Performance Comparison of Linear and Nonlinear Feature Selection Methods for the Analysis of Large Survey Datasets

Large survey databases for aging-related analysis are often examined to discover key factors that affect a dependent variable of interest. Typically, this analysis is performed with methods assuming linear dependencies between variables. Such assumptions however do not hold in many cases, wherein data are linked by way of non-linear dependencies. This in turn requires applications of analytic methods, which are more accurate in identifying potentially non-linear dependencies. Here, we objectively compared the feature selection performance of several frequently-used linear selection methods and three non-linear selection methods in the context of large survey data. These methods were assessed using both synthetic and real-world datasets, wherein relationships between the features and dependent variables were known in advance. In contrast to linear methods, we found that the non-linear methods offered better overall feature selection performance than linear methods in all usage conditions. Moreover, the performance of the non-linear methods was more stable, being unaffected by the inclusion or exclusion of variables from the datasets. These properties make non-linear feature selection methods a potentially preferable tool for both hypothesis-driven and exploratory analyses for aging-related datasets

Identification of Novel Molecular Targets for Endometrial Cancer Using a Drill-Down LC-MS/MS Approach with iTRAQ

BACKGROUND: The number of patients with endometrial carcinoma (EmCa) with advanced stage or high histological grade is increasing and prognosis has not improved for over the last decade. There is an urgent need for the discovery of novel molecular targets for diagnosis, prognosis and treatment of EmCa, which will have the potential to improve the clinical strategy and outcome of this disease. METHODOLOGY AND RESULTS: We used a "drill-down" proteomics approach to facilitate the identification of novel molecular targets for diagnosis, prognosis and/or therapeutic intervention for EmCa. Based on peptide ions identified and their retention times in the first LC-MS/MS analysis, an exclusion list was generated for subsequent iterations. A total of 1529 proteins have been identified below the Proteinpilot® 5% error threshold from the seven sets of iTRAQ experiments performed. On average, the second iteration added 78% new peptides to those identified after the first run, while the third iteration added 36% additional peptides. Of the 1529 proteins identified, only 40 satisfied our criteria for significant differential expression in EmCa in comparison to normal proliferative tissues. These proteins included metabolic enzymes (pyruvate kinase M2 and lactate dehydrogenase A); calcium binding proteins (S100A6, calcyphosine and calumenin), and proteins involved in regulating inflammation, proliferation and invasion (annexin A1, interleukin enhancer-binding factor 3, alpha-1-antitrypsin, macrophage capping protein and cathepsin B). Network analyses revealed regulation of these molecular targets by c-myc, Her2/neu and TNF alpha, suggesting intervention with these pathways may be a promising strategy for the development of novel molecular targeted therapies for EmCa. CONCLUSIONS: Our analyses revealed the significance of drill-down proteomics approach in combination with iTRAQ to overcome some of the limitations of current proteomics strategies. This study led to the identification of a number of novel molecular targets having therapeutic potential for targeted molecular therapies for endometrial carcinoma

Influence of time delay in coupling.

<p>(A) Standard Granger causality; (B) spectral causality; (C) transfer entropy as functions of the observed time diffrence at 10 Hz; and (D) phase difference at 10 Hz as a function of the time delay in coupling, given that the strength of coupling was unchanged.</p

Scenario 2: causality and phase synchronization.

<p>Relations between the reconstructed causality and phase-related effects in the case where the phase shift between the driver and response is : (A) measure of spectral Granger causality as a function of frequency; (B) transfer entropy as a function of the time lag ; (C) phase-locking index and (D) phase shift as functions of frequency.</p

ECoG data: spectral power.

<p>Mean spectral density and cross power spectral density of two ECoG channels, averaged across trials. The errorbars represent the standard error computed across trials.</p

Scenario 1: causality and phase synchronization.

<p>Relations between the reconstructed causality and effects of phase-locking and phase differences in the case where there is no phase shift at the main frequency ( Hz): (A) measure of spectral Granger causality as a function of frequency; (B) transfer entropy as a function of the time lag between the past of one signal and the future of the other; (C) phase-locking index and (D) phase shift as functions of frequency.</p

ECoG data: causality and phase synchronization.

<p>(A) Estimated spectral causality; (B) transfer entropy; (C) phase-locking index; and (D) phase differences, computed using local field potentials recorded from a pair of ECoG electrodes. Solid lines represent the mean of statistics under investigation, averaged across trials. The shaded area represents the variability (- and -quantiles) of the corresponding statistics based on surrogate data.</p

Influence of coupling strength.

<p>(A) Standard Granger causality; (B) spectral causality; (C) transfer entropy as functions of phase difference at 10 Hz; and (D) phase difference at 10 Hz as a function of the coupling strength, with the time delay in coupling kept constant.</p

Scenario 2: time series and spectral power.

<p>Characteristics of the driver and response in the case of a negative phase difference () between them at Hz (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053588#pone-0053588-g003" target="_blank">Fig. 3</a>): (A) simulated signals (two seconds of a randomly chosen realization); and (B) mean spectral density and cross power spectral density, averaged across realizations. The errorbars represent the standard error computed across realizations.</p