Abstract: [No abstract available]

Abstract: We consider how the orthology/paralogy information can be corrected in order to represent a gene tree, a problem that has recently gained interest in phylogenomics. Interestingly, the problem is related to the Minimum CoGraph Editing problem on the relation graph that represents orthology/paralogy information, where we want to minimize the number of edit operations on the given relation graph in order to obtain a cograph. In this paper we provide both theoretical and experimental results on the Minimum CoGraph Editing problem. On the theoretical side, we provide approximation algorithms for bounded degree relation graphs, for the general problem and for the problem restricted to deletion of edges. On the experimental side, we present a genetic algorithm for Minimum CoGraph Editing and we provide an experimental evaluation of the genetic algorithm on synthetic data.

Abstract: MicroRNAs (miRNAs) are small non-coding RNA molecules that have an important role in a wide range of biological processes, since they interact with specific mRNAs affecting the expression of the corresponding proteins. The role of miRNA can be deeply influenced by Single Nucleotide Polymorphisms (SNPs), in particular in their seed sites, since these variations may modify their affinity with particular transcripts, but they may also generate novel binding capabilities for specific miRNA binding sites or destroy them. Several computational tools for miRNA-target site predictions have been developed, but the obtained results are often not in agreement, making the study the binding sites hard, and the analysis of SNP effects even harder. For these reasons, we developed a web application called Rank miRNA, which allows to retrieve and aggregate the results of three prediction tools, but also to process and compare new input miRNA sequences, allowing the analysis of how variations impact on their function. Therefore, our tool is also able to predict the impact of SNPs (and any other kind of variations) on miRNA-mRNA binding capability and also to find the target genes of (potentially new) miRNA sequences. We evaluated the performance of Rank miRNA on specific human SNPs, which are likely to be involved in several mental disorder diseases, showing the potentiality of our tool in helping the study of miRNA-target interactions.

Abstract: In high-variation genomics datasets, such as found in metagenomics or complex polyploid genome analysis, error detection and variant calling are impeded by the difficulty in discerning sequencing errors from actual biological variation. Confirming base candidates with high frequency of occurrence is no longer a reliable measure, because of the natural variation and the presence of rare bases. This work employs machine learning models to classify bases into erroneous and rare variations, after preselecting potential error candidates with a weighted frequency measure, which aims to focus on unexpected variations by using the inter-sequence pairwise similarity. Different similarity measures are used to account for different types of datasets. Four machine learning models are tested.

Abstract: The human brain is a complex organ. An important first step to understand the function of such network is to model and to analyze its elements and connections, i.e. the connectome, in order to achieve a comprehensive description of the network. In this work we apply the graph theory formalisms to represent the connectomes. The human brain connectomes are usually derived from neuroimages; then an atlas-free random parcellation is used to define network nodes of individual brain networks. In this network domine, the question of comparison of the structure of networks arises. Such issue may be modeled as a network alignment (NA) problem. The use of different NA approaches, widely applied in molecular biology, has not been explored in relation to MRI connectomics. In this paper, we first defined the problem formally, then we applied three existing state of the art of multiple alignment algorithms (MNA) on diffusion MRI-derived brain networks and we compared the performances. The results confirm that MNA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes.

Abstract: Diffusion Weighted Image (DWI) methods (ADC and IVIM models) extract meaningful information about the microscopic motions of water of human tissues from MRIs. This is a non invasive method which plays an important role in the diagnosis of ischemic strokes, high grade gliomas or tumors. In the La Fe Polytechnic and University Hospital, the DWI methods aforementioned are used in clinical practice and Matlab is used as a development tool for his out of box performance and fast prototyping. However, each image may require hours to compute due to Matlab environment and interpreted functions. Because of this, its use in clinical practice is limited. In this paper we present three compiled versions on which different parallel paradigms based on multicore (OpenMP) and GPU (CUDA) are applied. These implementations have managed to reduce the computation time to less than one minute, therefore, it allows easing their use in daily clinical practice at a cheap acquisition cost.

Abstract: Computed tomography (CT) is one of the most widely used methods in Medical Imaging. Despite of its relevance in the diagnosis of diseases with a high impact in our society (such as cancer), it is one of the most potentially harmful modalities. CT requires a high X-ray dose to be induced to the patients. Solving the CT Image Reconstruction problem iteratively in order to approximate the solution allows working with only a subset of the input data required by direct methods. This directly implies a reduction of the radiation received by the patient and a strong reduction on the potential morbidity. Therefore, we aim to study the feasibility of such methods for their actual application, with the purpose of concluding if they are accurate and can obtain good quality images with a lower dose of X rays. This paper discusses the use of containers within a Grid Computing platform to perform a thorough study of all the possible congurations and parameters of various methods being developed to reconstruct CT images iteratively, which could lead to nd the optimal conguration of the parameters. The work compares two approaches for managing the software dependencies of the code: store the software libraries on a Storage Element and using containers for executing the job.

Abstract: This study proposes the use of a computational approach based on machine learning (ML) algorithms to build predictive models using eye tracking data. Our intention is to provide results that may support the study of medical investigation in the decision-making process in clinical bioethics, particularly in this work, in cases of euthanasia. The data used in the approach were collected from 75 students of the nursing undergraduate course using an eye tracker. The available data were processed through feature selection methods, and were later used to create models capable of predicting the euthanasia decision through ML algorithms. Statistical experiments showed that the predictive model resultant from the multilayer perceptron (MLP) algorithm led to the best performance compared with the other tested algorithms, presenting an accuracy of 90.7% and a mean area under the ROC curve of 0.90. Interesting knowledge (patterns and rules) for the studied bioethical decision-making was extracted using simulations with MLP models and inspecting the obtained decision-tree rules. The good performance shown by the obtained MLP predictive model demonstrates that the proposed investigation approach may be used to test scientific hypotheses related to visual attention and decision-making.

Abstract: In the article there are presented the results of recognition of seven emotional states (neutral, joy, sadness, surprise, anger, fear, disgust) based on facial expressions. Coefficients describing elements of facial expressions, registered for six subjects, were used as features. The features have been calculated for three-dimensional face model. The classification of features were performed using k-NN classifier and MLP neural network.

Abstract: Multiple Sclerosis (MS) is one of the most common neurodegenerative disorder that presents specific manifestations among which the impaired speech (known also as dysarthria). The evaluation of the speech plays a crucial role in the diagnosis and follow-up since the identification of anomalous patterns in vocal signal may represent a valid support to physician in diagnosis and monitoring of these neurological diseases. In this contribution, we present a method to perform voice analysis of neurologically impaired patients affected by MS aiming to early detection, differential diagnosis, and monitoring of disease progression. This method integrates two well-known methodologies to support the health structure in MS diagnosis in clinical practice. Acoustic analysis and vowel metric methodologies have been considered to implement this procedure to better define the pathological voices compared to healthy voices. Specifically, the method acquires and analyzes vocal signals performing features extraction and identifying possible important patterns useful to associate impaired speech with this neurological disease. The contribution consists in furnishing to physician a guide method to support MS trend. As result, this method furnishes patterns that could be valid indicators for physician in monitoring of patients affected by MS. Moreover, the procedure is appropriate to be used in early diagnosis that is critical in order to improve the patient's quality of life and to prolong it.