Abstract: This is the summary of the 8th Workshop on Biomedical and Bioinformatics Challenges for Computer Science - BBC2015

Abstract: A central issue in biological data analysis is that uncertainty, resulting from different factors of variabilities, may change the effect of the events being investigated. Therefore, robustness is a fundamental step to be considered. Robustness refers to the ability of a process to cope well with uncertainties, but the different ways to model both the processes and the uncertainties lead to many alternative conclusions in the robustness analysis. In this paper we apply a framework allowing to deal with such questions for mass spectrometry data. Specifically, we provide robust decisions when testing hypothesis over a case/control population of subject measurements (i.e. proteomic profiles). To this concern, we formulate (i) a reference model for the observed data (i.e., graphs), (ii) a reference method to provide decisions (i.e., test of hypotheses over graph properties) and (iii) a reference model of variability to employ sources of uncertainties (i.e., random graphs). We apply these models to a real-case study, analyzing the mass spectrometry pofiles of the most common type of Renal Cell Carcinoma; the Clear Cell variant.

Abstract: Exploration of tissue sections by imaging mass spectrometry reveals abundance of different biomolecular ions in different sample spots, allowing finding region specific features. In this paper we present computational and statistical methods for investigation of protein biomarkers i.e. biological features related to presence of different pathological states. Proposed complete processing pipeline includes data pre-processing, detection and quantification of peaks by using Gaussian mixture modeling and identification of specific features for different tissue regions by performing permutation tests. Application of created methodology provides detection of proteins/peptides with concentration levels specific for tumor area, normal epithelium, muscle or saliva gland regions with high confidence.

Abstract: NGS data processing in metagenomics studies has to deal with noisy data that can contain a large amount of reading errors which are difficult to detect and account for. This work introduces a fuzzy indicator of reliability technique to facilitate solutions to this problem. It includes modified Hamming and Levenshtein distance functions that are aimed to be used as drop-in replacements in NGS analysis procedures which rely on distances, such as phylogenetic tree construction. The distances utilise fuzzy sets of reliable bases or an equivalent fuzzy logic, potentially aggregating multiple sources of base reliability.

Abstract: Workflows are becoming the new paradigm in bioinformatics. In general, bioinformatics problems are solved by interconnecting several small software pieces to perform complex analyses. This demands a minimal expertise to create, enact and monitor such tools compositions. In addition bioinformatics is immersed in the big-data territory, facing huge problems to analyse such amount of data. We have addressed these problems by integrating a tools management platform (Galaxy) and a Cloud infrastructure, which prevents moving the big datasets between different locations and allows the dynamic scaling of the computing resources depending on the user needs. The result is a user-friendly platform that facilitates the work of the end-users while performing their experiments, installed in a Cloud environment that includes authentication, security and big-data transfer mechanisms. To demonstrate the suitability of our approach we have integrated in the infrastructure an existing pairwise and multiple genome comparison tool which comprises the management of huge datasets and high computational demands.

Abstract: In the medical imaging field, iterative methods have become a hot topic of research due to their capacity to resolve the reconstruction problem from a limited number of projections. This gives a good possibility to reduce radiation exposure on patients during the data acquisition. However, due to the complexity of the data, the reconstruction process is still time consuming, especially for 3D cases, even though implemented on modern computer architecture. Time of the reconstruction and high radiation dose imposed on patients are two major drawbacks in computed tomography. With the aim to resolve them effectively, we adapted Least Square QR method with soft threshold filtering technique for few-view image reconstruction and present its numerical validation. The method is implemented using CUDA programming mode and compared to standard SART algorithm. The numerical simulations and qualitative analysis of the reconstructed images show the reliability of the presented method.