Inferring Unknown Biological Function by Integration of GO Annotations and Gene Expression Data.

Characterizing genes with semantic information is an important process regarding the description of gene products. In spite that complete genomes of many organisms have been already sequenced, the biological functions of all of their genes are still unknown. Since experimentally studying the functions of those genes, one by one, would be unfeasible, new computational methods for gene functions inference are needed.

Rapid and direct volatile compound profiling of black and green teas (Camellia sinensis) from different countries with PTR-ToF-MS.

Volatile profiles of 63 black and 38 green teas from different countries were analysed with Proton Transfer Reaction-Time of Flight-Mass Spectrometry (PTR-ToF-MS) both for tea leaves and tea infusion. The headspace volatile fingerprints were collected and the tea classes and geographical origins were tracked with pattern recognition techniques. The high mass resolution achieved by ToF mass analyser provided determination of sum formula and tentative identifications of the mass peaks.

How many clusters: a validation index for arbitrary-shaped clusters.

Clustering validation indexes are intended to assess the goodness of clustering results. Many methods used to estimate the number of clusters rely on a validation index as a key element to find the correct answer. This paper presents a new validation index based on graph concepts, which has been designed to find arbitrary shaped clusters by exploiting the spatial layout of the patterns and their clustering label.

Clustering gene expression data with a penalized graph-based metric.

Background: The search for cluster structure in microarray datasets is a base problem for the so-called "-omic sciences". A difficult problem in clustering is how to handle data with a manifold structure, i.e.

Solving nonstationary classification problems with coupled support vector machines.

Many learning problems may vary slowly over time: in particular, some critical real-world applications. When facing this problem, it is desirable that the learning method could find the correct input-output function and also detect the change in the concept and adapt to it. We introduce the time-adaptive support vector machine (TA-SVM), which is a new method for generating adaptive classifiers, capable of learning concepts that change with time.

PTR-TOF-MS and data-mining methods for rapid characterisation of agro-industrial samples: influence of milk storage conditions on the volatile compounds profile of Trentingrana cheese.

Proton transfer reaction-mass spectrometry (PTR-MS), a direct injection mass spectrometric technique based on an efficient implementation of chemical ionisation, allows for fast and high-sensitivity monitoring of volatile organic compounds (VOCs). The first implementations of PTR-MS, based on quadrupole mass analyzers (PTR-Quad-MS), provided only the nominal mass of the ions measured and thus little chemical information. To partially overcome these limitations and improve the analytical capability of this technique, the coupling of proton transfer reaction ionisation with a time-of-flight mass analyser has been recently realised and commercialised (PTR-TOF-MS).

Artificial neural network learning of nonstationary behavior in time series.

We refine and complement a previously-proposed artificial neural network method for learning hidden signals forcing nonstationary behavior in time series. The method adds an extra input unit to the network and feeds it with the proposed profile for the unknown perturbing signal. The correct time evolution of this new input parameter is learned simultaneously with the intrinsic stationary dynamics underlying the series, which is accomplished by minimizing a suitably-defined error function for the training process.