A unifying view for performance measures in multi-class prediction

In the last few years, many different performance measures have been introduced to overcome the weakness of the most natural metric, the Accuracy. Among them, Matthews Correlation Coefficient has recently gained popularity among researchers not only in machine learning but also in several application fields such as bioinformatics. Nonetheless, further novel functions are being proposed in literature. We show that Confusion Entropy, a recently introduced classifier performance measure for multi-class problems, has a strong (monotone) relation with the multi-class generalization of a classical metric, the Matthews Correlation Coefficient. Computational evidence in support of the claim is provided, together with an outline of the theoretical explanation

Stability Indicators in Network Reconstruction

The number of algorithms available to reconstruct a biological network from a dataset of high-throughput measurements is nowadays overwhelming, but evaluating their performance when the gold standard is unknown is a difficult task. Here we propose to use a few reconstruction stability tools as a quantitative solution to this problem. We introduce four indicators to quantitatively assess the stability of a reconstructed network in terms of variability with respect to data subsampling. In particular, we give a measure of the mutual distances among the set of networks generated by a collection of data subsets (and from the network generated on the whole dataset) and we rank nodes and edges according to their decreasing variability within the same set of networks. As a key ingredient, we employ a global/local network distance combined with a bootstrap procedure. We demonstrate the use of the indicators in a controlled situation on a toy dataset, and we show their application on a miRNA microarray dataset with paired tumoral and non-tumoral tissues extracted from a cohort of 241 hepatocellular carcinoma patients

The HIM glocal metric and kernel for network comparison and classification

Due to the ever rising importance of the network paradigm across several areas of science, comparing and classifying graphs represent essential steps in the networks analysis of complex systems. Both tasks have been recently tackled via quite different strategies, even tailored ad-hoc for the investigated problem. Here we deal with both operations by introducing the Hamming-Ipsen-Mikhailov (HIM) distance, a novel metric to quantitatively measure the difference between two graphs sharing the same vertices. The new measure combines the local Hamming distance and the global spectral Ipsen-Mikhailov distance so to overcome the drawbacks affecting the two components separately. Building then the HIM kernel function derived from the HIM distance it is possible to move from network comparison to network classification via the Support Vector Machine (SVM) algorithm. Applications of HIM distance and HIM kernel in computational biology and social networks science demonstrate the effectiveness of the proposed functions as a general purpose solution.Comment: Frontiers of Network Analysis: Methods, Models, and Applications - NIPS 2013 Worksho

Minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers

We introduce a novel implementation in ANSI C of the MINE family of algorithms for computing maximal information-based measures of dependence between two variables in large datasets, with the aim of a low memory footprint and ease of integration within bioinformatics pipelines. We provide the libraries minerva (with the R interface) and minepy for Python, MATLAB, Octave and C++. The C solution reduces the large memory requirement of the original Java implementation, has good upscaling properties, and offers a native parallelization for the R interface. Low memory requirements are demonstrated on the MINE benchmarks as well as on large (n=1340) microarray and Illumina GAII RNA-seq transcriptomics datasets. Availability and Implementation: Source code and binaries are freely available for download under GPL3 licence at http://minepy.sourceforge.net for minepy and through the CRAN repository http://cran.r-project.org for the R package minerva. All software is multiplatform (MS Windows, Linux and OSX).Comment: Bioinformatics 2012, in pres

A Version of Jung’s Synchronicity in the Event of Correlation of Mental Processes in the Past and the Future: Possible Role of Quantum Entanglement in Quantum Vacuum

This paper deals with the version of Jung’s synchronicity in which correlation between mental processes of two different persons takes place not just in the case when at a certain moment of time the subjects are located at a distance from each other, but also in the case when both persons are alternately (and sequentially, one after the other) located in the same point of space. In this case, a certain period of time lapses between manifestation of mental process in one person and manifestation of mental process in the other person. Transmission of information from one person to the other via classical communication channel is ruled out. The author proposes a hypothesis, whereby such manifestation of synchronicity may become possible thanks to existence of quantum entanglement between the past and the future within the light cone. This hypothesis is based on the latest perception of the nature of quantum vacuu

Two apples a day lower serum cholesterol and improve cardiometabolic biomarkers in mildly hypercholesterolemic adults: a randomized, controlled, crossover trial

Background: Apples are rich in bioactive polyphenols and fiber. Evidence suggests that consumption of apples, or their bioactive components is associated with beneficial effects on lipid metabolism and other markers of cardiovascular disease (CVD). However, adequately powered randomized controlled trials are necessary to confirm these data and explore the mechanisms. Objective: To determine the effects of apple consumption on circulating lipids, vascular function and other CVD risk markers. Design: The trial was a randomized, controlled, crossover, intervention study. Healthy mildly hypercholesterolemic volunteers (23 women, 17 men), with a mean BMI (± SD) 25.3 (± 3.7)kg/m2 and age (± SD) 51.4 (± 11) years, consumed 2 apples/day (Renetta Canada, rich in proanthocyanidins), or a sugar and energy matched apple control beverage (CB) for 8 weeks separated by a 4-week washout period. Fasted blood was collected before and after each treatment. Serum lipids, glucose, insulin, bile acids, endothelial and inflammation biomarkers were measured, in addition to microvascular reactivity, using laser Doppler imaging with Iontophoresis and arterial stiffness, using Pulse Wave Analysis. Results: Whole apple (WA) consumption decreased serum total (WA: 5.89 mmol/l, CB: 6.11mmol/l; P=0.006) and LDL cholesterol (WA: 3.72 mmol/l, CB: 3.86 mmol/l; P=0.031), triacylglycerol (WA: 1.17 mmol/l, CB: 1.30 mmol/l; P=0.021) and intercellular cell adhesion molecule-1 (WA: 153.9 ng/ml, CB: 159.4 ng/ml; P=0.028), and increased serum uric acid (WA:341.4 μmol/l, CB: 330 μmol/l; P=0.020) compared with the CB. The response to endothelium dependent microvascular vasodilation was greater after the apples (WA: 853 (PU, perfusion units), CB: 760 PU; P=0.037) compared with the CB. Apples had no effect on blood pressure or other CVD markers. Conclusions: These data support beneficial hypocholesterolemic and vascular effects of the daily consumption of proanthocyanidin-rich apples by mildly hypercholesterolemic individuals

Clinical value of prognosis gene expression signatures in colorectal cancer: a systematic review

Introduction: the traditional staging system is inadequate to identify those patients with stage II colorectal cancer (CRC) at high risk of recurrence or with stage III CRC at low risk. A number of gene expression signatures to predict CRC prognosis have been proposed, but none is routinely used in the clinic. The aim of this work was to assess the prediction ability and potential clinical usefulness of these signatures in a series of independent datasets. Methods: a literature review identified 31 gene expression signatures that used gene expression data to predict prognosis in CRC tissue. The search was based on the PubMed database and was restricted to papers published from January 2004 to December 2011. Eleven CRC gene expression datasets with outcome information were identified and downloaded from public repositories. Random Forest classifier was used to build predictors from the gene lists. Matthews correlation coefficient was chosen as a measure of classification accuracy and its associated p-value was used to assess association with prognosis. For clinical usefulness evaluation, positive and negative post-tests probabilities were computed in stage II and III samples. Results: five gene signatures showed significant association with prognosis and provided reasonable prediction accuracy in their own training datasets. Nevertheless, all signatures showed low reproducibility in independent data. Stratified analyses by stage or microsatellite instability status showed significant association but limited discrimination ability, especially in stage II tumors. From a clinical perspective, the most predictive signatures showed a minor but significant improvement over the classical staging system. Conclusions: the published signatures show low prediction accuracy but moderate clinical usefulness. Although gene expression data may inform prognosis, better strategies for signature validation are needed to encourage their widespread use in the clinic