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The elucidation of biological networks regulating the metabolic basis of disease is critical for understanding disease progression and in identifying therapeutic targets. In molecular biology, this process often starts by clustering expression profiles which are candidates for disease phenotypes. However, each cluster may comprise several overlapping processes that are active in the cluster. This paper outlines empirical results using methods for blind source separation to map the pathways of biomarkers driving independent, hidden processes that underpin the clusters. The method is applied to a protein expression data set measured in tissue from breast cancer patients (n=1,076).

After decades of intensive use, K-Means is still a common choice for crisp data clustering in real-world applications, particularly in biomedicine and bioinformatics. It is well-known that different initializations of the algorithm can lead to different solutions, precluding replicability. It has also been reported that even solutions with very similar errors may widely differ. A criterion for the choice of clustering solutions according to a combination of error and stability measures has recently been suggested. It is based on the use of Cramér’s V index, calculated from contingency tables, which is valid only for crisp clustering. Here, this criterion is extended to fuzzy and probabilistic clustering by first defining weighted contingency tables and a corresponding weighted Cramér’s V index. The proposed method is illustrated using Fuzzy C-Means in a proteomics problem.

Smooth nonlinear topographic maps of the data distribution to guide a Grand Tour visualisation.Prioritisation of data linear views that are most consistent with data structure in the maps.Useful visualisations that cannot be obtained by other more classical approaches. Dimensionality reduction is required to produce visualisations of high dimensional data. In this framework, one of the most straightforward approaches to visualising high dimensional data is based on reducing complexity and applying linear projections while tumbling the projection axes in a defined sequence which generates a Grand Tour of the data. We propose using smooth nonlinear topographic maps of the data distribution to guide the Grand Tour, increasing the effectiveness of this approach by prioritising the linear views of the data that are most consistent with global data structure in these maps. A further consequence of this approach is to enable direct visualisation of the topographic map onto projective spaces that discern structure in the data. The experimental results on standard databases reported in this paper, using self-organising maps and generative topographic mapping, illustrate the practical value of the proposed approach. The main novelty of our proposal is the definition of a systematic way to guide the search of data views in the grand tour, selecting and prioritizing some of them, based on nonlinear manifold models.

In this paper a fuzzy controller capable to perform an automated estimation of the period of time necessary to recover a resilience level is proposed. Estimations where made by considering realistic time-dependent action changes for a set of resilience indicators originally proposed by Cardona (2001) and modified by Cardenas et al (2015). The fuzzy resilience controller works using two output control variables and four input variables designed to resemble politics decisions made over resilience recovery while considering an economical national growth factor. We applied the fuzzy controller onto Barcelona Spain, where different recovery times where estimated in terms of variations in Spaniard GDP (Gross domestic product) inter anual rate of change. This Decision Support System might be helpful to assist disaster reduction planning by allowing decision takers, governs or institutions to achieve reliable recovery time estimations while a proper supervision and control of resilience indicators progress is performed and an open evaluation and scrutiny of applied policies is made.

There are currently few commercially available clinical products with neural network components-in fact only one has gained FDA approval, and none have yet been certified by the MDA. A major step in the certification process is proving the integrity of the neural network component, which cannot be done by reference to substantially equivalent certified products because they do not exist. Consequently, the case for the use of the new technology has to be made explicitly, by recourse to best practice in its design and evaluation. This paper discusses the use neural networks in medical applications, with reference to a case study from magnetic resonance spectroscopy (MRS).

The world of pharmacology is becoming increasingly dependent on the advances in the fields of genomics and proteomics. This dependency brings about the challenge of finding robust methods to analyze the complex data they generate. In this brief paper, we focus on the analysis of a specific type of proteins, the G protein-couple receptors, which are the target for over 15% of current drugs. We describe a kernel method of the manifold learning family for the analysis and intuitive visualization of their protein amino acid symbolic sequences. This method is shown to reveal the grouping structure of the sequences in a way that closely resembles the corresponding phylogenetic trees.

Time-dependent natural phenomena and artificial processes can often be quantitatively expressed as multivariate time series (MTS). As in any other process of knowledge extraction from data, the analyst can benefit from the exploration of the characteristics of MTS through data visualization. This visualization often becomes difficult to interpret when MTS are modelled using nonlinear techniques. Despite their flexibility, nonlinear models can be rendered useless if such interpretability is lacking. In this brief paper, we model MTS using Variational Bayesian Generative Topographic Mapping Through Time (VB-GTM-TT), a variational Bayesian variant of a constrained hidden Markov model of the manifold learning family defined for MTS visualization. We aim to increase its interpretability by taking advantage of two results of the probabilistic definition of the model: the explicit estimation of probabilities of transition between states described in the visualization space and the quantification of the nonlinear mapping distortion.

Background Biology is experiencing a gradual but fast transformation from a laboratory-centred science towards a data-centred one. As such, it requires robust data engineering and the use of quantitative data analysis methods as part of database curation. This paper focuses on G protein-coupled receptors, a large and heterogeneous super-family of cell membrane proteins of interest to biology in general. One of its families, Class C, is of particular interest to pharmacology and drug design. This family is quite heterogeneous on its own, and the discrimination of its several sub-families is a challenging problem. In the absence of known crystal structure, such discrimination must rely on their primary amino acid sequences. Methods We are interested not as much in achieving maximum sub-family discrimination accuracy using quantitative methods, but in exploring sequence misclassification behavior. Specifically, we are interested in isolating those sequences showing consistent misclassification, that is, sequences that are very often misclassified and almost always to the same wrong sub-family. Random forests are used for this analysis due to their ensemble nature, which makes them naturally suited to gauge the consistency of misclassification. This consistency is here defined through the voting scheme of their base tree classifiers. Results Detailed consistency results for the random forest ensemble classification were obtained for all receptors and for all data transformations of their unaligned primary sequences. Shortlists of the most consistently misclassified receptors for each subfamily and transformation, as well as an overall shortlist including those cases that were consistently misclassified across transformations, were obtained. The latter should be referred to experts for further investigation as a data curation task. Conclusion The automatic discrimination of the Class C sub-families of G protein-coupled receptors from their unaligned primary sequences shows clear limits. This study has investigated in some detail the consistency of their misclassification using random forest ensemble classifiers. Different sub-families have been shown to display very different discrimination consistency behaviors. The individual identification of consistently misclassified sequences should provide a tool for quality control to GPCR database curators. Electronic supplementary material The online version of this article (doi:10.1186/s12938-017-0357-4) contains supplementary material, which is available to authorized users.

A reliable predictor of protein-protein interaction sites is a fundamental tool to investigate protein functional interaction networks. Hidden Markov Support Vector Machines (HM-SVM) have been shown to be among the best performing methods on this task. Furthermore, it has been noted that the performance of a predictor improves when its input takes advantage of the difference between observed and predicted residue solvent accessibility. In this paper, for first time, we combine these elements and we present ISPRED2, a new HM-SVM-based method that overpasses the state of the art performance (Q2=0.71 and correlation=0.43). ISPRED2 consists of a sets of Python scripts aimed at integrating the different third-party software to obtain the final prediction.