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摘要翻译：
子空间聚类在基因表达数据分析中得到了越来越广泛的应用。在子空间聚类模型中，最近引入的保序子矩阵(OPSM)具有很高的应用前景。OPSM本质上是一个基于模式的子空间簇，是数据矩阵中的行和列的子集，对于这些行和列，所有行都诱导相同的列线性排序。现有的OPSM发现方法不能很好地扩展到越来越大的表达式数据集。特别是，小树枝簇的基因少，实验多，计算量大，用现有的方法可以完全剪枝。然而，确定在许多条件下紧密共调控的小群基因是特别有趣的。本文提出了一种OPSM子空间聚类算法KiWi，该算法可扩展到大规模数据集，能够发现树枝簇，识别负相关和正相关。我们使用相关的生物学数据集对KiWi进行了广泛的验证，并表明KiWi正确地将冗余探针分配到相同的聚类中，用常见的临床注释对实验进行分组，区分真实启动子序列和阴性对照序列，并显示出与顺式调节基序预测的良好关联。
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英文标题：
《KiWi: A Scalable Subspace Clustering Algorithm for Gene Expression
Analysis》
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作者：
Obi L. Griffith, Byron J. Gao, Mikhail Bilenky, Yuliya Prichyna,
Martin Ester, Steven J.M. Jones
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最新提交年份：
2009
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Databases 数据库
分类描述：Covers database management, datamining, and data processing. Roughly includes material in ACM Subject Classes E.2, E.5, H.0, H.2, and J.1.
涵盖数据库管理、数据挖掘和数据处理。大致包括ACM学科类E.2、E.5、H.0、H.2和J.1中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Quantitative Biology 数量生物学
二级分类：Genomics 基因组学
分类描述：DNA sequencing and assembly; gene and motif finding; RNA editing and alternative splicing; genomic structure and processes (replication, transcription, methylation, etc); mutational processes.
DNA测序与组装；基因和基序的发现；RNA编辑和选择性剪接；基因组结构和过程（复制、转录、甲基化等）；突变过程。
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英文摘要：
Subspace clustering has gained increasing popularity in the analysis of gene expression data. Among subspace cluster models, the recently introduced order-preserving sub-matrix (OPSM) has demonstrated high promise. An OPSM, essentially a pattern-based subspace cluster, is a subset of rows and columns in a data matrix for which all the rows induce the same linear ordering of columns. Existing OPSM discovery methods do not scale well to increasingly large expression datasets. In particular, twig clusters having few genes and many experiments incur explosive computational costs and are completely pruned off by existing methods. However, it is of particular interest to determine small groups of genes that are tightly coregulated across many conditions. In this paper, we present KiWi, an OPSM subspace clustering algorithm that is scalable to massive datasets, capable of discovering twig clusters and identifying negative as well as positive correlations. We extensively validate KiWi using relevant biological datasets and show that KiWi correctly assigns redundant probes to the same cluster, groups experiments with common clinical annotations, differentiates real promoter sequences from negative control sequences, and shows good association with cis-regulatory motif predictions.
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PDF链接：
https://arxiv.org/pdf/0904.1931
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