EBIC：一种基于进化的并行模式发现双聚类算法
EBIC: an evolutionary-based parallel biclustering algorithm for pattern discover
摘要：
提出了一种基于人工智能的双聚类算法。这种名为EBIC的方法旨在检测复杂数据中具有生物学意义的、保持顺序的模式。该算法可能是第一个能够在真实基因表达数据集中发现准确率超过50%的多个复杂模式的算法。它也是为数不多的为具有多个图形处理单元（GPU）的并行环境设计的双聚类方法之一。我们证明了EBIC在恢复和相关性方面优于最先进的双聚类方法，无论是合成数据集还是遗传数据集。EBIC产生的结果比最精确的参考算法快12倍以上。该算法预计将被添加到无监督机器学习算法的剧目，用于分析数据集，包括那些从大规模的基因组研究。
英文摘要：
In this paper a novel biclustering algorithm based on artificial intelligence (AI) is introduced. The method called EBIC aims to detect biologically meaningful, order-preserving patterns in complex data. The proposed algorithm is probably the first one capable of discovering with accuracy exceeding 50% multiple complex patterns in real gene expression datasets. It is also one of the very few biclustering methods designed for parallel environments with multiple graphics processing units (GPUs). We demonstrate that EBIC outperforms state-of-the-art biclustering methods, in terms of recovery and relevance, on both synthetic and genetic datasets. EBIC also yields results over 12 times faster than the most accurate reference algorithms. The proposed algorithm is anticipated to be added to the repertoire of unsupervised machine learning algorithms for the analysis of datasets, including those from large-scale genomic studies.
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