in western blot analysis revealed that the formulation elicited apoptotic responses mediated by the Bcl-2 family of proteins in MDA-MB-231 and HCT116 cell lines while demonstrating no discernible impact on HEK293 normal cells.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 30-40"},"PeriodicalIF":4.8,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141329976","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}

引用次数: 0

Hybrid multimodal fusion for graph learning in disease prediction 疾病预测中图谱学习的混合多模态融合。

IF 4.8 3区生物学

Methods Pub Date : 2024-06-14 DOI: 10.1016/j.ymeth.2024.06.003

Ruomei Wang , Wei Guo , Yongjie Wang , Xin Zhou , Jonathan Cyril Leung , Shuo Yan , Lizhen Cui

{"title":"Hybrid multimodal fusion for graph learning in disease prediction","authors":"Ruomei Wang , Wei Guo , Yongjie Wang , Xin Zhou , Jonathan Cyril Leung , Shuo Yan , Lizhen Cui","doi":"10.1016/j.ymeth.2024.06.003","DOIUrl":"10.1016/j.ymeth.2024.06.003","url":null,"abstract":"<div><p>Graph neural networks (GNNs) have gained significant attention in disease prediction where the latent embeddings of patients are modeled as nodes and the similarities among patients are represented through edges. The graph structure, which determines how information is aggregated and propagated, plays a crucial role in graph learning. Recent approaches typically create graphs based on patients' latent embeddings, which may not accurately reflect their real-world closeness. Our analysis reveals that raw data, such as demographic attributes and laboratory results, offers a wealth of information for assessing patient similarities and can serve as a compensatory measure for graphs constructed exclusively from latent embeddings. In this study, we first construct adaptive graphs from both latent representations and raw data respectively, and then merge these graphs via weighted summation. Given that the graphs may contain extraneous and noisy connections, we apply degree-sensitive edge pruning and kNN sparsification techniques to selectively sparsify and prune these edges. We conducted intensive experiments on two diagnostic prediction datasets, and the results demonstrate that our proposed method surpasses current state-of-the-art techniques.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 41-48"},"PeriodicalIF":4.8,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141329975","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}

引用次数: 0

Methods最新文献