通过多层次特征对比和匹配实现战略性多传感器数据整合

IF 3.7 4区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS IEEE Transactions on NanoBioscience Pub Date : 2024-09-10 DOI:10.1109/TNB.2024.3456797
Jinli Zhang;Hongwei Ren;Zongli Jiang;Zheng Chen;Ziwei Yang;Yasuko Matsubara;Yasushi Sakurai
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引用次数: 0

摘要

多组学数据的分析和理解已成为生物信息学和数据科学领域的一个重要课题。然而,omics 数据的稀疏性和高维性给提取有意义的信息带来了困难。为了应对这些挑战,我们提出了基于自我关注的多级特征对比聚类模型 MFCC-SAtt,以从多组学数据中提取信息特征。MFCC-SAtt 将每种 omics 类型视为一种不同的模态,并针对每种模态采用具有自我注意功能的自动编码器,将它们各自的特征整合并压缩到一个共享特征空间中。通过利用多层次特征提取框架和语义信息提取器,我们缓解了不同学习目标带来的优化冲突。此外,MFCC-SAtt 还能引导基于多层次特征的深度聚类,从而进一步提高输出标签的质量。通过在多组学数据上进行大量实验,我们验证了 MFCC-SAtt 的卓越性能。例如,在泛癌症聚类任务中,MFCC-SAtt 的准确率超过了 80.38%。
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Strategic Multi-Omics Data Integration via Multi-Level Feature Contrasting and Matching
The analysis and comprehension of multi-omics data has emerged as a prominent topic in the field of bioinformatics and data science. However, the sparsity characteristics and high dimensionality of omics data pose difficulties in terms of extracting meaningful information. Moreover, the heterogeneity inherent in multiple omics sources makes the effective integration of multi-omics data challenging To tackle these challenges, we propose MFCC-SAtt, a multi-level feature contrast clustering model based on self-attention to extract informative features from multi-omics data. MFCC-SAtt treats each omics type as a distinct modality and employs autoencoders with self-attention for each modality to integrate and compress their respective features into a shared feature space. By utilizing a multi-level feature extraction framework along with incorporating a semantic information extractor, we mitigate optimization conflicts arising from different learning objectives. Additionally, MFCC-SAtt guides deep clustering based on multi-level features which further enhances the quality of output labels. By conducting extensive experiments on multi-omics data, we have validated the exceptional performance of MFCC-SAtt. For instance, in a pan-cancer clustering task, MFCC-SAtt achieved an accuracy of over 80.38%.
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来源期刊
IEEE Transactions on NanoBioscience
IEEE Transactions on NanoBioscience 工程技术-纳米科技
CiteScore
7.00
自引率
5.10%
发文量
197
审稿时长
>12 weeks
期刊介绍: The IEEE Transactions on NanoBioscience reports on original, innovative and interdisciplinary work on all aspects of molecular systems, cellular systems, and tissues (including molecular electronics). Topics covered in the journal focus on a broad spectrum of aspects, both on foundations and on applications. Specifically, methods and techniques, experimental aspects, design and implementation, instrumentation and laboratory equipment, clinical aspects, hardware and software data acquisition and analysis and computer based modelling are covered (based on traditional or high performance computing - parallel computers or computer networks).
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Electrospun Stannic Oxide Nanofiber Thin-Film Based Sensing Device for Monitoring Functional Behaviours of Adherent Mammalian Cells. "Galaxy" encoding: toward high storage density and low cost. 2024 Index IEEE Transactions on NanoBioscience Vol. 23 Table of Contents Front Cover
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