19 research outputs found
A voting approach to identify a small number of highly predictive genes using multiple classifiers
<p>Abstract</p> <p>Background</p> <p>Microarray gene expression profiling has provided extensive datasets that can describe characteristics of cancer patients. An important challenge for this type of data is the discovery of gene sets which can be used as the basis of developing a clinical predictor for cancer. It is desirable that such gene sets be compact, give accurate predictions across many classifiers, be biologically relevant and have good biological process coverage.</p> <p>Results</p> <p>By using a new type of multiple classifier voting approach, we have identified gene sets that can predict breast cancer prognosis accurately, for a range of classification algorithms. Unlike a wrapper approach, our method is not specialised towards a single classification technique. Experimental analysis demonstrates higher prediction accuracies for our sets of genes compared to previous work in the area. Moreover, our sets of genes are generally more compact than those previously proposed. Taking a biological viewpoint, from the literature, most of the genes in our sets are known to be strongly related to cancer.</p> <p>Conclusion</p> <p>We show that it is possible to obtain superior classification accuracy with our approach and obtain a compact gene set that is also biologically relevant and has good coverage of different biological processes.</p
SPARCoC: A New Framework for Molecular Pattern Discovery and Cancer Gene Identification
<div><p>It is challenging to cluster cancer patients of a certain histopathological type into molecular subtypes of clinical importance and identify gene signatures directly relevant to the subtypes. Current clustering approaches have inherent limitations, which prevent them from gauging the subtle heterogeneity of the molecular subtypes. In this paper we present a new framework: SPARCoC (Sparse-CoClust), which is based on a novel Common-background and Sparse-foreground Decomposition (CSD) model and the Maximum Block Improvement (MBI) co-clustering technique. SPARCoC has clear advantages compared with widely-used alternative approaches: hierarchical clustering (Hclust) and nonnegative matrix factorization (NMF). We apply SPARCoC to the study of lung adenocarcinoma (ADCA), an extremely heterogeneous histological type, and a significant challenge for molecular subtyping. For testing and verification, we use high quality gene expression profiling data of lung ADCA patients, and identify prognostic gene signatures which could cluster patients into subgroups that are significantly different in their overall survival (with p-values < 0.05). Our results are only based on gene expression profiling data analysis, without incorporating any other feature selection or clinical information; we are able to replicate our findings with completely independent datasets. SPARCoC is broadly applicable to large-scale genomic data to empower pattern discovery and cancer gene identification.</p></div
Early parental deprivation in the marmoset monkey produces long-term changes in hippocampal expression of genes involved in synaptic plasticity and implicated in mood disorder.
In mood disorder, early stressors including parental separation are vulnerability factors, and hippocampal involvement is prominent. In common marmoset monkeys, daily parental deprivation during infancy produces a prodepressive state of increased basal activity and reactivity in stress systems and mild anhedonia that persists at least to adolescence. Here we examined the expression of eight genes, each implicated in neural plasticity and in the pathophysiology of mood disorder, in the hippocampus of these same adolescent marmosets, relative to their normally reared sibling controls. We also measured hippocampal volume. Early deprivation led to decreases in hippocampal growth-associated protein-43 (GAP-43) mRNA, serotonin 1A receptor (5-HT(1A)R) mRNA and binding ([3H]WAY100635), and to increased vesicular GABA transporter mRNA. Brain-derived neurotrophic factor (BDNF), synaptophysin, vesicular glutamate transporter 1 (VGluT1), microtubule-associated protein-2, and spinophilin transcripts were unchanged. There were some correlations with in vivo biochemical and behavioral indices, including VGluT1 mRNA with reward-seeking behavior, and serotonin 1A receptor mRNA with CSF cortisol. Early deprivation did not affect hippocampal volume. We conclude that early deprivation in a nonhuman primate, in the absence of subsequent stressors, has a long-term effect on the hippocampal expression of genes implicated in synaptic function and plasticity. The reductions in GAP-43 and serotonin 1A receptor expressions are comparable with findings in mood disorder, supporting the possibility that the latter reflect an early developmental contribution to disease vulnerability. Equally, the negative results suggest that other features of mood disorder, such as decreased hippocampal volume and BDNF expression, are related to different aspects of the pathophysiological process
Changes in bacterial and archaeal community assemblages along an ombrotrophic peat bog profile
Peatlands are archives of extreme importance for the assessment of past ecological, environmental and climatic changes. The importance as natural archives is even greater in the case of ombrotrophic peat bogs, where the only inputs are atmospheric in origin. Here we integrated previously published physical and chemical results regarding the solid and liquid phase of peat with a biomolecular microbiological approach to assess the relationships between chemistry and microbial biodiversity along a Swiss bog profile corresponding to approximately 2,000 years of peat formation. The structure of bacterial and archaeal communities was assessed through a polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) approach followed by sequencing of PCR-DGGE bands of interest. Both chemical and microbiological data showed a differentiation of properties along the peat profile, with three major zones identified. Both bacterial and archaeal profiles clustered according to the depth (i.e., age) of samples. Among bacteria, Acidobacteria were recovered primarily in the first layers of the profile, whereas methanogenic archaea were more commonly recovered in the deepest part of the core, corresponding to the occurring anoxic conditions. Finally, a number of sequences had low homologies with known species, especially in bacteria: this points to an almost unknown microbial community adapted to the extreme conditions of peat bogs, which are acidic, rich in dissolved organic C, and predominantly anoxic. \ua9 2014 Springer-Verlag Berlin Heidelberg