Paradigm of tunable clustering using binarization of consensus partition matrices (Bi-CoPaM) for gene discovery

Copyright @ 2013 Abu-Jamous et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. In this paper, a new clustering paradigm is proposed. In this paradigm, all three eventualities of a gene being exclusively assigned to a single cluster, being assigned to multiple clusters, and being not assigned to any cluster are possible. These possibilities are realised through the primary novelty of the introduction of tunable binarization techniques. Results from multiple clustering experiments are aggregated to generate one fuzzy consensus partition matrix (CoPaM), which is then binarized to obtain the final binary partitions. This is referred to as Binarization of Consensus Partition Matrices (Bi-CoPaM). The method has been tested with a set of synthetic datasets and a set of five real yeast cell-cycle datasets. The results demonstrate its validity in generating relevant tight, wide, and complementary clusters that can meet requirements of different gene discovery studies.National Institute for Health Researc

Turnover of BRCA1 Involves in Radiation-Induced Apoptosis

Background: Germ-line mutations of the breast cancer susceptibility gene-1 (BRCA1) increase the susceptibility to tumorigenesis. The function of BRCA1 is to regulate critical cellular processes, including cell cycle progression, genomic integrity, and apoptosis. Studies on the regulation of BRCA1 have focused intensely on transcription and phosphorylation mechanisms. Proteolytic regulation of BRCA1 in response to stress signaling remains largely unknown. The manuscript identified a novel mechanism by which BRCA1 is regulated by the ubiquitin-dependent degradation in response to ionization. Methodology/Principal Findings: Here, we report that severe ionization triggers rapid degradation of BRCA1, which in turn results in the activation of apoptosis. Ionization-induced BRCA1 turnover is mediated via an ubiquitin-proteasomal pathway. The stabilization of BRCA1 significantly delays the onset of ionization-induced apoptosis. We have mapped the essential region on BRCA1, which mediates its proteolysis in response to ionization. Moreover, we have demonstrated that BRCA1 protein is most sensitive to degradation when ionization occurs during G2/M and S phase. Conclusions/Significance: Our results suggest that ubiquitin-proteasome plays an important role in regulating BRCA1 during genotoxic stress. Proteolytic regulation of BRCA1 involves in ionization-induced apoptosis. © 2010 Liu et al

Voting-based consensus clustering for combining multiple clusterings of chemical structures

<p>Abstract</p> <p>Background</p> <p>Although many consensus clustering methods have been successfully used for combining multiple classifiers in many areas such as machine learning, applied statistics, pattern recognition and bioinformatics, few consensus clustering methods have been applied for combining multiple clusterings of chemical structures. It is known that any individual clustering method will not always give the best results for all types of applications. So, in this paper, three voting and graph-based consensus clusterings were used for combining multiple clusterings of chemical structures to enhance the ability of separating biologically active molecules from inactive ones in each cluster.</p> <p>Results</p> <p>The cumulative voting-based aggregation algorithm (CVAA), cluster-based similarity partitioning algorithm (CSPA) and hyper-graph partitioning algorithm (HGPA) were examined. The F-measure and Quality Partition Index method (QPI) were used to evaluate the clusterings and the results were compared to the Ward’s clustering method. The MDL Drug Data Report (MDDR) dataset was used for experiments and was represented by two 2D fingerprints, ALOGP and ECFP_4. The performance of voting-based consensus clustering method outperformed the Ward’s method using F-measure and QPI method for both ALOGP and ECFP_4 fingerprints, while the graph-based consensus clustering methods outperformed the Ward’s method only for ALOGP using QPI. The Jaccard and Euclidean distance measures were the methods of choice to generate the ensembles, which give the highest values for both criteria.</p> <p>Conclusions</p> <p>The results of the experiments show that consensus clustering methods can improve the effectiveness of chemical structures clusterings. The cumulative voting-based aggregation algorithm (CVAA) was the method of choice among consensus clustering methods.</p

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