Feature selection using Haar wavelet power spectrum

BACKGROUND: Feature selection is an approach to overcome the 'curse of dimensionality' in complex researches like disease classification using microarrays. Statistical methods are utilized more in this domain. Most of them do not fit for a wide range of datasets. The transform oriented signal processing domains are not probed much when other fields like image and video processing utilize them well. Wavelets, one of such techniques, have the potential to be utilized in feature selection method. The aim of this paper is to assess the capability of Haar wavelet power spectrum in the problem of clustering and gene selection based on expression data in the context of disease classification and to propose a method based on Haar wavelet power spectrum. RESULTS: Haar wavelet power spectra of genes were analysed and it was observed to be different in different diagnostic categories. This difference in trend and magnitude of the spectrum may be utilized in gene selection. Most of the genes selected by earlier complex methods were selected by the very simple present method. Each earlier works proved only few genes are quite enough to approach the classification problem [1]. Hence the present method may be tried in conjunction with other classification methods. The technique was applied without removing the noise in data to validate the robustness of the method against the noise or outliers in the data. No special softwares or complex implementation is needed. The qualities of the genes selected by the present method were analysed through their gene expression data. Most of them were observed to be related to solve the classification issue since they were dominant in the diagnostic category of the dataset for which they were selected as features. CONCLUSION: In the present paper, the problem of feature selection of microarray gene expression data was considered. We analyzed the wavelet power spectrum of genes and proposed a clustering and feature selection method useful for classification based on Haar wavelet power spectrum. Application of this technique in this area is novel, simple, and faster than other methods, fit for a wide range of data types. The results are encouraging and throw light into the possibility of using this technique for problem domains like disease classification, gene network identification and personalized drug design

The normal breast microenvironment of premenopausal women differentially influences the behavior of breast cancer cells in vitro and in vivo

<p>Abstract</p> <p>Background</p> <p>Breast cancer studies frequently focus on the role of the tumor microenvironment in the promotion of cancer; however, the influence of the normal breast microenvironment on cancer cells remains relatively unknown. To investigate the role of the normal breast microenvironment on breast cancer cell tumorigenicity, we examined whether extracellular matrix molecules (ECM) derived from premenopausal African-American (AA) or Caucasian-American (CAU) breast tissue would affect the tumorigenicity of cancer cells <it>in vitro </it>and <it>in vivo</it>. We chose these two populations because of the well documented predisposition of AA women to develop aggressive, highly metastatic breast cancer compared to CAU women.</p> <p>Methods</p> <p>The effects of primary breast fibroblasts on tumorigenicity were analyzed via real-time PCR arrays and mouse xenograft models. Whole breast ECM was isolated, analyzed via zymography, and its effects on breast cancer cell aggressiveness were tested <it>in vitro </it>via soft agar and invasion assays, and <it>in vivo </it>via xenograft models. Breast ECM and hormone metabolites were analyzed via mass spectrometry.</p> <p>Results</p> <p>Mouse mammary glands humanized with premenopausal CAU fibroblasts and injected with primary breast cancer cells developed significantly larger tumors compared to AA humanized glands. Examination of 164 ECM molecules and cytokines from CAU-derived fibroblasts demonstrated a differentially regulated set of ECM proteins and increased cytokine expression. Whole breast ECM was isolated; invasion and soft agar assays demonstrated that estrogen receptor (ER)^-, progesterone receptor (PR)/PR^-cells were significantly more aggressive when in contact with AA ECM, as were ER⁺/PR⁺cells with CAU ECM. Using zymography, protease activity was comparatively upregulated in CAU ECM. In xenograft models, CAU ECM significantly increased the tumorigenicity of ER⁺/PR⁺cells and enhanced metastases. Mass spectrometry analysis of ECM proteins showed that only 1,759 of approximately 8,000 identified were in common. In the AA dataset, proteins associated with breast cancer were primarily related to tumorigenesis/neoplasia, while CAU unique proteins were involved with growth/metastasis. Using a novel mass spectrometry method, 17 biologically active hormones were measured; estradiol, estriol and 2-methoxyestrone were significantly higher in CAU breast tissue.</p> <p>Conclusions</p> <p>This study details normal premenopausal breast tissue composition, delineates potential mechanisms for breast cancer development, and provides data for further investigation into the role of the microenvironment in cancer disparities.</p

Differential Stress-Induced Neuronal Activation Patterns in Mouse Lines Selectively Bred for High, Normal or Low Anxiety

There is evidence for a disturbed perception and processing of emotional information in pathological anxiety. Using a rat model of trait anxiety generated by selective breeding, we previously revealed differences in challenge-induced neuronal activation in fear/anxiety-related brain areas between high (HAB) and low (LAB) anxiety rats. To confirm whether findings generalize to other species, we used the corresponding HAB/LAB mouse model and investigated c-Fos responses to elevated open arm exposure. Moreover, for the first time we included normal anxiety mice (NAB) for comparison. The results confirm that HAB mice show hyperanxious behavior compared to their LAB counterparts, with NAB mice displaying an intermediate anxiety phenotype. Open arm challenge revealed altered c-Fos response in prefrontal-cortical, limbic and hypothalamic areas in HAB mice as compared to LAB mice, and this was similar to the differences observed previously in the HAB/LAB rat lines. In mice, however, additional differential c-Fos response was observed in subregions of the amygdala, hypothalamus, nucleus accumbens, midbrain and pons. Most of these differences were also seen between HAB and NAB mice, indicating that it is predominately the HAB line showing altered neuronal processing. Hypothalamic hypoactivation detected in LAB versus NAB mice may be associated with their low-anxiety/high-novelty-seeking phenotype. The detection of similarly disturbed activation patterns in a key set of anxiety-related brain areas in two independent models reflecting psychopathological states of trait anxiety confirms the notion that the altered brain activation in HAB animals is indeed characteristic of enhanced (pathological) anxiety, providing information for potential targets of therapeutic intervention

Search for the Higgs boson produced in association with Z→ ℓ+ ℓ - Using the matrix element method at CDF II

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