Multi-objective clustering ensemble for gene expression data analysis

In this paper, we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is based on a Pareto-based multi-objective genetic algorithm, with a special crossover operator, which uses clustering validation measures as objective functions. The algorithm proposed can deal with data sets presenting different types of clusters, without the need of expertise in cluster analysis. its result is a concise set of partitions representing alternative trade-offs among the objective functions. We compare the results obtained with our algorithm, in the context of gene expression data sets, to those achieved with multi-objective Clustering with automatic K-determination (MOCK). the algorithm most closely related to ours. (C) 2009 Elsevier B.V. All rights reserved.

Evolutionary fuzzy clustering of relational data

This paper is concerned with the computational efficiency of fuzzy clustering algorithms when the data set to be clustered is described by a proximity matrix only (relational data) and the number of clusters must be automatically estimated from such data. A fuzzy variant of an evolutionary algorithm for relational clustering is derived and compared against two systematic (pseudo-exhaustive) approaches that can also be used to automatically estimate the number of fuzzy clusters in relational data. An extensive collection of experiments involving 18 artificial and two real data sets is reported and analyzed. (C) 2011 Elsevier B.V. All rights reserved.

Investigation of a new GRASP-based clustering algorithm applied to biological data

A large amount of biological data has been produced in the last years. Important knowledge can be extracted from these data by the use of data analysis techniques. Clustering plays an important role in data analysis, by organizing similar objects from a dataset into meaningful groups. Several clustering algorithms have been proposed in the literature. However, each algorithm has its bias, being more adequate for particular datasets. This paper presents a mathematical formulation to support the creation of consistent clusters for biological data. Moreover. it shows a clustering algorithm to solve this formulation that uses GRASP (Greedy Randomized Adaptive Search Procedure). We compared the proposed algorithm with three known other algorithms. The proposed algorithm presented the best clustering results confirmed statistically. (C) 2009 Elsevier Ltd. All rights reserved.

Evaluation of Quantitative RT-PCR Using Nonamplified and Amplified RNA

Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) is a standard assay in molecular medicine for gene expression analysis. Samples from incisional/needle biopsies, laser-microdissected tumor cells and other biologic sources, normally available in clinical cancer studies, generate very small amounts of RNA that are restrictive for expression analysis. As a consequence, an RNA amplification procedure is required to assess the gene expression levels of such sample types. The reproducibility and accuracy of relative gene expression data produced by sensitive methodology as qRT-PCR when cDNA converted from amplified (A) RNA is used as template has not yet been properly addressed. In this study, to properly evaluate this issue, we performed 1 round of linear RNA amplification in 2 breast cell lines (C5.2 and HB4a) and assessed the relative expression of 34 genes using cDNA converted from both nonamplified (NA) and A RNA. Relative gene expression was obtained from beta actin or glyceraldehyde 3-phosphate dehydrogenase normalized data using different dilutions of cDNA, wherein the variability and fold-change differences in the expression of the 2 methods were compared. Our data showed that 1 round of linear RNA amplification, even with suboptimal-quality RNA, is appropriate to generate reproducible and high-fidelity qRT-PCR relative expression data that have similar confidence levels as those from NA samples. The use of cDNA that is converted from both A and NA RNA in a single qRT-PCR experiment clearly creates bias in relative gene expression data.

Efficiency issues of evolutionary k-means

One of the top ten most influential data mining algorithms, k-means, is known for being simple and scalable. However, it is sensitive to initialization of prototypes and requires that the number of clusters be specified in advance. This paper shows that evolutionary techniques conceived to guide the application of k-means can be more computationally efficient than systematic (i.e., repetitive) approaches that try to get around the above-mentioned drawbacks by repeatedly running the algorithm from different configurations for the number of clusters and initial positions of prototypes. To do so, a modified version of a (k-means based) fast evolutionary algorithm for clustering is employed. Theoretical complexity analyses for the systematic and evolutionary algorithms under interest are provided. Computational experiments and statistical analyses of the results are presented for artificial and text mining data sets. (C) 2010 Elsevier B.V. All rights reserved.

Towards improving cluster-based feature selection with a simplified silhouette filter

This paper proposes a filter-based algorithm for feature selection. The filter is based on the partitioning of the set of features into clusters. The number of clusters, and consequently the cardinality of the subset of selected features, is automatically estimated from data. The computational complexity of the proposed algorithm is also investigated. A variant of this filter that considers feature-class correlations is also proposed for classification problems. Empirical results involving ten datasets illustrate the performance of the developed algorithm, which in general has obtained competitive results in terms of classification accuracy when compared to state of the art algorithms that find clusters of features. We show that, if computational efficiency is an important issue, then the proposed filter May be preferred over their counterparts, thus becoming eligible to join a pool of feature selection algorithms to be used in practice. As an additional contribution of this work, a theoretical framework is used to formally analyze some properties of feature selection methods that rely on finding clusters of features. (C) 2011 Elsevier Inc. All rights reserved.

On comparing two sequences of numbers and its applications to clustering analysis

A conceptual problem that appears in different contexts of clustering analysis is that of measuring the degree of compatibility between two sequences of numbers. This problem is usually addressed by means of numerical indexes referred to as sequence correlation indexes. This paper elaborates on why some specific sequence correlation indexes may not be good choices depending on the application scenario in hand. A variant of the Product-Moment correlation coefficient and a weighted formulation for the Goodman-Kruskal and Kendall`s indexes are derived that may be more appropriate for some particular application scenarios. The proposed and existing indexes are analyzed from different perspectives, such as their sensitivity to the ranks and magnitudes of the sequences under evaluation, among other relevant aspects of the problem. The results help suggesting scenarios within the context of clustering analysis that are possibly more appropriate for the application of each index. (C) 2008 Elsevier Inc. All rights reserved.

Quality indices for (practical) clustering evaluation

Clustering quality or validation indices allow the evaluation of the quality of clustering in order to support the selection of a specific partition or clustering structure in its natural unsupervised environment, where the real solution is unknown or not available. In this paper, we investigate the use of quality indices mostly based on the concepts of clusters` compactness and separation, for the evaluation of clustering results (partitions in particular). This work intends to offer a general perspective regarding the appropriate use of quality indices for the purpose of clustering evaluation. After presenting some commonly used indices, as well as indices recently proposed in the literature, key issues regarding the practical use of quality indices are addressed. A general methodological approach is presented which considers the identification of appropriate indices thresholds. This general approach is compared with the simple use of quality indices for evaluating a clustering solution.

A fast and robust statistical test based on likelihood ratio with Bartlett correction to identify Granger causality between gene sets

We propose a likelihood ratio test ( LRT) with Bartlett correction in order to identify Granger causality between sets of time series gene expression data. The performance of the proposed test is compared to a previously published bootstrapbased approach. LRT is shown to be significantly faster and statistically powerful even within non- Normal distributions. An R package named gGranger containing an implementation for both Granger causality identification tests is also provided.

The Arabidopsis bZIP Gene AtbZIP63 Is a Sensitive Integrator of Transient Abscisic Acid and Glucose Signals

Glucose modulates plant metabolism, growth, and development. In Arabidopsis (Arabidopsis thaliana), Hexokinase1 (HXK1) is a glucose sensor that may trigger abscisic acid (ABA) synthesis and sensitivity to mediate glucose-induced inhibition of seedling development. Here, we show that the intensity of short-term responses to glucose can vary with ABA activity. We report that the transient (2 h/4 h) repression by 2% glucose of AtbZIP63, a gene encoding a basic-leucine zipper (bZIP) transcription factor partially involved in the Snf1-related kinase KIN10-induced responses to energy limitation, is independent of HXK1 and is not mediated by changes in ABA levels. However, high-concentration (6%) glucose-mediated repression appears to be modulated by ABA, since full repression of AtbZIP63 requires a functional ABA biosynthetic pathway. Furthermore, the combination of glucose and ABA was able to trigger a synergistic repression of AtbZIP63 and its homologue AtbZIP3, revealing a shared regulatory feature consisting of the modulation of glucose sensitivity by ABA. The synergistic regulation of AtbZIP63 was not reproduced by an AtbZIP63 promoter-5`-untranslated region:beta-glucuronidase fusion, thus suggesting possible posttranscriptional control. A transcriptional inhibition assay with cordycepin provided further evidence for the regulation of mRNA decay in response to glucose plus ABA. Overall, these results indicate that AtbZIP63 is an important node of the glucose-ABA interaction network. The mechanisms by which AtbZIP63 may participate in the fine-tuning of ABA-mediated abiotic stress responses according to sugar availability (i.e., energy status) are discussed.

Proteome analysis of human dorsolateral prefrontal cortex using shotgun mass spectrometry

The prefrontal cortex executes important functions such as differentiation of conflicting thoughts, correct social behavior and personality expression, and is directly implicated in different neurodegenerative diseases. We performed a shotgun proteome analysis that included IEF fractionation, RP-LC, and MALDI-TOF/TOF mass spectrometric analysis of tryptic digests from a pool of seven human dorsolateral prefrontal cortex protein extracts. In this report, we present a catalog of 387 proteins expressed in these samples, identified by two or more peptides and high confidence search scores. These proteins are involved in different biological processes such as cell growth and/or maintenance, metabolism/energy pathways, cell communication/signal trarisduction, protein metabolism, transport, regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolism, and immune response. This analysis contributes to the knowledge of the human brain proteome by adding sample diversity and protein expression data from an alternative technical approach. It will also aid comparative studies of different brain areas and medical conditions, with future applications in basic and clinical research.

Matrix Metalloproteinase Expression in Teeth with Apical Periodontitis Is Differentially Modulated by the Modality of Root Canal Treatment

Introduction: The objective of this study was to investigate the expression of matrix metalloproteinases (MM Ps) in apical periodontitis and during the periapical healing phase after root canal treatment. Methods: Apical periodontitis was induced in dog teeth, and root canal treatment was performed in a single visit or by using an additional calcium hydroxide root canal dressing. One hundred eighty days after treatment the presence of inflammation was examined, and tissues were stained to detect bacteria. Bacterial status was correlated to the degree of tissue organization, and to further investigate molecules involved in this process, tissues were stained for MMP-1, MMP-2, MMP-8, and MMP-9. Data were analyzed by using one-way analysis of variance followed by Tukey test or Kruskal-Wallis followed by Dunn test. Results: Teeth with apical periodontitis that had root canal therapy performed in a single visit presented an intense inflammatory cell infiltrate. Periapical tissue was extremely disorganized, and this was correlated with the presence of bacteria. Higher MMP expression was evident, similar to teeth with untreated apical periodontitis. In contrast, teeth with apical periodontitis submitted to root canal treatment with calcium hydroxide presented a lower inflammatory cell infiltrate. This group had moderately organized connective tissue, lower prevalence of bacteria, and lower number of MMP-positive cells, similar to healthy teeth submitted to treatment. Conclusions: Teeth treated with calcium hydroxide root canal dressing exhibited a lower percentage of bacterial contamination, a lower MMP expression, and a more organized extracellular matrix, unlike those treated in a single visit. This suggests that calcium hydroxide might be beneficial in tissue repair processes. (J Endod 2010;36:231-237)

Cramoll 1,4 lectin increases ROS production, calcium levels, and cytokine expression in treated spleen cells of rats

This study reports the in vivo stimulatory effects of Cramoll 1,4 on rat spleen lymphocytes as evidenced by an increase in intracellular reactive oxygen species (ROS) production, Ca(2+) levels, and interleukin (IL)-1 beta expression. Cramoll 1,4 extracted from seeds of the Leguminosae Cratylia mollis Mart., is a lectin with antitumor and lymphocyte mitogenic activities. Animals (Nine-week-old male albino Wistar rats, Rattus norvegicus) were treated with intraperitoneal injection of Cramoll 1,4 (235 mu g ml(-1) single dose) and, 7 days later, spleen lymphocytes were isolated and analyzed for intracellular ROS, cytosolic Ca(2+), and IL-6, IL-10, and IL-1 mRNAs. Cell viability was investigated by annexin V-FITC and 7-amino-actinomycin D staining. The data showed that in lymphocytes activated by Cramoll 1,4 the increase in cytosolic and mitochondrial ROS was related to higher cytosolic Ca(2+) levels. Apoptosis and necrosis were not detected in statistically significant values and thus the lectin effector activities did not induce lymphocyte death. In vivo Cramoll 1,4 treatment led to a significant increase in IL-1 beta but IL-6 and -10 levels did not change. Cramoll 1,4 had modulator activities on spleen lymphocytes and stimulated the Th2 response.

Aquaporin 2 expression increased by glucagon in normal rat inner medullary collecting ducts

Yano Y, Cesar KR, Araujo M, Rodrigues Jr. AC, Andrade LC, Magaldi AJ. Aquaporin 2 expression increased by glucagon in normal rat inner medullary collecting ducts. Am J Physiol Renal Physiol 296: F54-F59, 2009. First published October 1, 2008; doi: 10.1152/ajprenal.90367.2008.-It is well known that Glucagon (Gl) is released after a high protein diet and participates in water excretion by the kidney, principally after a protein meal. To study this effect in in vitro perfused inner medullary collecting ducts (IMCD), the osmotic water permeability (Pf; mu m/s) at 37 degrees C and pH 7.4 in normal rat IMCDs (n = 36) perfused with Ringer/HCO(3) was determined. Gl (10(-7) M) in absence of Vasopressin (AVP) enhanced the Pf from 4.38 +/- 1.40 to 11.16 +/- 1.44 mu m/s (P < 0.01). Adding 10(-8), 10(-7), and 10(-6) M Gl, the Pf responded in a dose-dependent manner. The protein kinase A inhibitor H8 blocked the Gl effect. The specific Gl inhibitor, des-His(1)-[Glu(9)] glucagon (10(-7) M), blocked the Gl-stimulated Pf but not the AVP-stimulated Pf. There occurred a partial additional effect between Gl and AVP. The cAMP level was enhanced from the control 1.24 +/- 0.39 to 59.70 +/- 15.18 fm/mg prot after Gl 10(-7) M in an IMCD cell suspension. The immunoblotting studies indicated an increase in AQP2 protein abundance of 27% (cont 100.0 +/- 3.9 vs. Gl 127.53; P = 0.0035) in membrane fractions extracted from IMCD tubule suspension, incubated with 10(-6) M Gl. Our data showed that 1) Gl increased water absorption in a dose-dependent manner; 2) the anti-Gl blocked the action of Gl but not the action of AVP; 3) Gl stimulated the cAMP generation; 4) Gl increased the AQP2 water channel protein expression, leading us to conclude that Gl controls water absorption by utilizing a Gl receptor, rather than a AVP receptor, increasing the AQP2 protein expression.

Elevated CO(2) increases photosynthesis, biomass and productivity, and modifies gene expression in sugarcane

Because of the economical relevance of sugarcane and its high potential as a source of biofuel, it is important to understand how this crop will respond to the foreseen increase in atmospheric [CO(2)]. The effects of increased [CO(2)] on photosynthesis, development and carbohydrate metabolism were studied in sugarcane (Saccharum ssp.). Plants were grown at ambient (similar to 370 ppm) and elevated (similar to 720 ppm) [CO(2)] during 50 weeks in open-top chambers. The plants grown under elevated CO(2) showed, at the end of such period, an increase of about 30% in photosynthesis and 17% in height, and accumulated 40% more biomass in comparison with the plants grown at ambient [CO(2)]. These plants also had lower stomatal conductance and transpiration rates (-37 and -32%, respectively), and higher water-use efficiency (c.a. 62%). cDNA microarray analyses revealed a differential expression of 35 genes on the leaves (14 repressed and 22 induced) by elevated CO(2). The latter are mainly related to photosynthesis and development. Industrial productivity analysis showed an increase of about 29% in sucrose content. These data suggest that sugarcane crops increase productivity in higher [CO(2)], and that this might be related, as previously observed for maize and sorghum, to transient drought stress.