Methods最新文献 - Book学术

Multi-Omics clustering by integrating clinical features from large language model

IF 4.2 3区生物学

Methods Pub Date : 2025-04-01 DOI: 10.1016/j.ymeth.2025.03.017

Xiucai Ye, Tianyi Shi, Dong Huang, Tetsuya Sakurai

{"title":"Multi-Omics clustering by integrating clinical features from large language model","authors":"Xiucai Ye, Tianyi Shi, Dong Huang, Tetsuya Sakurai","doi":"10.1016/j.ymeth.2025.03.017","DOIUrl":"10.1016/j.ymeth.2025.03.017","url":null,"abstract":"<div><div>Multi-omics clustering has emerged as a powerful approach for understanding complex biological systems and enabling cancer subtyping by integrating diverse omics data. Existing methods primarily focus on the integration of different types of omics data, often overlooking the value of clinical context. In this study, we propose a novel framework that incorporates clinical features extracted from large language model (LLM) to enhance multi-omics clustering. Leveraging clinical data extracted from pathology reports using a BERT-based model, our framework converts unstructured medical text into structured clinical features. These features are integrated with omics data through an autoencoder, enriching the information content of each omics layer to improve feature extraction. The extracted features are then projected into a latent subspace using singular value decomposition (SVD), followed by spectral clustering to obtain the final clustering result. We evaluate the proposed framework on six cancer datasets on three omics levels, comparing it with several state-of-the-art methods. The experimental results demonstrate that the proposed framework outperforms existing methods in multi-omics clustering for cancer subtyping. Moreover, the results highlight the efficacy of integrating clinical features derived from LLM, significantly enhancing clustering performance. This work underscores the importance of clinical context in multi-omics analysis and showcases the transformative potential of LLM in advancing precision medicine.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"239 ","pages":"Pages 64-71"},"PeriodicalIF":4.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769113","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

Strategies for ocular drug delivery.

IF 4.2 3区生物学

Methods Pub Date : 2025-03-28 DOI: 10.1016/j.ymeth.2025.03.020

Silvia L Fialho

引用次数: 0

Corrigendum to “Production of AFM wedged cantilevers for stress-relaxation experiments: Uniaxial loading of soft, spherical cells” [Methods 236 (2025) 1–9]

IF 4.2 3区生物学

Methods Pub Date : 2025-03-28 DOI: 10.1016/j.ymeth.2025.03.013

Riccardo Campanile , Jonne Helenius , Cristina Scielzo , Lydia Scarfò , Domenico Salerno , Mario Bossi , Marta Falappi , Alessia Saponara , Daniel J. Müller , Francesco Mantegazza , Valeria Cassina

引用次数: 0

A methodological comparison of synthesizing heavy metal substituted bioapatite

IF 4.2 3区生物学

Methods Pub Date : 2025-03-25 DOI: 10.1016/j.ymeth.2025.03.016

Kennedy A. Drake, Tyler A. Grubelich, Stephanie Wong, Alix C. Deymier

{"title":"A methodological comparison of synthesizing heavy metal substituted bioapatite","authors":"Kennedy A. Drake, Tyler A. Grubelich, Stephanie Wong, Alix C. Deymier","doi":"10.1016/j.ymeth.2025.03.016","DOIUrl":"10.1016/j.ymeth.2025.03.016","url":null,"abstract":"<div><div>This study evaluates two methods—maturation and direct precipitation—for synthesizing heavy metal substituted biomimetic hydroxyapatite (HA), focusing on their efficacy in mimicking human bone composition and crystallinity. Cobalt (Co) and chromium (Cr) substitutions were investigated due to their relevance to metal-on-metal implant degradation and the potential integration of these ions into bone mineral. The maturation method involves prolonged incubation, producing amorphous and bioresorbable apatites, while the direct precipitation (DP) method achieves rapid synthesis of highly crystalline apatites through controlled titration. Both approaches were characterized using X-ray diffraction (XRD), Raman spectroscopy, and Fourier Transform Infrared (FTIR) spectroscopy, confirming the apatitic nature of the samples and lattice strain induced by metal ion substitution. This study highlights the maturation method’s adaptability for long-term biological interactions and the DP method’s mechanical stability for load-bearing applications. Comparison of the structural and chemical properties of substituted HA from each method provides insights into optimizing synthesis techniques for diverse biomedical applications, such as bone tissue engineering and mitigating the effects of heavy metal ion release on bone health. These findings contribute to advancing hydroxyapatite-based biomaterials tailored for therapeutic and regenerative medicine needs.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"239 ","pages":"Pages 42-48"},"PeriodicalIF":4.2,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714607","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

An overview of extracellular field potentials: Different potentiation and measurable components, interpretations, and hippocampal synaptic activity models

IF 4.2 3区生物学

Methods Pub Date : 2025-03-25 DOI: 10.1016/j.ymeth.2025.03.015

Maryam Radahmadi , Alireza Halabian , Arshia Halabian

{"title":"An overview of extracellular field potentials: Different potentiation and measurable components, interpretations, and hippocampal synaptic activity models","authors":"Maryam Radahmadi , Alireza Halabian , Arshia Halabian","doi":"10.1016/j.ymeth.2025.03.015","DOIUrl":"10.1016/j.ymeth.2025.03.015","url":null,"abstract":"<div><div>The hippocampus and some other brain regions are critically involved in synaptic plasticity. Electrophysiological recordings using extracellular field potentials (EFPs) reveal diverse synaptic activity within the hippocampus, including input/output functions (reflecting neural excitability), paired-pulse responses (reflecting short-term plasticity), and long-term potentiation (reflecting long-term plasticity). EFP techniques offer various measurable components for assessing multiple neural functions. These include fEPSP slope, amplitude, and area under curve (AUC), as well as latency (fEPSP onset or peak after stimulation), width at half amplitude, fiber volley, decay time, time-course (fEPSP rise and decay time constants; tau), initial slope/initial area and −/late area derived from a fEPSP waveform sample. Each of these parameters is separately evaluated and provides distinct electrophysiological interpretations. Despite the rich data offered by EFP techniques, many studies adopt a limited approach, focusing solely on fEPSP slope, amplitude, and occasionally AUC, thereby neglecting the potential insights provided by other parameters. Given the inherent variability of fEPSP components within a single recording and timeframe, a comprehensive analysis of synaptic activity within a specific hippocampal region is necessary for obtaining the full spectrum of fEPSP-related data. Researchers should consider the potential influence of additional factors contributing to the variability of synaptic activity magnitude. A detailed analysis considering different parts of extracellular fEPSP recordings and their properties is crucial for a deeper understanding of synaptic activity changes within the brain. Therefore, this review aims to provide a comprehensive overview of diverse forms of hippocampal synaptic activity, measurable components of EFP recordings, and their corresponding interpretations.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"239 ","pages":"Pages 50-63"},"PeriodicalIF":4.2,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143727501","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 synergistic strategy for E2E+ESM2-driven protein a design and wet lab validation

IF 4.2 3区生物学

Methods Pub Date : 2025-03-20 DOI: 10.1016/j.ymeth.2025.03.008

Huijia Song, Shibo Zhang, Qiang He, Huainian Zhang, Chun Fang, Xiaozhu Lin

{"title":"A synergistic strategy for E2E+ESM2-driven protein a design and wet lab validation","authors":"Huijia Song, Shibo Zhang, Qiang He, Huainian Zhang, Chun Fang, Xiaozhu Lin","doi":"10.1016/j.ymeth.2025.03.008","DOIUrl":"10.1016/j.ymeth.2025.03.008","url":null,"abstract":"<div><div>Protein A is widely used in the biopharmaceutical field, playing a key role in antibody purification. It also serves as an important tool for the research of other biomolecules. Therefore, Protein A design is critical for bioengineering and drug development. Although computational protein design has made progress in model building and functional prediction, current methods still face the following limitations: (1) the predictive accuracy of generative models needs improvement, particularly in matching structural and functional features; (2) the multidimensional screening process for generated proteins requires further optimization. To address these issues, a synergistic strategy for Protein A design and wet-lab validation based on E2E+ESM2 is proposed. In the multidimensional screening process, this research introduces the innovative concept of feature distance. First, multiple Protein A-like sequences are synthesized using a generative model, and their tertiary structures are predicted using AlphaFold. Then, feature distances are calculated based on the ESM2 model, and multidimensional screening is performed by combining parameters such as skeleton distance and solubility. Finally, the functional performance of the selected synthetic proteins is validated through affinity testing. The experimental results show that the synthetic protein V2 exhibits excellent binding kinetics, with a <span><math><msub><mrow><mi>K</mi></mrow><mrow><mi>D</mi></mrow></msub></math></span> value of 3.81±0.17E-10 M, close to the target Protein A. The balance between the association and dissociation rates indicates strong binding performance. This method improves the functional consistency and application potential of the generated proteins, providing a promising solution for protein design.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"239 ","pages":"Pages 30-41"},"PeriodicalIF":4.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681301","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

Multitask learning model for predicting non-coding RNA-disease associations: Incorporating local and global context 预测非编码 RNA 与疾病关联的多任务学习模型：结合本地和全球背景。

IF 4.2 3区生物学

Methods Pub Date : 2025-03-18 DOI: 10.1016/j.ymeth.2025.03.009

Xiaohan Li, Guohua Wang, Dan Li, Yang Li

{"title":"Multitask learning model for predicting non-coding RNA-disease associations: Incorporating local and global context","authors":"Xiaohan Li, Guohua Wang, Dan Li, Yang Li","doi":"10.1016/j.ymeth.2025.03.009","DOIUrl":"10.1016/j.ymeth.2025.03.009","url":null,"abstract":"<div><div>Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are crucial non-coding RNAs involved in various diseases. Understanding these interactions is vital for advancing diagnostic, preventive, and therapeutic strategies. Existing computational methods often address lncRNA-miRNA-disease associations as isolated tasks, resulting in sparse connections and limited generalizability. Additionally, these ncRNA-disease relationships involve higher-order topological information that is frequently overlooked. To address these challenges, we propose the MTL-NRDA model, which employs a multi-task learning framework to simultaneously predict lncRNA-disease associations, miRNA-disease associations, and lncRNA-miRNA interactions. The model integrates multi-source information through a heterogeneous network encompassing lncRNAs, miRNAs, and disease association networks as well as various similarity networks. Node embeddings are optimized by combining local and global contexts, and local features are aggregated using higher-order graph convolutional networks (HOGCN) to capture ncRNA-disease associations, while global features are extracted via a transformer encoder, effectively handling long-range dependencies. MTL-NRDA uses independent bilinear output layers for each task and dynamically adjusts the loss weights to calculate task-specific association probabilities. Experiments on two independent datasets show that MTL-NRDA outperforms existing models. Ablation studies confirmed the effectiveness of the model components and multi-task strategy, whereas hyperparameter tuning further improved the performance. Case studies on breast and liver cancers demonstrated the practical applicability of the model.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"239 ","pages":"Pages 10-21"},"PeriodicalIF":4.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668683","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

DriverMEDS: Cancer driver gene identification using mutual exclusivity from embeded features and driver mutation scoring

IF 4.2 3区生物学

Methods Pub Date : 2025-03-18 DOI: 10.1016/j.ymeth.2025.03.010

Sichen Yi , Minzhu Xie

{"title":"DriverMEDS: Cancer driver gene identification using mutual exclusivity from embeded features and driver mutation scoring","authors":"Sichen Yi , Minzhu Xie","doi":"10.1016/j.ymeth.2025.03.010","DOIUrl":"10.1016/j.ymeth.2025.03.010","url":null,"abstract":"<div><div>Efficiently identifying cancer driver genes plays a key role in the cancer development, diagnosis and treatment. Current unsupervised driver gene identification methods typically integrate multi-omics data into gene function networks and employ network embedding algorithms to learn gene features. Additionally, they consider mutual exclusivity and mutation frequency as crucial concepts in identifying driver genes. However, existing approaches neglect the possible important implications of mutual exclusivity in the embedding space. Furthermore, they simply assume that all driver genes exhibit high mutation frequencies. Fortunately, we explored the mutual exclusivity implanted in the learned features and have verified that the Euclidean distances between learned features are strongly related to the mutual exclusivity and they can reveal more information for the mutual exclusivity. Thus, we designed an unsupervised driver gene predicting framework DriverMEDS based on the above idea and a novel driver mutation scoring strategy. First, we design a feature clustering algorithm to generate gene modules. In each module, the Euclidean distances of learned features are used to calculate a module importance score for each gene based on the related mutual exclusivity. Then, following the fact that most of driver genes have intermediate mutation frequencies, a driver mutation scoring function is designed for each gene to optimize the existing mutation frequency scoring strategy. Finally, the weighted sum of the module importance score and the driver mutation score is used to prioritize the genes. The experiment results and analysis show that DriverMEDS could detect novel cancer driver genes and relevant function modules, and outperforms other five state-of-the-art methods for cancer driver identification.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"239 ","pages":"Pages 22-29"},"PeriodicalIF":4.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668679","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

Iontophoresis impact on corneal properties using an ex vivo bovine eye model

IF 4.2 3区生物学

Methods Pub Date : 2025-03-16 DOI: 10.1016/j.ymeth.2025.03.011

Gabriela Fávero Galvão , Izabella Cristina Bernardo Maríngolo , Yugo Araújo Martins , Janette Bezebeth Villarruel Muñoz , Marina Zilio Fantucci , Ricardo Roberto da Silva , Eduardo Melani Rocha , Eloísa Berbel Manaia , Gilles Ponchel , Renata Fonseca Vianna Lopez

{"title":"Iontophoresis impact on corneal properties using an ex vivo bovine eye model","authors":"Gabriela Fávero Galvão , Izabella Cristina Bernardo Maríngolo , Yugo Araújo Martins , Janette Bezebeth Villarruel Muñoz , Marina Zilio Fantucci , Ricardo Roberto da Silva , Eduardo Melani Rocha , Eloísa Berbel Manaia , Gilles Ponchel , Renata Fonseca Vianna Lopez","doi":"10.1016/j.ymeth.2025.03.011","DOIUrl":"10.1016/j.ymeth.2025.03.011","url":null,"abstract":"<div><div>This study addresses the challenge of low drug bioavailability in topical ocular administration by developing and validating an ex vivo bovine eye model chamber to evaluate the effects of iontophoresis on drug delivery and corneal properties. Transepithelial electrical resistance (TEER) was assessed as a predictor of corneal epithelial integrity in dissected bovine eyes. TEER measurements were correlated with methylene blue permeation, confirming a threshold of 4.2 kOhm·cm2 as an indicator of epithelial integrity. The model chamber enabled the application of drug solutions around a defined area of the cornea without leakage, facilitating the placement of electrodes and the application of constant electric currents. Applying iontophoresis at 2 mA/cm2 for 6 min significantly increased rhodamine B penetration into the cornea by nearly sixfold compared to passive diffusion (approximately 1.3 µg/cm2 vs. 0.24 µg/cm2), allowing detectable drug levels in the aqueous humor (27.9 ± 0.5 ng/mL). Morphological analyses revealed temporary changes in the cornea, including a 2.3-fold increase in surface roughness (from 44.6 nm to 105.3 nm) and mild collagen disorganization in the stroma, while Bowman’s membrane remained intact. A significant increase in corneal stiffness was noted, with a 200 % rise in the area under the stress–strain curve after iontophoresis. These findings provide insights into iontophoresis-induced changes and highlight the model’s potential for optimizing ocular drug delivery systems. Additionally, the model aligns with the 3Rs principles and could be instrumental in advancing the understanding of anterior segment diseases driven by structural and biomechanical alterations.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"238 ","pages":"Pages 74-83"},"PeriodicalIF":4.2,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654814","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

Domain alignment method based on masked variational autoencoder for predicting patient anticancer drug response

IF 4.2 3区生物学

Methods Pub Date : 2025-03-14 DOI: 10.1016/j.ymeth.2025.03.012

Wei Dai , Gong Chen , Wei Peng , Chuyue Chen , Xiaodong Fu , Li Liu , Lijun Liu , Ning Yu

{"title":"Domain alignment method based on masked variational autoencoder for predicting patient anticancer drug response","authors":"Wei Dai , Gong Chen , Wei Peng , Chuyue Chen , Xiaodong Fu , Li Liu , Lijun Liu , Ning Yu","doi":"10.1016/j.ymeth.2025.03.012","DOIUrl":"10.1016/j.ymeth.2025.03.012","url":null,"abstract":"<div><div>Predicting the patient’s response to anticancer drugs is essential in personalized treatment plans. However, due to significant distribution differences between cell line data and patient data, models trained well on cell line data may perform poorly on patient anticancer drug response predictions. Some existing methods use transfer learning strategies to implement domain feature alignment between cell lines and patient data and leverage knowledge from cell lines to predict patient anticancer drug responses. This study proposes a domain alignment method based on masked variational autoencoders, MVAEDA, to predict patient anticancer drug responses. The model constructs multiple variational autoencoders (VAEs) and mask predictors to extract specific and domain-invariant features of cell lines and patients. Then, it masks and reconstructs the gene expression matrix, using generative adversarial training to learn domain-invariant features from the cell line and patient domains. These domain-invariant features are then used to train a classifier. Finally, the final trained model predicts the anticancer drug response in the target domain. Our model is experimentally evaluated on the clinical dataset and the preclinical dataset. The results show that our method performs better than other state-of-the-art methods.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"238 ","pages":"Pages 61-73"},"PeriodicalIF":4.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143639353","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