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首页> 外文会议>International conference on communications, signal processing, and systems >Integrating Heterogeneous Datasets by Using Multimodal Deep Learning
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Integrating Heterogeneous Datasets by Using Multimodal Deep Learning

机译：使用多模式深度学习整合异构数据集

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摘要

Rapid collection of data sources, varying in volume and structure poses a challenge for scientists to establish a practical approach to manipulating heterogeneous data sources. A multimodal learning and an integrated analysis make it possible to extract much worthwhile information from a collection of multiple simple raw data. Therefore, data integration can lead to a more reliable and robust result. High-throughput sequencing technologies, especially next-generation sequencing, leave us with multi-platform genomic data such as gene expression, SNP, CNV, DNA methylation, and miRNA expression. In this paper, we represented a multimodal deep neural network to exploit the mutual information between three different modalities to classify breast cancer patients into two groups based on their survival rate. Experimental results indicate that our method improves the classification accuracy and performs better on imbalanced data compared to the other single-modal state-of-the-art methods.
机译：快速收集数量和结构各异的数据源，对科学家们提出了挑战，要求他们建立一种实用的方法来处理异构数据源。多模式学习和集成分析使从多个简单原始数据的集合中提取大量有价值的信息成为可能。因此，数据集成可以导致更可靠，更可靠的结果。高通量测序技术，尤其是下一代测序，为我们提供了多平台基因组数据，例如基因表达，SNP，CNV，DNA甲基化和miRNA表达。在本文中，我们代表了一个多模式深度神经网络，以利用三种不同模式之间的相互信息，根据乳腺癌患者的生存率将其分为两组。实验结果表明，与其他单模式最新技术相比，我们的方法提高了分类准确性，并且在不平衡数据上表现更好。

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