反应性GRASP在基因表达数据双聚类中的应用

Q2 Medicine
Shyama Das, S. M. Idicula
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引用次数: 7

摘要

基因表达数据集中的双聚类是通过一组条件表现出相似表达模式的基因子集。在本工作中,分两个步骤确定双聚类。第一步,使用KMeans聚类算法生成高质量的双聚类种子。然后使用反应贪婪随机自适应搜索程序(RGRASP)对这些种子进行扩展,RGRASP是一种多起点元启发式方法,其中有两个阶段:构建和局部搜索。这里的目标是识别MSR低于给定阈值的最大大小的双聚类。实验在酵母和人类淋巴瘤数据集上进行。在基准数据集上的实验结果表明,与许多现有的双聚类算法相比,RGRASP能够识别高质量的双聚类。与基于相同RGRASP元启发式的现有算法相比,该算法在酵母数据集上获得了更大尺寸和更低均方残差的双聚类。此外,在本研究中,RGRASP首次应用于从人类淋巴瘤数据集中发现双聚类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of reactive GRASP to the biclustering of gene expression data
A bicluster in gene expression dataset is a subset of genes that exhibit similar expression patterns through a subset of conditions. In this work biclusters are identified in two steps. In the first step high quality bicluster seeds are generated using KMeans clustering algorithm. These seeds are then enlarged using Reactive Greedy Randomized Adaptive Search Procedure (RGRASP) which is a multi-start metaheuristic method in which there are two phases, construction and local search. The objective here is to identify biclusters of maximum size with MSR lower than a given threshold. Experiments are conducted on both Yeast and Human Lymphoma datasets. The Experimental results on the benchmark datasets demonstrate that RGRASP is capable of identifying high quality biclusters compared to many of the already existing biclustering algorithms. Compared to the already existing algorithm based on the same RGRASP metaheuristics biclusters with larger size and lower mean squared residue are obtained using this algorithm in Yeast dataset. Moreover in this study the RGRASP is applied for the first time to find biclusters from the Human Lymphoma dataset.
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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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