Methods最新文献_第5页

CSSEC: An adaptive approach integrating consensus and specific self-expressive coefficients for multi-omics cancer subtyping

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-01-27 DOI: 10.1016/j.ymeth.2025.01.016

Yueyi Cai, Nan Zhou, Junran Zhao, Weihua Li, Shunfang Wang

Cancer is a complex and heterogeneous disease, and accurate cancer subtyping can significantly improve patient survival rates. The complexity of cancer spans multiple omics levels, and analyzing multi-omics data for cancer subtyping has become a major focus of research. However, extracting complementary information from different omics data sources and adaptively integrating them remains a major challenge. To address this, we proposed an adaptive approach integrating consensus and specific self-expressive coefficients for multi-omics cancer subtyping (CSSEC). First, independent self-expressive networks are applied to each omics to calculate coefficient matrices to measure patient similarity. Then, two feature graph convolutional network modules capture consensus and specific similarity features using the topK relevant features. Finally, the multi-omics self-expression coefficient matrix is constructed by consensus and specific similarity features. Furthermore, joint consistency and disparity constraints are applied to regularize the fusion of the self-expressive coefficients. Experimental results demonstrate that CSSEC outperforms existing state-of-the-art methods in survival analysis. Moreover, case studies on kidney cancer confirm that the cancer subtypes identified by CSSEC are biologically significant. The complete code can be available at https://github.com/ykxhs/CSSEC.

{"title":"CSSEC: An adaptive approach integrating consensus and specific self-expressive coefficients for multi-omics cancer subtyping","authors":"Yueyi Cai, Nan Zhou, Junran Zhao, Weihua Li, Shunfang Wang","doi":"10.1016/j.ymeth.2025.01.016","DOIUrl":"10.1016/j.ymeth.2025.01.016","url":null,"abstract":"<div><div>Cancer is a complex and heterogeneous disease, and accurate cancer subtyping can significantly improve patient survival rates. The complexity of cancer spans multiple omics levels, and analyzing multi-omics data for cancer subtyping has become a major focus of research. However, extracting complementary information from different omics data sources and adaptively integrating them remains a major challenge. To address this, we proposed an adaptive approach integrating consensus and specific self-expressive coefficients for multi-omics cancer subtyping (CSSEC). First, independent self-expressive networks are applied to each omics to calculate coefficient matrices to measure patient similarity. Then, two feature graph convolutional network modules capture consensus and specific similarity features using the topK relevant features. Finally, the multi-omics self-expression coefficient matrix is constructed by consensus and specific similarity features. Furthermore, joint consistency and disparity constraints are applied to regularize the fusion of the self-expressive coefficients. Experimental results demonstrate that CSSEC outperforms existing state-of-the-art methods in survival analysis. Moreover, case studies on kidney cancer confirm that the cancer subtypes identified by CSSEC are biologically significant. The complete code can be available at <span><span>https://github.com/ykxhs/CSSEC</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"235 ","pages":"Pages 26-33"},"PeriodicalIF":4.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143063046","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

LiteMamba-Bound: A lightweight Mamba-based model with boundary-aware and normalized active contour loss for skin lesion segmentation

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-01-27 DOI: 10.1016/j.ymeth.2025.01.008

Quang-Huy Ho, Thi-Nhu-Quynh Nguyen, Thi-Thao Tran, Van-Truong Pham

In the field of medical science, skin segmentation has gained significant importance, particularly in dermatology and skin cancer research. This domain demands high precision in distinguishing critical regions (such as lesions or moles) from healthy skin in medical images. With growing technological advancements, deep learning models have emerged as indispensable tools in addressing these challenges. One of the state-of-the-art modules revealed in recent years, the 2D Selective Scan (SS2D), based on state-space models that have already seen great success in natural language processing, has been increasingly adopted and is gradually replacing Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Leveraging the strength of this module, this paper introduces LiteMamba-Bound, a lightweight model with approximately 957K parameters, designed for skin image segmentation tasks. Notably, the Channel Attention Dual Mamba (CAD-Mamba) block is proposed within both the encoder and decoder alongside the Mix Convolution with Simple Attention bottleneck block to emphasize key features. Additionally, we propose the Reverse Attention Boundary Module to highlight challenging boundary features. Also, the Normalized Active Contour loss function presented in this paper significantly improves the model's performance compared to other loss functions. To validate performance, we conducted tests on two skin image datasets, ISIC2018 and PH2, with results consistently showing superior performance compared to other models. Our code will be made publicly available at: https://github.com/kwanghwi242/A-new-segmentation-model.

在医学科学领域，皮肤分割已变得越来越重要，尤其是在皮肤病学和皮肤癌研究方面。该领域要求高精度地从医学图像中区分关键区域（如病变或痣）和健康皮肤。随着技术的不断进步，深度学习模型已成为应对这些挑战不可或缺的工具。二维选择性扫描（SS2D）是近年来揭示的最先进的模块之一，它基于状态空间模型，已在自然语言处理领域取得巨大成功，被越来越多地采用，并逐渐取代卷积神经网络（CNN）和视觉转换器（ViT）。利用这一模块的优势，本文介绍了 LiteMamba-Bound，这是一种参数约为 957K 的轻量级模型，专为皮肤图像分割任务而设计。值得注意的是，我们在编码器和解码器中提出了通道注意力双曼巴（CAD-Mamba）模块，以及混合卷积与简单注意力瓶颈模块，以强调关键特征。此外，我们还提出了反向注意力边界模块，以突出具有挑战性的边界特征。此外，与其他损失函数相比，本文提出的归一化主动轮廓损失函数显著提高了模型的性能。为了验证性能，我们在 ISIC2018 和 PH2 两个皮肤图像数据集上进行了测试，结果一致显示，与其他模型相比，我们的模型性能更优。我们的代码将在以下网址公开：urlhttps://github.com/kwanghwi242/A-new-segmentation-model。

{"title":"LiteMamba-Bound: A lightweight Mamba-based model with boundary-aware and normalized active contour loss for skin lesion segmentation","authors":"Quang-Huy Ho, Thi-Nhu-Quynh Nguyen, Thi-Thao Tran, Van-Truong Pham","doi":"10.1016/j.ymeth.2025.01.008","DOIUrl":"10.1016/j.ymeth.2025.01.008","url":null,"abstract":"<div><div>In the field of medical science, skin segmentation has gained significant importance, particularly in dermatology and skin cancer research. This domain demands high precision in distinguishing critical regions (such as lesions or moles) from healthy skin in medical images. With growing technological advancements, deep learning models have emerged as indispensable tools in addressing these challenges. One of the state-of-the-art modules revealed in recent years, the 2D Selective Scan (SS2D), based on state-space models that have already seen great success in natural language processing, has been increasingly adopted and is gradually replacing Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Leveraging the strength of this module, this paper introduces LiteMamba-Bound, a lightweight model with approximately 957K parameters, designed for skin image segmentation tasks. Notably, the Channel Attention Dual Mamba (CAD-Mamba) block is proposed within both the encoder and decoder alongside the Mix Convolution with Simple Attention bottleneck block to emphasize key features. Additionally, we propose the Reverse Attention Boundary Module to highlight challenging boundary features. Also, the Normalized Active Contour loss function presented in this paper significantly improves the model's performance compared to other loss functions. To validate performance, we conducted tests on two skin image datasets, ISIC2018 and PH2, with results consistently showing superior performance compared to other models. Our code will be made publicly available at: <span><span>https://github.com/kwanghwi242/A-new-segmentation-model</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"235 ","pages":"Pages 10-25"},"PeriodicalIF":4.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045193","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

MPEMDA: A multi-similarity integration approach with pre-completion and error correction for predicting microbe-drug associations

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-01-23 DOI: 10.1016/j.ymeth.2024.12.013

Yuxiang Li , Haochen Zhao , Jianxin Wang

Exploring the associations between microbes and drugs offers valuable insights into their underlying mechanisms. Traditional wet lab experiments, while reliable, are often time-consuming and labor-intensive, making computational approaches an attractive alternative. Existing similarity-based machine learning models for predicting microbe-drug associations typically rely on integrated similarities as input, neglecting the unique contributions of individual similarities, which can compromise predictive accuracy. To overcome these limitations, we develop MPEMDA, a novel method that pre-completes the microbe-drug association matrix using various similarity combinations and employs a label propagation algorithm with error correction to predict microbe-drug associations. Compared with existing methods, MPEMDA simultaneously utilizes the integrated and individual similarities obtained through the Similarity Network Fusion (SNF) method to pre-complete the known drug-microbe association matrix, followed by error correction to optimize the predictive scores generated by the label propagation algorithm. Experimental results on three benchmark datasets show that MPEMDA outperforms state-of-the-art methods in both the 5-fold cross-validation and de novo test. Additionally, case studies on drugs and microbes highlight the method's strong potential to identify novel microbe-drug associations. The MPEMDA code is available at https://github.com/lyx8527/MPEMDA.

{"title":"MPEMDA: A multi-similarity integration approach with pre-completion and error correction for predicting microbe-drug associations","authors":"Yuxiang Li , Haochen Zhao , Jianxin Wang","doi":"10.1016/j.ymeth.2024.12.013","DOIUrl":"10.1016/j.ymeth.2024.12.013","url":null,"abstract":"<div><div>Exploring the associations between microbes and drugs offers valuable insights into their underlying mechanisms. Traditional wet lab experiments, while reliable, are often time-consuming and labor-intensive, making computational approaches an attractive alternative. Existing similarity-based machine learning models for predicting microbe-drug associations typically rely on integrated similarities as input, neglecting the unique contributions of individual similarities, which can compromise predictive accuracy. To overcome these limitations, we develop MPEMDA, a novel method that pre-completes the microbe-drug association matrix using various similarity combinations and employs a label propagation algorithm with error correction to predict microbe-drug associations. Compared with existing methods, MPEMDA simultaneously utilizes the integrated and individual similarities obtained through the Similarity Network Fusion (SNF) method to pre-complete the known drug-microbe association matrix, followed by error correction to optimize the predictive scores generated by the label propagation algorithm. Experimental results on three benchmark datasets show that MPEMDA outperforms state-of-the-art methods in both the 5-fold cross-validation and <em>de novo</em> test. Additionally, case studies on drugs and microbes highlight the method's strong potential to identify novel microbe-drug associations. The MPEMDA code is available at <span><span>https://github.com/lyx8527/MPEMDA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"235 ","pages":"Pages 1-9"},"PeriodicalIF":4.2,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143035727","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

Evaluation of unitary conductance of gap junction channels based on stationary fluctuation analysis

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-01-20 DOI: 10.1016/j.ymeth.2025.01.006

Orestas Makniusevicius , Lukas Gudaitis , Tadas Kraujalis , Lina Kraujaliene , Mindaugas Snipas

Gap junction (GJ) channels, formed of connexin (Cx) protein, enable direct intercellular communication in most vertebrate tissues. One of the key biophysical characteristics of these channels is their unitary conductance, which can be affected by mutations in Cx genes and various biochemical factors, such as posttranslational modifications. Due to the unique intercellular configuration of GJ channels, recording single-channel currents is challenging, and precise data on unitary conductances of some Cx isoforms remain limited. In this study, we applied stationary noise analysis, a method successfully used for ion channels with very low unitary conductances, to GJ channels. We modified this technique to account for the residual conductance of GJ channels and present three strategies for estimating unitary conductance, including model-based evaluation of open-state probability and subtraction of residual conductance. To assess the validity, advantages, and limitations of these approaches, we performed mathematical analysis and simulation experiments. We also addressed practical issues such as the underestimation of sample variance in autocorrelated recordings and channel rundown, proposing solutions to these issues. Finally, we applied these strategies to electrophysiological data recorded from cells expressing Cx45. Our findings showed that noise-based estimates of Cx45 unitary conductance from macroscopic currents align well with those obtained from single-channel recordings.

{"title":"Evaluation of unitary conductance of gap junction channels based on stationary fluctuation analysis","authors":"Orestas Makniusevicius , Lukas Gudaitis , Tadas Kraujalis , Lina Kraujaliene , Mindaugas Snipas","doi":"10.1016/j.ymeth.2025.01.006","DOIUrl":"10.1016/j.ymeth.2025.01.006","url":null,"abstract":"<div><div>Gap junction (GJ) channels, formed of connexin (Cx) protein, enable direct intercellular communication in most vertebrate tissues. One of the key biophysical characteristics of these channels is their unitary conductance, which can be affected by mutations in Cx genes and various biochemical factors, such as posttranslational modifications. Due to the unique intercellular configuration of GJ channels, recording single-channel currents is challenging, and precise data on unitary conductances of some Cx isoforms remain limited. In this study, we applied stationary noise analysis, a method successfully used for ion channels with very low unitary conductances, to GJ channels. We modified this technique to account for the residual conductance of GJ channels and present three strategies for estimating unitary conductance, including model-based evaluation of open-state probability and subtraction of residual conductance. To assess the validity, advantages, and limitations of these approaches, we performed mathematical analysis and simulation experiments. We also addressed practical issues such as the underestimation of sample variance in autocorrelated recordings and channel rundown, proposing solutions to these issues. Finally, we applied these strategies to electrophysiological data recorded from cells expressing Cx45. Our findings showed that noise-based estimates of Cx45 unitary conductance from macroscopic currents align well with those obtained from single-channel recordings.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"235 ","pages":"Pages 81-91"},"PeriodicalIF":4.2,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143021544","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

In silico identification of Histone Deacetylase inhibitors using Streamlined Masked Transformer-based Pretrained features 使用基于预训练特征的流式掩码变换器对组蛋白去乙酰化酶抑制剂进行硅学识别。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-11-23 DOI: 10.1016/j.ymeth.2024.11.009

Tuan Vinh , Thanh-Hoang Nguyen-Vo , Viet-Tuan Le , Xuan-Phuc Phan-Nguyen , Binh P. Nguyen

Histone Deacetylases (HDACs) are enzymes that regulate gene expression by removing acetyl groups from histones. They are involved in various diseases, including neurodegenerative, cardiovascular, inflammatory, and metabolic disorders, as well as fibrosis in the liver, lungs, and kidneys. Successfully identifying potent HDAC inhibitors may offer a promising approach to treating these diseases. In addition to experimental techniques, researchers have introduced several in silico methods for identifying HDAC inhibitors. However, these existing computer-aided methods have shortcomings in their modeling stages, which limit their applications. In our study, we present a Streamlined Masked Transformer-based Pretrained (SMTP) encoder, which can be used to generate features for downstream tasks. The training process of the SMTP encoder was directed by masked attention-based learning, enhancing the model's generalizability in encoding molecules. The SMTP features were used to develop 11 classification models identifying 11 HDAC isoforms. We trained SMTP, a lightweight encoder, with only 1.9 million molecules, a smaller number than other known molecular encoders, yet its discriminant ability remains competitive. The results revealed that machine learning models developed using the SMTP feature set outperformed those developed using other feature sets in 8 out of 11 classification tasks. Additionally, chemical diversity analysis confirmed the encoder's effectiveness in distinguishing between two classes of molecules.

组蛋白去乙酰化酶（HDACs）是一种通过去除组蛋白上的乙酰基来调节基因表达的酶。它们与多种疾病有关，包括神经退行性疾病、心血管疾病、炎症和代谢紊乱，以及肝、肺和肾的纤维化。成功鉴定出强效的 HDAC 抑制剂可能会为治疗这些疾病提供一种前景广阔的方法。除实验技术外，研究人员还引入了几种用于鉴定 HDAC 抑制剂的硅学方法。然而，这些现有的计算机辅助方法在建模阶段存在缺陷，限制了它们的应用。在我们的研究中，我们提出了一种基于简化屏蔽变换器的预训练（SMTP）编码器，可用于生成下游任务的特征。SMTP 编码器的训练过程由基于掩蔽注意力的学习指导，从而增强了模型在编码分子中的通用性。我们利用 SMTP 特征开发了 11 个分类模型，识别了 11 种 HDAC 异构体。我们仅用 190 万个分子对轻量级编码器 SMTP 进行了训练，这比其他已知分子编码器的数量要少，但其判别能力仍然具有竞争力。结果显示，在 11 项分类任务中，使用 SMTP 特征集开发的机器学习模型在 8 项任务中的表现优于使用其他特征集开发的模型。此外，化学多样性分析也证实了编码器在区分两类分子方面的有效性。

{"title":"In silico identification of Histone Deacetylase inhibitors using Streamlined Masked Transformer-based Pretrained features","authors":"Tuan Vinh , Thanh-Hoang Nguyen-Vo , Viet-Tuan Le , Xuan-Phuc Phan-Nguyen , Binh P. Nguyen","doi":"10.1016/j.ymeth.2024.11.009","DOIUrl":"10.1016/j.ymeth.2024.11.009","url":null,"abstract":"<div><div>Histone Deacetylases (HDACs) are enzymes that regulate gene expression by removing acetyl groups from histones. They are involved in various diseases, including neurodegenerative, cardiovascular, inflammatory, and metabolic disorders, as well as fibrosis in the liver, lungs, and kidneys. Successfully identifying potent HDAC inhibitors may offer a promising approach to treating these diseases. In addition to experimental techniques, researchers have introduced several <em>in silico</em> methods for identifying HDAC inhibitors. However, these existing computer-aided methods have shortcomings in their modeling stages, which limit their applications. In our study, we present a <u>S</u>treamlined <u>M</u>asked <u>T</u>ransformer-based Pretrained (SMTP) encoder, which can be used to generate features for downstream tasks. The training process of the SMTP encoder was directed by masked attention-based learning, enhancing the model's generalizability in encoding molecules. The SMTP features were used to develop 11 classification models identifying 11 HDAC isoforms. We trained SMTP, a lightweight encoder, with only 1.9 million molecules, a smaller number than other known molecular encoders, yet its discriminant ability remains competitive. The results revealed that machine learning models developed using the SMTP feature set outperformed those developed using other feature sets in 8 out of 11 classification tasks. Additionally, chemical diversity analysis confirmed the encoder's effectiveness in distinguishing between two classes of molecules.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 1-9"},"PeriodicalIF":4.2,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142708536","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

Robust feature learning using contractive autoencoders for multi-omics clustering in cancer subtyping 利用收缩自编码器进行稳健特征学习，用于癌症亚型中的多组学聚类。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-11-20 DOI: 10.1016/j.ymeth.2024.11.013

Mengke Guo, Xiucai Ye, Dong Huang, Tetsuya Sakurai

Cancer can manifest in virtually any tissue or organ, necessitating precise subtyping of cancer patients to enhance diagnosis, treatment, and prognosis. With the accumulation of vast amounts of omics data, numerous studies have focused on integrating multi-omics data for cancer subtyping using clustering techniques. However, due to the heterogeneity of different omics data, extracting important features to effectively integrate these data for accurate clustering analysis remains a significant challenge. This study proposes a new multi-omics clustering framework for cancer subtyping, which utilizes contractive autoencoder to extract robust features. By encouraging the learned representation to be less sensitive to small changes, the contractive autoencoder learns robust feature representations from different omics. To incorporate survival information into the clustering analysis, Cox proportional hazards regression is used to further select the key features significantly associated with survival for integration. Finally, we utilize K-means clustering on the integrated feature to obtain the clustering result. The proposed framework is evaluated on ten different cancer datasets across four levels of omics data and compared to other existing methods. The experimental results indicate that the proposed framework effectively integrates the four omics datasets and outperforms other methods, achieving higher C-index scores and showing more significant differences between survival curves. Additionally, differential gene analysis and pathway enrichment analysis are performed to further demonstrate the effectiveness of the proposed method framework.

癌症几乎可以发生在任何组织或器官，因此有必要对癌症患者进行精确的亚型分类，以提高诊断、治疗和预后效果。随着海量组学数据的积累，许多研究集中于利用聚类技术整合多组学数据进行癌症亚型分析。然而，由于不同组学数据的异质性，如何提取重要特征以有效整合这些数据并进行准确的聚类分析仍是一项重大挑战。本研究提出了一种用于癌症亚型划分的新型多组学聚类框架，该框架利用收缩自动编码器提取稳健特征。通过鼓励所学表征对微小变化不那么敏感，收缩自动编码器可从不同的组学中学习稳健的特征表征。为了将生存信息纳入聚类分析，我们使用考克斯比例危险回归进一步选择与生存显著相关的关键特征进行整合。最后，我们对整合后的特征进行 K-means 聚类，以获得聚类结果。我们在 10 个不同的癌症数据集上对所提出的框架进行了评估，这些数据集涉及 4 个层次的 omics 数据，并与其他现有方法进行了比较。实验结果表明，提出的框架有效地整合了四个全息数据集，并优于其他方法，获得了更高的 C 指数得分，生存曲线之间的差异也更显著。此外，还进行了差异基因分析和通路富集分析，进一步证明了所提方法框架的有效性。

{"title":"Robust feature learning using contractive autoencoders for multi-omics clustering in cancer subtyping","authors":"Mengke Guo, Xiucai Ye, Dong Huang, Tetsuya Sakurai","doi":"10.1016/j.ymeth.2024.11.013","DOIUrl":"10.1016/j.ymeth.2024.11.013","url":null,"abstract":"<div><div>Cancer can manifest in virtually any tissue or organ, necessitating precise subtyping of cancer patients to enhance diagnosis, treatment, and prognosis. With the accumulation of vast amounts of omics data, numerous studies have focused on integrating multi-omics data for cancer subtyping using clustering techniques. However, due to the heterogeneity of different omics data, extracting important features to effectively integrate these data for accurate clustering analysis remains a significant challenge. This study proposes a new multi-omics clustering framework for cancer subtyping, which utilizes contractive autoencoder to extract robust features. By encouraging the learned representation to be less sensitive to small changes, the contractive autoencoder learns robust feature representations from different omics. To incorporate survival information into the clustering analysis, Cox proportional hazards regression is used to further select the key features significantly associated with survival for integration. Finally, we utilize K-means clustering on the integrated feature to obtain the clustering result. The proposed framework is evaluated on ten different cancer datasets across four levels of omics data and compared to other existing methods. The experimental results indicate that the proposed framework effectively integrates the four omics datasets and outperforms other methods, achieving higher C-index scores and showing more significant differences between survival curves. Additionally, differential gene analysis and pathway enrichment analysis are performed to further demonstrate the effectiveness of the proposed method framework.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"233 ","pages":"Pages 52-60"},"PeriodicalIF":4.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692388","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

Optimizing Retinal Imaging: Evaluation of ultrasmall TiO2 nanoparticle- fluorescein conjugates for improved Fundus Fluorescein Angiography 优化视网膜成像：评估超小 TiO2 纳米粒子-荧光素共轭物对改进眼底荧光素血管造影的作用。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-11-19 DOI: 10.1016/j.ymeth.2024.11.012

Marina França Dias , Rodrigo Ken Kawassaki , Lutiana Amaral de Melo , Koiti Araki , Robson Raphael Guimarães , Sílvia Ligorio Fialho

Fundus Fluorescein Angiography (FFA) has been extensively used for the identification, management, and diagnosis of various retinal and choroidal diseases, such as age-related macular degeneration, diabetic retinopathy, retinopathy of prematurity, among others. This exam enables clinicians to evaluate retinal morphology and the pathophysiology of retinal vasculature. However, adverse events, including from mild to severe reactions to sodium fluorescein, have been reported. Titanium dioxide nanoparticles (NPTiO₂) have shown significant potential in numerous biological applications. Coating or conjugating these nanoparticles with small molecules can enhance their stability, photochemical properties, and biocompatibility, as well as increase the hydrophilicity of the nanoparticles, making them more suitable for biomedical applications. This work demonstrates the potential use of ultrasmall titanium dioxide nanoparticles conjugated with sodium fluorescein to improve the quality of angiography exams. The strategy of conjugating fluorescein with NPTiO₂ successfully enhanced the fluorescence photostability of the contrast agent and increased its retention time in the retina. Preliminary in vivo and in vitro safety tests suggest that these nanoparticles are safe for the intended application demonstrating low tendency to hemolysis, and no significant changes in the retina thickness or in the electroretinography a-wave and b-wave amplitudes. Overall, the conjugation of fluorescein to NPTiO₂ has produced a nanomaterial with favorable properties for use as an innovative contrast agent in FFA examinations. By providing a clear description of our methodology of analysis, we also aim to offer better perspectives and reproducible conditions for future research.

眼底荧光素血管造影（FFA）已被广泛用于各种视网膜和脉络膜疾病的识别、管理和诊断，如老年性黄斑变性、糖尿病视网膜病变、早产儿视网膜病变等。临床医生可通过该检查评估视网膜形态和视网膜血管的病理生理学。然而，荧光素钠的不良反应也时有报道，从轻微到严重不等。二氧化钛纳米粒子（NPTiO2）在许多生物应用中都显示出巨大的潜力。在这些纳米粒子上涂覆或共轭小分子可增强其稳定性、光化学特性和生物相容性，还能增加纳米粒子的亲水性，使其更适合生物医学应用。这项研究展示了超小二氧化钛纳米粒子与荧光素钠共轭的潜在用途，以提高血管造影检查的质量。荧光素与 NPTiO2 共轭的策略成功地增强了造影剂的荧光光稳定性，并延长了其在视网膜中的保留时间。初步的体内和体外安全性测试表明，这些纳米颗粒对预期应用是安全的，溶血倾向低，视网膜厚度或视网膜电图 a 波和 b 波振幅无明显变化。总之，荧光素与 NPTiO2 的共轭作用产生了一种具有良好特性的纳米材料，可作为一种创新的造影剂用于 FFA 检查。通过清楚地描述我们的分析方法，我们还希望为未来的研究提供更好的视角和可重复的条件。

{"title":"Optimizing Retinal Imaging: Evaluation of ultrasmall TiO2 nanoparticle- fluorescein conjugates for improved Fundus Fluorescein Angiography","authors":"Marina França Dias , Rodrigo Ken Kawassaki , Lutiana Amaral de Melo , Koiti Araki , Robson Raphael Guimarães , Sílvia Ligorio Fialho","doi":"10.1016/j.ymeth.2024.11.012","DOIUrl":"10.1016/j.ymeth.2024.11.012","url":null,"abstract":"<div><div>Fundus Fluorescein Angiography (FFA) has been extensively used for the identification, management, and diagnosis of various retinal and choroidal diseases, such as age-related macular degeneration, diabetic retinopathy, retinopathy of prematurity, among others. This exam enables clinicians to evaluate retinal morphology and the pathophysiology of retinal vasculature. However, adverse events, including from mild to severe reactions to sodium fluorescein, have been reported. Titanium dioxide nanoparticles (NPTiO<sub>2</sub>) have shown significant potential in numerous biological applications. Coating or conjugating these nanoparticles with small molecules can enhance their stability, photochemical properties, and biocompatibility, as well as increase the hydrophilicity of the nanoparticles, making them more suitable for biomedical applications. This work demonstrates the potential use of ultrasmall titanium dioxide nanoparticles conjugated with sodium fluorescein to improve the quality of angiography exams. The strategy of conjugating fluorescein with NPTiO<sub>2</sub> successfully enhanced the fluorescence photostability of the contrast agent and increased its retention time in the retina. Preliminary <em>in vivo</em> and <em>in vitro</em> safety tests suggest that these nanoparticles are safe for the intended application demonstrating low tendency to hemolysis, and no significant changes in the retina thickness or in the electroretinography a-wave and b-wave amplitudes. Overall, the conjugation of fluorescein to NPTiO<sub>2</sub> has produced a nanomaterial with favorable properties for use as an innovative contrast agent in FFA examinations. By providing a clear description of our methodology of analysis, we also aim to offer better perspectives and reproducible conditions for future research.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"233 ","pages":"Pages 30-41"},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142680419","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

SITP: A single cell bioinformatics analysis flow captures proteasome markers in the development of breast cancer SITP：单细胞生物信息学分析流捕捉乳腺癌发展过程中的蛋白酶体标记。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-11-15 DOI: 10.1016/j.ymeth.2024.11.011

Xue-Jie Zhou , Xiao-Feng Liu , Xin Wang , Xu-Chen Cao

Single cell sequencing and related databases have been widely used in the exploration of cancer occurrence and development, but there is still no in-depth explanation of specific and complicated cellular protein modification processes. Ubiquitin-Proteasome System (UPS), as a specific and precise protein modification and degradation process, plays an important role in the biological functions of cancer cell proliferation and apoptosis. Proteasomes, vital multi-catalytic proteinases in eukaryotic cells, play a crucial role in protein degradation and contribute to tumor regulation. The 26S proteasome, part of the ubiquitin–proteasome system. In this study, we have enrolled a common SITP process including analysis of single cell sequencing to elucidate a flow that can capture typical proteasome markers in the oncogenesis and progression of breast cancer. PSMD11, a key component of the 26S proteasome regulatory particle, has been identified as a critical survival factor in cancer cells. Results suggest that PSMD11’s rapid degradation is linked to acute apoptosis in cancer cells, making it a potential target for cancer treatment. Our study explored the potential mechanisms of PSMD11 in breast cancer development. The findings revealed the feasibility of disclosing ubiquitinating biomarkers from public database, as well as presented new evidence supporting PSMD11 as a potential therapeutic biomarker for breast cancer.

单细胞测序和相关数据库已被广泛应用于癌症发生和发展的探索，但对特异而复杂的细胞蛋白质修饰过程仍没有深入的解释。泛素-蛋白酶体系统（UPS）作为一种特异而精确的蛋白质修饰和降解过程，在癌细胞增殖和凋亡的生物学功能中发挥着重要作用。蛋白酶体是真核细胞中重要的多催化蛋白酶，在蛋白质降解过程中发挥着至关重要的作用，并有助于肿瘤的调控。26S 蛋白酶体是泛素-蛋白酶体系统的一部分。在这项研究中，我们采用了一种常见的 SITP 流程，包括单细胞测序分析，以阐明一种可捕捉乳腺癌肿瘤发生和发展过程中典型蛋白酶体标记物的流程。PSMD11是26S蛋白酶体调控颗粒的关键成分，已被确定为癌细胞中的关键生存因子。研究结果表明，PSMD11的快速降解与癌细胞的急性凋亡有关，使其成为癌症治疗的潜在靶点。我们的研究探索了 PSMD11 在乳腺癌发展中的潜在机制。研究结果揭示了从公共数据库中公开泛素化生物标志物的可行性，并提供了支持PSMD11作为乳腺癌潜在治疗生物标志物的新证据。

{"title":"SITP: A single cell bioinformatics analysis flow captures proteasome markers in the development of breast cancer","authors":"Xue-Jie Zhou , Xiao-Feng Liu , Xin Wang , Xu-Chen Cao","doi":"10.1016/j.ymeth.2024.11.011","DOIUrl":"10.1016/j.ymeth.2024.11.011","url":null,"abstract":"<div><div>Single cell sequencing and related databases have been widely used in the exploration of cancer occurrence and development, but there is still no in-depth explanation of specific and complicated cellular protein modification processes. Ubiquitin-Proteasome System (UPS), as a specific and precise protein modification and degradation process, plays an important role in the biological functions of cancer cell proliferation and apoptosis. Proteasomes, vital multi-catalytic proteinases in eukaryotic cells, play a crucial role in protein degradation and contribute to tumor regulation. The 26S proteasome, part of the ubiquitin–proteasome system. In this study, we have enrolled a common SITP process including analysis of single cell sequencing to elucidate a flow that can capture typical proteasome markers in the oncogenesis and progression of breast cancer. PSMD11, a key component of the 26S proteasome regulatory particle, has been identified as a critical survival factor in cancer cells. Results suggest that PSMD11’s rapid degradation is linked to acute apoptosis in cancer cells, making it a potential target for cancer treatment. Our study explored the potential mechanisms of PSMD11 in breast cancer development. The findings revealed the feasibility of disclosing ubiquitinating biomarkers from public database, as well as presented new evidence supporting PSMD11 as a potential therapeutic biomarker for breast cancer.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"233 ","pages":"Pages 1-10"},"PeriodicalIF":4.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643675","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

Ab-Amy 2.0: Predicting light chain amyloidogenic risk of therapeutic antibodies based on antibody language model Ab-amy 2.0：基于抗体语言模型预测治疗性抗体的轻链淀粉样蛋白致病风险。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-11-15 DOI: 10.1016/j.ymeth.2024.11.005

Yuwei Zhou , Wenwen Liu , Chunmei Luo , Ziru Huang , Gunarathne Samarappuli Mudiyanselage Savini , Lening Zhao , Rong Wang , Jian Huang

Therapeutic antibodies have emerged as a promising treatment option for a wide range of diseases. However, the light chain of antibodies can potentially induce amyloidosis, a condition characterized by protein misfolding and aggregation, posing a significant safety concern. Therefore, it is crucial to assess the amyloidogenic risk of therapeutic antibodies during the early stages of drug development. In this study, we introduce AB-Amy 2.0, a new computational model with enhanced performance for assessing the light chain amyloidogenic risk of therapeutic antibodies. By employing pretrained protein language models (PLMs) embeddings, AB-Amy 2.0 achieves higher accuracy in amyloidogenic risk prediction compared with traditional features offering a crucial tool for early-stage identification of antibodies with low aggregation propensity. The AB-Amy 2.0 was trained on antiBERTy embeddings and utilizes the SVM algorithm, resulting in superior performance metrics. On an independent test dataset, the model achieved high sensitivity, specificity, ACC, MCC and AUC of 93.47%, 89.23%, 91.92%, 0.8261 and 0.9739, respectively. These results highlight the effectiveness and robustness of AB-Amy 2.0 in predicting light chain amyloidogenic risk accurately. To facilitate user-friendly access, we have developed an online web server (http://i.uestc.edu.cn/AB-Amy2) and a command line tool (https://github.com/zzyywww/ABAmy2). These resources enable the broader application of this advanced model and promise to enhance the development of safer therapeutic antibodies.

治疗性抗体已成为治疗多种疾病的理想选择。然而，抗体的轻链有可能诱发淀粉样变性（一种以蛋白质错误折叠和聚集为特征的疾病），从而带来重大的安全隐患。因此，在药物开发的早期阶段评估治疗性抗体的淀粉样变性风险至关重要。在这项研究中，我们介绍了 AB-Amy 2.0，这是一种性能更强的新型计算模型，用于评估治疗性抗体的轻链淀粉样蛋白致病风险。通过使用预训练的蛋白质语言模型（PLMs）嵌入，与传统特征相比，AB-Amy 2.0 在淀粉样蛋白生成风险预测方面实现了更高的准确性，为早期识别低聚集倾向的抗体提供了重要工具。AB-Amy 2.0 采用 SVM 算法，以 antiBERTy 嵌入为基础进行训练，因此性能指标更优越。在独立测试数据集上，该模型的灵敏度、特异性、ACC、MCC 和 AUC 分别达到了 93.47%、89.23%、91.92%、0.8261 和 0.9739 的高水平。这些结果凸显了 AB-Amy 2.0 在准确预测轻链淀粉样变性风险方面的有效性和稳健性。为了方便用户访问，我们开发了一个在线网络服务器（http://i.uestc.edu.cn/AB-Amy2）和一个命令行工具（https://github.com/zzyywww/ABAmy2）。这些资源使这一先进模型得到了更广泛的应用，并有望促进更安全的治疗性抗体的开发。

{"title":"Ab-Amy 2.0: Predicting light chain amyloidogenic risk of therapeutic antibodies based on antibody language model","authors":"Yuwei Zhou , Wenwen Liu , Chunmei Luo , Ziru Huang , Gunarathne Samarappuli Mudiyanselage Savini , Lening Zhao , Rong Wang , Jian Huang","doi":"10.1016/j.ymeth.2024.11.005","DOIUrl":"10.1016/j.ymeth.2024.11.005","url":null,"abstract":"<div><div>Therapeutic antibodies have emerged as a promising treatment option for a wide range of diseases. However, the light chain of antibodies can potentially induce amyloidosis, a condition characterized by protein misfolding and aggregation, posing a significant safety concern. Therefore, it is crucial to assess the amyloidogenic risk of therapeutic antibodies during the early stages of drug development. In this study, we introduce AB-Amy 2.0, a new computational model with enhanced performance for assessing the light chain amyloidogenic risk of therapeutic antibodies. By employing pretrained protein language models (PLMs) embeddings, AB-Amy 2.0 achieves higher accuracy in amyloidogenic risk prediction compared with traditional features offering a crucial tool for early-stage identification of antibodies with low aggregation propensity. The AB-Amy 2.0 was trained on antiBERTy embeddings and utilizes the SVM algorithm, resulting in superior performance metrics. On an independent test dataset, the model achieved high sensitivity, specificity, ACC, MCC and AUC of 93.47%, 89.23%, 91.92%, 0.8261 and 0.9739, respectively. These results highlight the effectiveness and robustness of AB-Amy 2.0 in predicting light chain amyloidogenic risk accurately. To facilitate user-friendly access, we have developed an online web server (<span><span><u>http://i.uestc.edu.cn/AB-Amy2</u></span><svg><path></path></svg></span>) and a command line tool (<span><span><u>https://github.com/zzyywww/ABAmy2</u></span><svg><path></path></svg></span>). These resources enable the broader application of this advanced model and promise to enhance the development of safer therapeutic antibodies.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"233 ","pages":"Pages 11-18"},"PeriodicalIF":4.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643649","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

Data preprocessing methods for selective sweep detection using convolutional neural networks 使用卷积神经网络进行选择性扫频检测的数据预处理方法。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-11-15 DOI: 10.1016/j.ymeth.2024.11.003

Hanqing Zhao, Nikolaos Alachiotis

The identification of positive selection has been framed as a classification task, with Convolutional Neural Networks (CNNs) already outperforming summary statistics and likelihood-based approaches in accuracy. Despite the prevalence of CNN-based methods that manipulate the pixels of images representing raw genomic data as a preprocessing step to improve classification accuracy, the efficacy of these pixel-rearrangement techniques remains inadequately examined, particularly in the presence of confounding factors like population bottlenecks, migration and recombination hotspots. We introduce a set of pixel rearrangement algorithms aimed at enhancing CNN classification accuracy in detecting selective sweeps. These algorithms are employed to assess the performance of four CNN models for selective sweep detection. Our findings illustrate that the judicious application of rearrangement algorithms notably enhances the overall classification accuracy of a CNN across various datasets simulating confounding factors. We observed that sorting the columns of the genomic matrices has higher on CNN performance than rearranging the sequences. To some extent, these rearrangement algorithms are more robust to misspecified demographic models compared with the utilization of the default preprocessing algorithm as suggested by the respective authors of each CNN architecture. We provide the data rearrangement algorithms as a distinct package available for download at: https://github.com/Zhaohq96/Genetic-data-rearrangement.

阳性选择的识别被视为一项分类任务，卷积神经网络（CNN）的准确性已经超过了汇总统计和基于似然法的方法。尽管基于卷积神经网络的方法非常普遍，这些方法通过处理代表原始基因组数据的图像像素作为提高分类准确性的预处理步骤，但这些像素重排技术的功效仍未得到充分检验，尤其是在存在种群瓶颈、迁移和重组热点等混杂因素的情况下。我们介绍了一套像素重排算法，旨在提高 CNN 在检测选择性扫描时的分类准确性。我们利用这些算法评估了四种 CNN 模型在选择性扫描检测方面的性能。我们的研究结果表明，在各种模拟混杂因素的数据集上，合理应用重排算法可显著提高 CNN 的整体分类准确性。我们观察到，对基因组矩阵列进行排序比重新排列序列对 CNN 性能的影响更大。在某种程度上，与使用每个 CNN 架构的作者所建议的默认预处理算法相比，这些重新排列算法对指定错误的人口模型更具鲁棒性。我们将数据重新排列算法作为一个单独的软件包提供给大家下载：https://github.com/Zhaohq96/Genetic-data-rearrangement。

{"title":"Data preprocessing methods for selective sweep detection using convolutional neural networks","authors":"Hanqing Zhao, Nikolaos Alachiotis","doi":"10.1016/j.ymeth.2024.11.003","DOIUrl":"10.1016/j.ymeth.2024.11.003","url":null,"abstract":"<div><div>The identification of positive selection has been framed as a classification task, with Convolutional Neural Networks (CNNs) already outperforming summary statistics and likelihood-based approaches in accuracy. Despite the prevalence of CNN-based methods that manipulate the pixels of images representing raw genomic data as a preprocessing step to improve classification accuracy, the efficacy of these pixel-rearrangement techniques remains inadequately examined, particularly in the presence of confounding factors like population bottlenecks, migration and recombination hotspots. We introduce a set of pixel rearrangement algorithms aimed at enhancing CNN classification accuracy in detecting selective sweeps. These algorithms are employed to assess the performance of four CNN models for selective sweep detection. Our findings illustrate that the judicious application of rearrangement algorithms notably enhances the overall classification accuracy of a CNN across various datasets simulating confounding factors. We observed that sorting the columns of the genomic matrices has higher on CNN performance than rearranging the sequences. To some extent, these rearrangement algorithms are more robust to misspecified demographic models compared with the utilization of the default preprocessing algorithm as suggested by the respective authors of each CNN architecture. We provide the data rearrangement algorithms as a distinct package available for download at: <span><span>https://github.com/Zhaohq96/Genetic-data-rearrangement</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"233 ","pages":"Pages 19-29"},"PeriodicalIF":4.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643673","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