Methods最新文献

{"title":"PGCNMDA: Learning node representations along paths with graph convolutional network for predicting miRNA-disease associations","authors":"Shuang Chu ,&nbsp;Guihua Duan ,&nbsp;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}

{"title":"A comprehensive preanalytical protocol for fresh solid tumor biospecimens","authors":"Areesha A. Charania ,&nbsp;Aman G. Pokal ,&nbsp;Dana R. Zuaiter ,&nbsp;Chelsea L. Crawford ,&nbsp;Ashwini K. Esnakula ,&nbsp;Mozaffarul Islam ,&nbsp;Alex C. Kim ,&nbsp;Karim I. Budhwani","doi":"10.1016/j.ymeth.2024.06.005","DOIUrl":"10.1016/j.ymeth.2024.06.005","url":null,"abstract":"<div><p>Nearly seventy percent of diagnostic lab test errors occur due to variability in preanalytical factors. These are the parameters involved with all aspects of tissue processing, starting from the time tissue is collected from the patient in the operating room, until it is received and tested in the laboratory. While there are several protocols for transporting fixed tissue, organs, and liquid biopsies, such protocols are lacking for transport and handling of live solid tumor tissue specimens. There is a critical need to establish preanalytical protocols to reduce variability in biospecimen integrity and improve diagnostics for personalized medicine. Here, we provide a comprehensive protocol for the standard collection, handling, packaging, cold-chain logistics, and receipt of solid tumor tissue biospecimens to preserve tissue viability.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 108-114"},"PeriodicalIF":4.2,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141441883","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":"Editorial for methods special issue: Big data in digital health: methods, analysis and prospects","authors":"Tapabrata Chakraborti,&nbsp;Subhadip Basu","doi":"10.1016/j.ymeth.2024.06.006","DOIUrl":"10.1016/j.ymeth.2024.06.006","url":null,"abstract":"","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 61-62"},"PeriodicalIF":4.2,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425975","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":"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 ,&nbsp;Qiwen Liu ,&nbsp;Yuhang Zhao ,&nbsp;Yenan Qi ,&nbsp;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}

{"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 ,&nbsp;Priyadarshini Pratikshya Nayak ,&nbsp;Kartik Muduli ,&nbsp;Selvakumar Elangovan ,&nbsp;Sethuraman Sivakumar Paramasivan ,&nbsp;Pradeep Kumar Mallick ,&nbsp;Saumendra Kumar Mohapatra ,&nbsp;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 log₁₀ (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}

{"title":"Hybrid multimodal fusion for graph learning in disease prediction","authors":"Ruomei Wang ,&nbsp;Wei Guo ,&nbsp;Yongjie Wang ,&nbsp;Xin Zhou ,&nbsp;Jonathan Cyril Leung ,&nbsp;Shuo Yan ,&nbsp;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}

{"title":"DP-site: A dual deep learning-based method for protein-peptide interaction site prediction","authors":"Shima Shafiee ,&nbsp;Abdolhossein Fathi ,&nbsp;Ghazaleh Taherzadeh","doi":"10.1016/j.ymeth.2024.06.001","DOIUrl":"10.1016/j.ymeth.2024.06.001","url":null,"abstract":"<div><h3>Background</h3><p>Protein-peptide interaction prediction is an important topic for several applications including various biological processes, understanding drug discovery, protein function abnormal cellular behaviors, and treating diseases. Over the years, studies have shown that experimental methods have improved the identification of this bio-molecular interaction. However, predicting protein-peptide interactions using these methods is laborious, time-consuming, dependent on third-party tools, and costly.</p></div><div><h3>Method</h3><p>To address these previous drawbacks, this study introduces a computational framework called DP-Site. The proposed framework concentrates on using a compound of a dual pipeline along with a combination predictor. A deep convolutional neural network for feature extraction and classification is embedded in pipeline 1. In addition, pipeline 2 includes a deep long-short-term memory<strong>-</strong>based and a random forest classifier for feature extraction and classification. In this investigation, the evolutionary, structure-based, sequence-based, and physicochemical information of proteins is utilized for identifying protein-peptide interaction at the residue level.</p></div><div><h3>Results</h3><p>The proposed method is evaluated on both the ten-fold cross-validation and independent test sets. The robust and consistent results between cross-validation and independent test sets confirm the ability of the proposed method to predict peptide binding residues in proteins. Moreover, experimental findings demonstrate that DP-Site has significantly outperformed other state-of-the-art sequence-based and structure-based methods. The proposed method achieves a remarkable balance between a specificity of 0.799 and a sensitivity of 0.770, along with the best f-measure of 0.661 and the highest precision of 0.580 using an independent test set.</p></div><div><h3>Conclusions</h3><p>The outcome of various experiments confirms the proficiency of the proposed method and outperforms state-of-the-art sequence-based and structure-based methods in terms of the mentioned criteria. DP-Site can be accessed at <span>https://github.com/shafiee</span><svg><path></path></svg> 95/shima.shafiee.DP-Site.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 17-29"},"PeriodicalIF":4.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141316367","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":"MSO-GP: 3-D segmentation of large and complex conjoined tree structures","authors":"Arijit De ,&nbsp;Nirmal Das ,&nbsp;Punam K. Saha ,&nbsp;Alejandro Comellas ,&nbsp;Eric Hoffman ,&nbsp;Subhadip Basu ,&nbsp;Tapabrata Chakraborti","doi":"10.1016/j.ymeth.2024.05.016","DOIUrl":"10.1016/j.ymeth.2024.05.016","url":null,"abstract":"<div><p>Robust segmentation of large and complex conjoined tree structures in 3-D is a major challenge in computer vision. This is particularly true in computational biology, where we often encounter large data structures in size, but few in number, which poses a hard problem for learning algorithms. We show that merging multiscale opening with geodesic path propagation, can shed new light on this classic machine vision challenge, while circumventing the learning issue by developing an unsupervised visual geometry approach (digital topology/morphometry). The novelty of the proposed MSO-GP method comes from the geodesic path propagation being guided by a skeletonization of the conjoined structure that helps to achieve robust segmentation results in a particularly challenging task in this area, that of artery-vein separation from non-contrast pulmonary computed tomography angiograms. This is an important first step in measuring vascular geometry to then diagnose pulmonary diseases and to develop image-based phenotypes. We first present proof-of-concept results on synthetic data, and then verify the performance on pig lung and human lung data with less segmentation time and user intervention needs than those of the competing methods.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 9-16"},"PeriodicalIF":4.8,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141260624","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":"Copper catalyzed cycloaddition for the synthesis of non isomerisable 2′ and 3′-regioisomers of arg-tRNAarg","authors":"Yusif Afandizada ,&nbsp;Thilini Abeywansha ,&nbsp;Vincent Guerineau ,&nbsp;Yi Zhang ,&nbsp;Bruno Sargueil ,&nbsp;Luc Ponchon ,&nbsp;Laura Iannazzo ,&nbsp;Mélanie Etheve-Quelquejeu","doi":"10.1016/j.ymeth.2024.05.017","DOIUrl":"10.1016/j.ymeth.2024.05.017","url":null,"abstract":"<div><p>In this report, non-isomerisable analogs of arginine tRNA (Arg-triazole-tRNA) have been synthesized as tools to study tRNA-dependent aminoacyl-transferases. The synthesis involves the incorporation of 1,4 substituted-1,2,3 triazole ring to mimic the ester bond that connects the amino acid to the terminal adenosine in the natural substrate. The synthetic procedure includes (i) a coupling between 2′- or 3′-azido-adenosine derivatives and a cytidine phosphoramidite to access dinucleotide molecules, (ii) Cu-catalyzed cycloaddition reactions between 2′- or 3′-azido dinucleotide in the presence of an alkyne molecule mimicking the arginine, providing the corresponding Arg-triazole-dinucleotides, (iii) enzymatic phosphorylation of the 5′-end extremity of the Arg-triazole-dinucleotides with a polynucleotide kinase, and (iv) enzymatic ligation of the 5′-phosphorylated dinucleotides with a 23-nt RNA micro helix that mimics the acceptor arm of arg-tRNA or with a full tRNA^arg. Characterization of nucleoside and nucleotide compounds involved MS spectrometry, ¹H, ¹³C and ³¹P NMR analysis. This strategy allows to obtain the pair of the two stable regioisomers of arg-tRNA analogs (2′ and 3′) which are instrumental to explore the regiospecificity of arginyl transferases enzyme. In our study, a first binding assay of the arg-tRNA micro helix with the Arginyl-tRNA-protein transferase 1 (ATE1) was performed by gel shift assays.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 94-107"},"PeriodicalIF":4.2,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141246889","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":"RDscan: Extracting RNA-disease relationship from the literature based on pre-training model","authors":"Yang Zhang ,&nbsp;Yu Yang ,&nbsp;Liping Ren ,&nbsp;Lin Ning ,&nbsp;Quan Zou ,&nbsp;Nanchao Luo ,&nbsp;Yinghui Zhang ,&nbsp;Ruijun Liu","doi":"10.1016/j.ymeth.2024.05.012","DOIUrl":"https://doi.org/10.1016/j.ymeth.2024.05.012","url":null,"abstract":"<div><p>With the rapid advancements in molecular biology and genomics, a multitude of connections between RNA and diseases has been unveiled, making the efficient and accurate extraction of RNA-disease (RD) relationships from extensive biomedical literature crucial for advancing research in this field. This study introduces RDscan, a novel text mining method developed based on the pre-training and fine-tuning strategy, aimed at automatically extracting RD-related information from a vast corpus of literature using pre-trained biomedical large language models (LLM). Initially, we constructed a dedicated RD corpus by manually curating from literature, comprising 2,082 positive and 2,000 negative sentences, alongside an independent test dataset (comprising 500 positive and 500 negative sentences) for training and evaluating RDscan. Subsequently, by fine-tuning the Bioformer and BioBERT pre-trained models, RDscan demonstrated exceptional performance in text classification and named entity recognition (NER) tasks. In 5-fold cross-validation, RDscan significantly outperformed traditional machine learning methods (Support Vector Machine, Logistic Regression and Random Forest). In addition, we have developed an accessible webserver that assists users in extracting RD relationships from text. In summary, RDscan represents the first text mining tool specifically designed for RD relationship extraction, and is poised to emerge as an invaluable tool for researchers dedicated to exploring the intricate interactions between RNA and diseases. Webserver of RDscan is free available at <span>https://cellknowledge.com.cn/RDscan/</span><svg><path></path></svg>.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"228 ","pages":"Pages 48-54"},"PeriodicalIF":4.8,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141090649","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}