Prostate cancer (PCa) is the second most commonly diagnosed cancer in men, accounting for 15% of all cancers in men worldwide. Asian populations consume soy foods as part of a regular diet, which may contribute to the lower PCa incidence observed in these countries. This meta-analysis provides a comprehensive updated analysis that builds on previously published meta-analyses, demonstrating that...

Recent studies have indicated that single nucleotide polymorphisms (SNPs) within the 8q24 region may be a risk factor for prostate cancer (PCa). Here, we performed a meta-analysis to evaluate the association between the 8q24 rs6983267 T/G polymorphism and PCa risk. A systematic literature search was carried out in multiple electronic databases independently by two investigators. Pooled odds rat...

Prostate cancer (PCa) is the second leading cause of cancer-related deaths in men in the United States and is the most commonly diagnosed non-cutaneous cancer. CCL4 is a secreted chemokine that is over expressed in patients that show PCa recurrence after prostatectomy. Currently, no reported evidence shows the biological role of CCL4 in PCa progression. We studied the role of CCL4 in PCa progre...

Pleomorphic adenoma gene like-2 (PLAGL2) is a member of the PLAG gene family. Previous studies have revealed that overexpression of PLAGL2 is associated with many human cancers. However, it has been reported that PLAGL2 also plays as a tumor suppressor. The precise role of PLAGL2 in prostate cancer (PCa) is still unknown. The aim of this study was to investigate the expression and prognostic va...

this paper compares clusters of aligned persian and english texts obtained from k-means method. text clustering has many applications in various fields of natural language processing. so far, much english documents clustering research has been accomplished. now this question arises, are the results of them extendable to other languages? since the goal of document clustering is grouping of docum...

Analysing the face recognition rate of various current face recognition algorithms is absolutely critical in developing new robust algorithms. In his paper we propose performance analysis of Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Locality Preserving Projections (LPP) for face recognition. This analysis was carried out on various current PCA, LDA and LPP based...

Principal component analyses (PCA) has been widely used in reduction of the dimensionality of datasets, classification, feature extraction, etc. It has been combined with many other algorithms such as EM (expectation-maximization), ANN (artificial neural network), probabilistic models, statistic analyses, etc., and has its own development such as MPCA (moving PCA), MS-PCA (multi-scale PCA), etc...

in this article the methodology proposed by li and wang for mixed qualitative and quantitative modeling and simulation of temporal behavior of processing unit is reexamined and extended to more complex case. the main issue of their approach considers the multivariate statistics of principal component analysis (pca), along with clustered fuzzy digraphs and reasoning. the pca and fuzzy clustering...