In analysis of bioinformatics data, a unique challenge arises from the high dimensionality of measurements. Without loss of generality, we use genomic study with gene expression measurements as a representative example but note that analysis techniques discussed in this article are also applicable to other types of bioinformatics studies. Principal component analysis (PCA) is a classic dimensio...

Recently, many l1-norm based PCA methods have been developed for dimensionality reduction, but they do not explicitly consider the reconstruction error. Moreover, they do not take into account the relationship between reconstruction error and variance of projected data. This reduces the robustness of algorithms. To handle this problem, a novel formulation for PCA, namely angle PCA, is proposed....

The state-of-the-art in human face recognition is the subspace methods originated by the Principal Component Analysis (PCA), the Eigenfaces of the facial images. Recently, a technique called Two-Dimensional PCA (2DPCA) was proposed for human face representation and recognition. It was developed for image feature extraction based on 2D matrices as opposed to the standard PCA, which is based on 1...

This short paper updates results presented in two previous publications, “ A Nonparametric Statistical Comparison of Principal Component and Linear Discriminant Subspaces for Face Recognition” presented at CVPR 2001 and “Parametric and Nonparametric Methods for the Statistical Evaluation of Human ID Algorithms” presented at the Workshop on the Empirical Evaluation of Computer Vision Algorithms ...

Prostate cancer (PCa) is the most common type of cancer among men over 60 years old. The aggressiveness and mortality of PCa can be correlated with obesity. Adipose tissue-derived cytokines such as adiponectin may explain the correlation between PCa and obesity. Since the correlation between adiponectin and aggressive PCa is still not fully evaluated, we aimed to investigate the probable role o...

OBJECTIVES To review the historical origins and current evidence for the belief that testosterone (T) causes prostate cancer (pCA) growth. METHODS Review of the historical literature regarding T administration and pCA, as well as more recent studies investigating the relationship of T and pCA. RESULTS In 1941 Huggins and Hodges reported that marked reductions in T by castration or estrogen ...

Prostate cancer (PCa) is a common malignant tumor and the second leading cause of morbidity and mortality in men worldwide. Considering the prevalence and effects of PCa in males, an understanding of the molecular mechanisms underlying PCa tumorigenesis are essential and may provide novel therapeutic strategies for treating PCa. Bloom syndrome protein (BLM) is a member of the RecQ helicase fami...

BACKGROUND Hip fractures are associated with increased mortality and are a known adverse effect of androgen deprivation therapy (ADT) for prostate cancer (PCa). It was our aim to evaluate how mortality after hip fracture is modified by PCa and ADT. METHODS PCa dataBase Sweden (PCBaSe 2.0) is based on the National PCa Register and also contains age and county-matched PCa-free men. We selected ...

Since it is not possible to compute E-scores for 10-mers from PBM data, we used 10-mer pseudo-intensities to perform an analysis similar to the one described in the main text for 8-mer E-scores. We estimated the 10-mers intensities from the 7-mer and 8-mer median intensities. Let X = X1X2 . . . Xk be a DNA site of length k, with Ik(X) it’s median intensity, and Ek(X) it’s energy. Then the inten...

1 Intro The theme of these two lectures is that for L2 methods we need not work in infinite dimensional spaces. In particular, we can unadaptively find and work in a low dimensional space and achieve about as good results. These results question the need for explicitly working in infinite (or high) dimensional spaces for L2 methods. In contrast, for sparsity based methods (including L1 regulari...