Clustering cancer gene expression data: a comparative study
نویسندگان
چکیده
منابع مشابه
A Comparative Study of Some Clustering Algorithms on Shape Data
Recently, some statistical studies have been done using the shape data. One of these studies is clustering shape data, which is the main topic of this paper. We are going to study some clustering algorithms on shape data and then introduce the best algorithm based on accuracy, speed, and scalability criteria. In addition, we propose a method for representing the shape data that facilitates and ...
متن کاملComparative Study of Clustering Techniques for Gene Expression Microarray Data
Scientists can now monitor on a genomic scale the patterns of gene expression under varying environmental conditions. With this rapidly growing wealth of information comes the need for organizing and analyzing the data. One natural approach is to group together genes with similar patterns of expression. Several approaches have suggested various alternatives for similarity metrics and clustering...
متن کاملClustering cancer gene expression data by projective clustering ensemble
Gene expression data analysis has paramount implications for gene treatments, cancer diagnosis and other domains. Clustering is an important and promising tool to analyze gene expression data. Gene expression data is often characterized by a large amount of genes but with limited samples, thus various projective clustering techniques and ensemble techniques have been suggested to combat with th...
متن کاملRecent Advances in Gene Expression Data Clustering: A Case Study with Comparative Results
Several advanced techniques have been proposed for data clustering and many of them have been applied to gene expression data, with partial success. The high dimensionality and the multitude of admissible perspectives for data analysis of gene expression require additional computational resources, such as hierarchical structures and dynamic allocation of resources. We present an immune-inspired...
متن کاملEXCLUVIS: A MATLAB GUI Software for Comparative Study of Clustering and Visualization of Gene Expression Data
The result of one clustering algorithm varies from that of another for the same input dataset as the input parameters of an algorithms can substantially affect the behavior and execution of the algorithms. Cluster validity measures can be used to find the partitioning that best fits the underlying data. In most realistic applications, this analysis can be visualized using simple Computer-Aided-...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2008
ISSN: 1471-2105
DOI: 10.1186/1471-2105-9-497