Journal ArticleDOI
Rough-Fuzzy Clustering for Grouping Functionally Similar Genes from Microarray Data
Pradipta Maji,Sushmita Paul +1 more
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TLDR
An efficient method is proposed to select initial prototypes of different gene clusters, which enables the proposed c-means algorithm to converge to an optimum or near optimum solutions and helps to discover coexpressed gene clusters.Abstract:
Gene expression data clustering is one of the important tasks of functional genomics as it provides a powerful tool for studying functional relationships of genes in a biological process. Identifying coexpressed groups of genes represents the basic challenge in gene clustering problem. In this regard, a gene clustering algorithm, termed as robust rough-fuzzy $(c)$-means, is proposed judiciously integrating the merits of rough sets and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in cluster definition, the integration of probabilistic and possibilistic memberships of fuzzy sets enables efficient handling of overlapping partitions in noisy environment. The concept of possibilistic lower bound and probabilistic boundary of a cluster, introduced in robust rough-fuzzy $(c)$-means, enables efficient selection of gene clusters. An efficient method is proposed to select initial prototypes of different gene clusters, which enables the proposed $(c)$-means algorithm to converge to an optimum or near optimum solutions and helps to discover coexpressed gene clusters. The effectiveness of the algorithm, along with a comparison with other algorithms, is demonstrated both qualitatively and quantitatively on 14 yeast microarray data sets.read more
Citations
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Journal ArticleDOI
Multigranulation rough-fuzzy clustering based on shadowed sets
TL;DR: By integrating the notions of shadowed sets and multigranulation into rough-fuzzy clustering approaches, the overall topology of data can be captured well and the uncertain information implicated inData can be effectively addressed, including the uncertainty generated by fuzzification coefficient.
Journal ArticleDOI
Gene selection for tumor classification using neighborhood rough sets and entropy measures
TL;DR: A novel gene selection method based on the neighborhood rough set model is proposed, which has the ability of dealing with real-value data whilst maintaining the original gene classification information, and an entropy measure is addressed under the frame of neighborhood rough sets for tackling the uncertainty and noisy of gene expression data.
Journal ArticleDOI
Adaptive fuzzy consensus clustering framework for clustering analysis of cancer data
TL;DR: Experiments on real cancer gene expression profiles indicate that R DCFCE and A-RDCFCE works well on these data sets, and outperform most of the state-of-the-art tumor clustering algorithms.
Journal ArticleDOI
Detecting Overlapping Protein Complexes by Rough-Fuzzy Clustering in Protein-Protein Interaction Networks
TL;DR: A novel rough-fuzzy clustering method to detect overlapping protein complexes in protein-protein interaction (PPI) networks and provides a new insight of network division, and it can also be applied to identify overlapping community structure in social networks and LFR benchmark networks.
Journal ArticleDOI
Cross-domain, soft-partition clustering with diversity measure and knowledge reference
Pengjiang Qian,Shouwei Sun,Yizhang Jiang,Kuan-Hao Su,Tongguang Ni,Shitong Wang,Raymond F. Muzic +6 more
TL;DR: The quadratic weights and Gini-Simpson diversity based fuzzy clustering model (QWGSD-FC), is first proposed as a basis of this work and two types of cross-domain, soft-partition clustering frameworks and their corresponding algorithms, referred to as type-I/type-II knowledge-transfer-oriented c-means (TI-KT-CM and TII-KT
References
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Journal ArticleDOI
Exploring Expression Data: Identification and Analysis of Coexpressed Genes
TL;DR: A similarity measure that reduces the number of false positives, a new clustering algorithm designed specifically for grouping gene expression patterns, and an interactive graphical cluster analysis tool that allows user feedback and validation are described.
Journal ArticleDOI
Some new indexes of cluster validity
James C. Bezdek,Nikhil R. Pal +1 more
TL;DR: This work reviews two clustering algorithms and three indexes of crisp cluster validity and shows that while Dunn's original index has operational flaws, the concept it embodies provides a rich paradigm for validation of partitions that have cloud-like clusters.
Journal ArticleDOI
Pattern Recognition with Fuzzy Objective Function Algorithms (James C. Bezdek)
Journal ArticleDOI
Model-based clustering and data transformations for gene expression data.
TL;DR: The model-based approach has superior performance on synthetic data sets, consistently selecting the correct model and the number of clusters, and the validity of the Gaussian mixture assumption on different transformations of real data is explored.
Book
Yeast Physiology and Biotechnology
TL;DR: Introduction to Yeast Cytology, Yeast Nutrition, and Yeast Metabolism.