Abstract: In this paper, we present the optimization and parallelization of a state-of-the-art algorithm for automatic classification, aiming to perform real time classification of clinical data. The parallelization has been carried out so that the algorithm can be used in real time in standard computers, or in high performance computing servers. The fastest versions have been obtained carrying out most of the computations in Graphics processing Units (GPUs). The algorithms obtained have been tested in a case of automatic classification of electroencephalographic signals from patients.

Abstract: MicroRNAs (miRNAs) are small non-coding RNAs with a key role in the post-transcriptional gene expression regularization, thanks to their ability to link with the target mRNA through the complementary base pairing mechanism. Given their role, it is important to identify their targets and, to this purpose, different tools were proposed to solve this problem. However, their results can be very different, so the community is now moving toward the deployment of integration tools, which should be able to perform better than the single ones. As Machine and Deep Learning algorithms are now in their popular years, we developed different classifiers from both areas to verify their ability to recognize possible miRNA-mRNA interactions and evaluated their performance, showing the potentialities and the limits that those algorithms have in this field.

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.

Abstract: The number of computational models in cardiac research has grown over the last decades. Every year new models with different assumptions appear in the literature dealing with differences in interspecies cardiac properties. Generally, these new models update the physiological knowledge using new equations which reflect better the molecular basis of process. New equations require the fitting of parameters to previously known experimental data or even, in some cases, simulated data. This work studies and proposes a new method of parameter adjustment based on Polynomial Chaos and Genetic Algorithm to find the best values for the parameters upon changes in the formulation of ionic channels. It minimizes the search space and the computational cost combining it with a Sensitivity Analysis. We use the analysis of different models of L-type calcium channels to see that by reducing the number of parameters, the quality of the Genetic Algorithm dramatically improves. In addition, we test whether the use of the Polynomial Chaos Expansions improves the process of the Genetic Algorithm search. We find that it reduces the Genetic Algorithm execution in an order of 10³ times in the case studied here, maintaining the quality of the results. We conclude that polynomial chaos expansions can improve and reduce the cost of parameter adjustment in the development of new models.

Abstract: Digital Medical Imaging is increasingly being used in clinical routine and research. As a consequence, the workload in medical imaging departments in hospitals has multiplied by over 20 in the last decade. Medical Image processing requires intensive computing resources not available at hospitals, but which could be provided by public clouds. The article analyses the requirements of processing digital medical images and introduces a cloud-based architecture centred on a DevOps approach to deploying resources on demand, tailored to different types of workloads (based on the request of resources and the expected execution time). Results presented show a low overhead and high flexibility executing a lung disease biomarker on a public cloud.

Abstract: Molecular Docking is a methodology that deals with the problem of predicting the non-covalent binding of a receptor and a ligand at an atomic level to form a stable complex. Because the search space of possible conformations is vast, molecular docking is classified in computational complexity theory as a NP-hard problem. Because of the high complexity, exact methods are not efficient and several metaheuristics have been proposed. However, these methods are very dependent on parameter settings and search mechanism definitions, which requires approaches able to self-adapt these configurations along the optimization process. We proposed and developed a novel self-adaptive coordination of local search operators in a Multimeme Memetic Algorithm. The approach is based on the Biased Random Key Genetic Algorithm enhanced with four local search algorithms. The self-adaptation of methods and radius perturbation in local improvements works under a proposed probability function, which measures their performance to best guide the search process. The methods have been tested on a test set based on HIV-protease and compared to existing tools. Statistical test performed on the results shows that this approach reaches better results than a non-adaptive algorithm and is competitive with traditional methods.

Abstract: Algebraic factorization methods applied to the discipline of Computerized Tomography (CT) Medical Imaging Reconstruction involve a high computational cost. Since these techniques are significantly slower than the traditional analytical ones and time is critical in this field, we need to employ parallel implementations in order to exploit the machine resources and obtain efficient reconstructions. In this paper, we analyze the performance of the sparse QR decomposition implemented on SuiteSparseQR factorization package applied to the CT reconstruction problem. We explore both the parallelism provided by BLAS threads and the use of the Householder reflections to reconstruct multiple slices at once efficiently. Combining both strategies, we can boost the performance of the reconstructions and implement a reliable and competitive method that gets high-quality CT images.