IF 4.2 3区生物学

Methods Pub Date : 2025-02-01 DOI: 10.1016/j.ymeth.2025.01.004

Junfang Li , Yifei Zhang , Qiu Yang , Yi Qu

{"title":"Integrated analyses of prognostic and immunotherapeutic significance of EZH2 in uveal melanoma","authors":"Junfang Li , Yifei Zhang , Qiu Yang , Yi Qu","doi":"10.1016/j.ymeth.2025.01.004","DOIUrl":"10.1016/j.ymeth.2025.01.004","url":null,"abstract":"<div><div>The EZH2 expression shows significantly associated with immunotherapeutic resistance in several tumors. A comprehensive analysis of the predictive values of EZH2 for immune checkpoint blockade (ICB) effectiveness in uveal melanoma (UM) remains unclear. We analyzed UM data from The Cancer Genome Atlas (TCGA) database, identified 888 differentially expressed genes (DEGs) associated with EZH2 expression, then conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses to elucidate biological features of EZH2 in UM assays. The correlation of the expression of EZH2 with tumor immunity related factors such as immune-related pathways, infiltration of various immune cells, immune score and immune checkpoints were explored. The evaluation of EZH2′s capability to predict immune therapy outcomes in UM was assessed by incorporating the Tumor Immune Dysfunction and Exclusion (TIDE) score. Lastly, programmed death-ligand 1 (PD-L1) expression was detected in an independent UM patient cohort by immunohistochemical analyses, the correlation of EZH2 with PD-L1 was evaluated. Results highlighted that the EZH2 expression was correlated with immune-related pathways, infiltration of various immune cells, immune score, the expression of immune checkpoints and immunotherapy sensitivity. Collectively, we suggested that EZH2 might be considered as predictor on the therapeutic effects of ICBs on UM patients, and a potential target for combined immunotherapy.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 242-252"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142942260","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

Predicting cyclins based on key features and machine learning methods 基于关键特征和机器学习方法预测周期蛋白。

IF 4.2 3区生物学

Methods Pub Date : 2025-02-01 DOI: 10.1016/j.ymeth.2024.12.009

Cheng-Yan Wu , Zhi-Xue Xu , Nan Li , Dan-Yang Qi , Hong-Ye Wu , Hui Ding , Yan-Ting Jin

{"title":"Predicting cyclins based on key features and machine learning methods","authors":"Cheng-Yan Wu , Zhi-Xue Xu , Nan Li , Dan-Yang Qi , Hong-Ye Wu , Hui Ding , Yan-Ting Jin","doi":"10.1016/j.ymeth.2024.12.009","DOIUrl":"10.1016/j.ymeth.2024.12.009","url":null,"abstract":"<div><div>Cyclins are a group of proteins that regulate the cell cycle process by modulating various stages of cell division to ensure correct cell proliferation, differentiation, and apoptosis. Research on cyclins is crucial for understanding the biological functions and pathological states of cells. However, current research on cyclin identification based on machine learning only focuses on accuracy ignoring the interpretability of features. Therefore, in this study, we pay more attention to the interpretation and analysis of key features associated with cyclins. Firstly, we developed an SVM-based model for identifying cyclins with an accuracy of 92.8% through 5-fold. Then we analyzed the physicochemical properties of the 14 key features used in the model construction and identified the G and charged C1 features that are critical for distinguishing cyclins from non-cyclins. Furthermore, we constructed an SVM-based model using only these two features with an accuracy of 81.3% through the leave-one-out cross-validation. Our study shows that cyclins differ from non-cyclins in their physicochemical properties and that using only two features can achieve good prediction accuracy.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 112-119"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851969","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

Ins-ATP: Deep estimation of ATP for organoid based on high throughput microscope images Ins-ATP：基于高通量显微镜图像的类器官ATP深度估计。

IF 4.2 3区生物学

Methods Pub Date : 2025-01-31 DOI: 10.1016/j.ymeth.2025.01.012

Xuesheng Bian , Shuting Chen , Weiquan Liu

{"title":"Ins-ATP: Deep estimation of ATP for organoid based on high throughput microscope images","authors":"Xuesheng Bian , Shuting Chen , Weiquan Liu","doi":"10.1016/j.ymeth.2025.01.012","DOIUrl":"10.1016/j.ymeth.2025.01.012","url":null,"abstract":"<div><div>Adenosine triphosphate (ATP) is a high-energy phosphate compound, the most direct energy source in organisms. ATP is an important biomarker for evaluating cell viability in biology. Researchers often use ATP bioluminescence to measure the ATP of organoid after drug to evaluate the drug efficacy. However, ATP bioluminescence has limitations, leading to unreliable drug screening results. ATP bioluminescence measurement requires the lysis of organoid cells, making it impossible to continuously monitor the long-term viability changes of organoids after drug administration. To overcome the disadvantages of ATP bioluminescence, we propose Ins-ATP, a non-invasive strategy, the first organoid ATP estimation model based on the high-throughput microscope image. Ins-ATP directly estimates the ATP of organoids from high-throughput microscope images so that it does not influence the drug reactions of organoids. Therefore, the ATP change of organoids can be observed for a long time to obtain more stable results. Experimental results show that the ATP estimation by Ins-ATP is in good agreement with those determined by ATP bioluminescence. Specifically, the predictions of Ins-ATP are consistent with the results measured by ATP bioluminescence in the efficacy evaluation experiments of different drugs.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"235 ","pages":"Pages 34-44"},"PeriodicalIF":4.2,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143073381","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

ZeRPI: A graph neural network model for zero-shot prediction of RNA-protein interactions ZeRPI：用于rna -蛋白质相互作用零射击预测的图神经网络模型。

IF 4.2 3区生物学

Methods Pub Date : 2025-01-30 DOI: 10.1016/j.ymeth.2025.01.014

Yifei Gao , Runhan Shi , Gufeng Yu , Yuyang Huang , Yang Yang

{"title":"ZeRPI: A graph neural network model for zero-shot prediction of RNA-protein interactions","authors":"Yifei Gao , Runhan Shi , Gufeng Yu , Yuyang Huang , Yang Yang","doi":"10.1016/j.ymeth.2025.01.014","DOIUrl":"10.1016/j.ymeth.2025.01.014","url":null,"abstract":"<div><div>RNA-protein interactions are crucial for biological functions across multiple levels. RNA binding proteins (RBPs) intricately engage in diverse biological processes through specific RNA molecule interactions. Previous studies have revealed the indispensable role of RBPs in both health and disease development. With the increase of experimental data, machine-learning methods have been widely used to predict RNA-protein interactions. However, most current methods either train models for individual RBPs or develop multi-task models for a fixed set of multiple RBPs. These approaches are incapable of predicting interactions with previously unseen RBPs. In this study, we present ZeRPI, a zero-shot method for predicting RNA-protein interactions. Based on a graph neural network model, ZeRPI integrates RNA and protein information to generate detailed representations, using a novel loss function based on contrastive learning principles to augment the alignment between interacting pairs in feature space. ZeRPI demonstrates competitive performance in predicting RNA-protein interactions across a wide array of RBPs. Notably, our model exhibits remarkable versatility in accurately predicting interactions for unseen RBPs, demonstrating its capacity to transfer knowledge learned from known RBPs.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"235 ","pages":"Pages 45-52"},"PeriodicalIF":4.2,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143073378","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

CSSEC: An adaptive approach integrating consensus and specific self-expressive coefficients for multi-omics cancer subtyping CSSEC：一种整合共识和特定自我表达系数的适应性方法，用于多组学癌症亚型分型。

IF 4.2 3区生物学

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

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

{"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 LiteMamba-Bound：一种基于边界感知和归一化活动轮廓损失的轻量级mamba模型，用于皮肤损伤分割。

IF 4.2 3区生物学

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

{"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 MPEMDA：一种具有预补全和误差校正的多相似性集成方法用于预测微生物与药物的关联。

IF 4.2 3区生物学

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

Yuxiang Li , Haochen Zhao , Jianxin Wang

{"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 基于平稳波动分析的间隙结通道的统一电导评价。

IF 4.2 3区生物学

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

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

{"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

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

{"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

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

Mengke Guo, Xiucai Ye, Dong Huang, Tetsuya Sakurai

{"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

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