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Implementation of BiClusO and its comparison with other biclustering algorithms

机译:BiClusO的实现及其与其他双簇算法的比较

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Abstract This paper describes the implementation of biclustering algorithm BiClusO using graphical user interface and different parameters to generate overlapping biclusters from a binary sparse matrix. We compare our algorithm with several other biclustering algorithms in the context of two different types of biological datasets and four synthetic datasets with known embedded biclusters. Biclustering technique is widely used in different fields of studies for analyzing bipartite relationship dataset. Over the past decade, different biclustering algorithms have been proposed by researchers which are mainly used for biological data analysis. The performance of these algorithms differs depending on dataset size, pattern, and property. These issues create difficulties for a researcher to take the right decision for selecting a good biclustering algorithm. Two different scoring methods along with Gene Ontology(GO) term enrichment analysis have been used to measure and compare the performance of our algorithm. Our algorithm shows the best performance over some other well-known biclustering algorithms.
机译:摘要本文描述了使用图形用户界面和不同参数从二进制稀疏矩阵生成重叠的二元组的二元组算法BiClusO的实现。我们在两种不同类型的生物学数据集和具有已知嵌入式二类聚类的四个合成数据集的背景下,将我们的算法与其他几种双类聚类算法进行了比较。混群技术已广泛用于不同研究领域,以分析二方关系数据集。在过去的十年中,研究人员提出了不同的二类聚类算法,这些算法主要用于生物数据分析。这些算法的性能取决于数据集的大小,模式和属性。这些问题使研究人员难以做出正确的决定,以选择好的双聚类算法。两种不同的评分方法以及基因本体(GO)术语丰富化分析已用于测量和比较我们算法的性能。与其他一些著名的双聚类算法相比,我们的算法显示出最佳性能。

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