A novel deep contrastive convolutional autoencoder based binning approach for taxonomic independent metagenomics data

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Sharanbasappa D. Madival, Girish Kumar Jha, Dwijesh Chandra Mishra, Sunil Kumar, Neeraj Budhlakoti, Anu Sharma, Krishna Kumar Chaturvedi, S. Kabilan, Mohammad Samir Farooqi, Sudhir Srivastava
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引用次数: 0

Abstract

In this study, we present an innovative binning approach for metagenomics data that combines Natural Language Processing (NLP) with a Deep Contrastive Convolutional Autoencoder (DCAE). We used NLP for feature extraction, specifically focusing on Tetra-nucleotide frequency (TNF) through CountVec and (Term Frequency -Inverse Document Frequency) TF-IDF, further enriched by integrating GC-Content into their respective feature matrices. The DCAE, equipped with advanced convolutional layers and a contrastive loss function, excels at capturing intricate patterns in the data, providing a sophisticated representation for binning. By applying k-means clustering to the latent representations obtained from the DCAE, our approach consistently achieves impressive results. To assess the performance of our method, we utilized three standard benchmark metagenomics datasets: 10s, 25s, and Sharon datasets. Across all datasets, we observed Silhouette Indices exceeding 0.6 and Rand Indices surpassing 0.8, demonstrating the superior performance of our proposed method. Compared to existing methodologies, our approach not only surpasses the Rand Index and Silhouette Index of current unsupervised methods but also performs on par with semi-supervised methods across datasets. This underscores the effectiveness and versatility of our approach in metagenomics analysis.

Abstract Image

基于深度对比卷积自动编码器的新颖分选方法,适用于独立于分类的元基因组学数据
在这项研究中,我们针对元基因组学数据提出了一种创新的分选方法,它将自然语言处理(NLP)与深度对比卷积自动编码器(DCAE)相结合。我们使用 NLP 进行特征提取,特别是通过 CountVec 和(术语频率 - 反向文档频率)TF-IDF 重点关注四核苷酸频率 (TNF),并通过将 GC-Content 整合到各自的特征矩阵中进一步丰富特征。DCAE 配备了先进的卷积层和对比损失函数,善于捕捉数据中错综复杂的模式,为分选提供了复杂的表示方法。通过对从 DCAE 中获得的潜在表示进行 k-means 聚类,我们的方法不断取得令人印象深刻的成果。为了评估我们方法的性能,我们使用了三个标准基准元基因组学数据集:10s、25s 和 Sharon 数据集。在所有数据集上,我们观察到剪影指数超过了 0.6,兰德指数超过了 0.8,这表明我们提出的方法具有卓越的性能。与现有方法相比,我们的方法不仅超越了当前无监督方法的兰德指数和轮廓指数,而且在所有数据集上的表现与半监督方法相当。这凸显了我们的方法在元基因组学分析中的有效性和通用性。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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