Sparse canonical correlation analysis for identifying, connecting and completing gene-expression networks

<p>Abstract</p> <p>Background</p> <p>We generalized penalized canonical correlation analysis for analyzing microarray gene-expression measurements for checking completeness of known metabolic pathways and identifying candidate genes for incorporation in the pathway. We used Wold's method for calculation of the canonical variates, and we applied ridge penalization to the regression of pathway genes on canonical variates of the non-pathway genes, and the elastic net to the regression of non-pathway genes on the canonical variates of the pathway genes.</p> <p>Results</p> <p>We performed a small simulation to illustrate the model's capability to identify new candidate genes to incorporate in the pathway: in our simulations it appeared that a gene was correctly identified if the correlation with the pathway genes was 0.3 or more. We applied the methods to a gene-expression microarray data set of 12, 209 genes measured in 45 patients with glioblastoma, and we considered genes to incorporate in the glioma-pathway: we identified more than 25 genes that correlated > 0.9 with canonical variates of the pathway genes.</p> <p>Conclusion</p> <p>We concluded that penalized canonical correlation analysis is a powerful tool to identify candidate genes in pathway analysis.</p

Neuromuscular Consequences of an Extreme Mountain Ultra-Marathon

We investigated the physiological consequences of one of the most extreme exercises realized by humans in race conditions: a 166-km mountain ultra-marathon (MUM) with 9500 m of positive and negative elevation change. For this purpose, (i) the fatigue induced by the MUM and (ii) the recovery processes over two weeks were assessed. Evaluation of neuromuscular function (NMF) and blood markers of muscle damage and inflammation were performed before and immediately following (n = 22), and 2, 5, 9 and 16 days after the MUM (n = 11) in experienced ultra-marathon runners. Large maximal voluntary contraction decreases occurred after MUM (−35% [95% CI: −28 to −42%] and −39% [95% CI: −32 to −46%] for KE and PF, respectively), with alteration of maximal voluntary activation, mainly for KE (−19% [95% CI: −7 to −32%]). Significant modifications in markers of muscle damage and inflammation were observed after the MUM as suggested by the large changes in creatine kinase (from 144±94 to 13,633±12,626 UI L−1), myoglobin (from 32±22 to 1,432±1,209 µg L−1), and C-Reactive Protein (from <2.0 to 37.7±26.5 mg L−1). Moderate to large reductions in maximal compound muscle action potential amplitude, high-frequency doublet force, and low frequency fatigue (index of excitation-contraction coupling alteration) were also observed for both muscle groups. Sixteen days after MUM, NMF had returned to initial values, with most of the recovery process occurring within 9 days of the race. These findings suggest that the large alterations in NMF after an ultra-marathon race are multi-factorial, including failure of excitation-contraction coupling, which has never been described after prolonged running. It is also concluded that as early as two weeks after such an extreme running exercise, maximal force capacities have returned to baseline

Sex and age-related differences in performance in a 24-hour ultra-cycling draft-legal event - a cross-sectional data analysis

Background The purpose of this study was to examine the sex and age-related differences in performance in a draft-legal ultra-cycling event. Methods Age-related changes in performance across years were investigated in the 24-hour draft-legal cycling event held in Schotz, Switzerland, between 2000 and 2011 using multi-level regression analyses including age, repeated participation and environmental temperatures as co-variables. Results For all finishers, the age of peak cycling performance decreased significantly (Ss = -0.273, p = 0.036) from 38 +/- 10 to 35 +/- 6 years in females but remained unchanged (Ss = -0.035, p = 0.906) at 41.0 +/- 10.3 years in males. For the annual fastest females and males, the age of peak cycling performance remained unchanged at 37.3 +/- 8.5 and 38.3 +/- 5.4 years, respectively. For all female and male finishers, males improved significantly (Ss = 7.010, p = 0.006) the cycling distance from 497.8 +/- 219.6 km to 546.7 +/- 205.0 km whereas females (Ss = -0.085, p = 0.987) showed an unchanged performance of 593.7 +/- 132.3 km. The mean cycling distance achieved by the male winners of 960.5 +/- 51.9 km was significantly (p 0.05). The sex difference in performance for the annual winners of 19.7 +/- 7.8% remained unchanged across years (p > 0.05). The achieved cycling distance decreased in a curvilinear manner with advancing age. There was a significant age effect (F = 28.4, p < 0.0001) for cycling performance where the fastest cyclists were in age group 35-39 years. Conclusion In this 24-h cycling draft-legal event, performance in females remained unchanged while their age of peak cycling performance decreased and performance in males improved while their age of peak cycling performance remained unchanged. The annual fastest females and males were 37.3 +/- 8.5 and 38.3 +/- 5.4 years old, respectively. The sex difference for the fastest finishers was ~20%. It seems that women were not able to profit from drafting to improve their ultra-cycling performance

A comparison of methods for classifying clinical samples based on proteomics data: A case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems

Comparison of linear multivariable, partial least square regression, and artificial neural network analyses to study the effect of different parameters on anode properties

Carbon anodes constitute a substantial part of the cost during the electrolytic production of aluminum. The industry tries to minimize the consumption of anodes by improving their quality. Therefore, a clear understanding of the impact of the quality of raw materials as well as process parameters on anode properties is important. The plants have a large collection of data, which is complex and difficult to analyze using conventional methods. In this article, linear multivariable (LMA), partial least square regression (PLS), and artificial neural network (ANN) analyses are presented and compared as tools to predict the influence of different parameters on anode properties. Published laboratory data have been processed using Matlab software to carry out the analyses. The results clearly show that ANN is the best tool for prediction purposes. Unlike other methods, ANN can handle nonlinear complex relations even if a well-defined relationship is not available