Abstract: Contemporary development in personalized medicine based both on extended clinical records and implementation of different high-throughput “omics” technologies has generated large amounts of data. To make use of these data, new approaches need to be developed for their search, storage, analysis, integration and processing. In this paper we suggest an approach for integration of data from diverse domains and various information sources enabling extraction of novel knowledge in cancer studies. Its application can contribute to the early detection and diagnosis of cancer as well as to its proper personalized treatment. The data used in our research consist of clinical records from two particular cancer studies with different factors and different origin, and also include gene expression datasets from different high-throughput technologies – microarray and next generation sequencing. An especially developed workflow, able to deal effectively with the heterogeneity of data and the enormous number of relations between patients and proteins, is used to automate the data integration process. During this process, our software tool performs advanced search for additional expressed protein relationships in a set of available knowledge sources and generates semantic links to them. As a result, a set of hidden common expressed protein mutations and their subsequent relations with patients is generated in the form of new knowledge about the studied cancer cases.

Abstract: Purkinje fibers are fundamental structures in the process of the electrical stimulation of the heart. To allow the contraction of the ventricle muscle, these fibers need to stimulate the myocardium in a syn- chronized manner. However, certain changes in the properties of these fibers may provide a desynchronization of the heart rate. This can oc- cur through failures in the propagation of the electrical stimulus due to conduction blocks occurring at the junctions that attach the Purkinje fibers to the ventricle muscle. This condition is considered a risk state for cardiac arrhythmias. The aim of this work is to investigate and analyze which properties may affect the propagation velocity and activation time of the Purkinje fibers, such as cell geometry, conductivity, coupling of the fibers with ventricular tissue and number of bifurcations in the network. In order to reach this goal, several Purkinje networks were generated by varying these parameters to perform a sensibility analysis. For the implementation of the computational model, the monodomain equation was used to describe mathematically the phenomenon and the numeri- cal solution was calculated using the Finite Volume Method. The results of the present work were in accordance with those obtained in the lit- erature: the model was able to reproduce certain behaviors that occur in the propagation velocity and activation time of the Purkinje fibers. In addition, the model was able to reproduce the characteristic delay in propagation that occurs at the Purkinje-muscle junctions.

Abstract: Tertiary protein structure prediction is one of the most challenging problems in Structural Bioinformatics, and it is an NP-Complete problem in computational complexity theory. The complexity is related to the significant number of possible conformations a single protein can assume. Metaheuristics became useful algorithms to find feasible solutions in viable computational time since exact algorithms are not capable. However, these stochastic methods are highly-dependent from parameter tuning for finding the balance between exploitation and exploration capabilities. Thus, self-adaptive techniques were created to handle the parameter definition task, since it is time-consuming. In this paper, we enhance the Self-Adaptive Differential Evolution with problem-domain knowledge provided by the angle probability list approach, comparing it with every single mutation we used to compose our set of mutation operators. Moreover, a population diversity metric is used to analyze the behavior of each one of them. The proposed method was tested with ten protein sequences with different folding patterns. Results obtained showed that the self-adaptive mechanism has a better balance between the search capabilities, providing better results regarding root mean square deviation and potential energy than the non-adaptive single-mutation methods.

Abstract: The prediction of protein structures is one of the most challenging problems in Structural Bioinformatics. In this paper, we present some variations of the Artificial Bee Colony algorithm to deal with the problem by the introducing of multi-objective optimization and knowledge from experimental proteins through the use of protein contact maps. Obtained results indicate that our approaches surpassed their previous version, demonstrating the importance of adapting the algorithm to deal with the particularities of the problem.