Main findings and advances in bioinformatics and biomedical engineeringIWBBIO 2018

We want to thank the great work done by the reviewers of each of the papers, together with the great interest shown by the editorial of BMC Bioinformatics in IWBBIO Conference. Special thanks to D. Omar El Bakry for his interest and great help to make this Special Issue. Thank the Ministry of Spain for the economic resources within the project with reference RTI2018-101674-B-I00.In the current supplement, we are proud to present seventeen relevant contributions from the 6th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2018), which was held during April 25-27, 2018 in Granada (Spain). These contributions have been chosen because of their quality and the importance of their findings.This research has been partially supported by the proyects with reference RTI2018-101674-B-I00 (Ministry of Spain) and B-TIC-414-UGR18 (FEDER, Junta Andalucia and UGR)

Efficient Parallel Feature Selection for Steganography Problems

The steganography problem consists of the identification of images hiding a secret message, which cannot be seen by visual inspection. This problem is nowadays becoming more and more important since the World Wide Web contains a large amount of images, which may be carrying a secret message. Therefore, the task is to design a classifier, which is able to separate the genuine images from the non-genuine ones. However, the main obstacle is that there is a large number of variables extracted from each image and the high dimensionality makes the feature selection mandatory in order to design an accurate classifier. This paper presents a new efficient parallel feature selection algorithm based on the Forward-Backward Selection algorithm. The results will show how the parallel implementation allows to obtain better subsets of features that allow the classifiers to be more accurate.TIN2007-60587, P07-TIC-02768 and P07-TIC-02906,TIC-3928Nokia Foundation, Finlan

Advances and challenges in Bioinformatics and Biomedical Engineering: IWBBIO 2020

This Supplement issue, presents five research articles which are distributed, mainly due to the subject they address, from the 8th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2020), which was held on line, during September, 30th–2nd October, 2020. These contributions have been chosen because of their quality and the importance of their findings. Those contributions were then invited to participate in this supplement for the following journals of BMC: BMC Bioinformatics and BMC Genomics. In the present Editorial in BMC journal, we summarize the contributions that provide a clear overview of the thematic areas covered by the IWBBIO conference, ranging from theoretical/review aspects to real-world applications of bioinformatic and biomedical engineeringPID2021-128317OB-I00 (Ministry of Spain)P20-00163 (FEDER, Junta Andalucia

Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm

This work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Project RTI-2018-101674-B-I00 and the projects from Junta de Andalucia B-TIC-414, A-TIC-530-UGR20 and P20-00163.In this contribution, a novel methodology for multi-class classification in the field of Parkinson’s disease is proposed. The methodology is structured in two phases. In a first phase, the most relevant volumes of interest (VOI) of the brain are selected by means of an evolutionary multi-objective optimization (MOE) algorithm. Each of these VOIs are subjected to volumetric feature extraction using the Three-Dimensional Discrete Wavelet Transform (3D-DWT). When applying 3D-DWT, a high number of coefficients is obtained, requiring the use of feature selection/reduction algorithms to find the most relevant features. The method used in this contribution is based on Mutual Redundancy (MI) and Minimum Maximum Relevance (mRMR) and PCA. To optimize the VOI selection, a first group of 550 MRI was used for the 5 classes: PD, SWEDD, Prodromal, GeneCohort and Normal. Once the Pareto Front of the solutions is obtained (with varying degrees of complexity, reflected in the number of selected VOIs), these solutions are tested in a second phase. In order to analyze the SVM classifier accuracy, a test set of 367 MRI was used. The methodology obtains relevant results in multi-class classification, presenting several solutions with different levels of complexity and precision (Pareto Front solutions), reaching a result of 97% as the highest precision in the test data.Spanish Government RTI-2018-101674-B-I00Junta de Andalucia B-TIC-414 A-TIC-530-UGR20 P20-0016

Media Competence in the Curriculum from Latin American Countries: A Systematic Review

The objective of this systematic review is to characterize scientific production on media competence (MC) in the curriculum of 33 countries in Latin America, starting with the analysis of four dimensions: geographical-temporal distribution, conceptual configuration, methodology, and the main curricular experiences and conclusions. After the identification and selection of the literature following the PRISMA protocol, 32 studies conducted between January, 2012, and December, 2021, were identified in the Web of Science (WoS), Scopus, ERIC (ProQuest), Dialnet (Plus) and Redalyc databases, after the application of the inclusion criteria. Among the findings, the following can be underlined: 1) there was a predominance of studies whose objective was to determine the presence of MC in the curriculum; 2) none of the countries had a course whose main objective was MC, nevertheless, the widespread presence of its dimensions suggests its inclusion as a cross-curricular component; 3) the region lacks studies centered on initial education, adult education, and the curricular basis of education for indigenous people. For this, opportunities must be offered to address future studies, projects, and curricular proposals that guarantee the development of critical, operational, and social competences, to face the socio-cultural demands and phenomena of the new media system

Media and Information Literacy in the Prescribed Curriculum: A Systematic Review on its Integration

The curricular integration of Media and Information Literacy (MIL) is crucial for cultivating informed, critical, and engaged citizens in contemporary society. It assists in addressing the challenges of the digital era and capitalizing on the opportunities presented by the ever-changing media landscape. Thus, the present systematic literature review uses the PRISMA guidelines to examine three dimensions in the process of integration of Media and Information Literacy (MIL) in the prescribed curriculum: formulation, implementation, and evaluation and challenges. Starting with the search criteria, 131 studies were found in the Web of Science, Scopus, ERIC, Dialnet and Google Scholar databases, published between January, 2013, and March, 2023, written in English, Spanish, Portuguese, and Turkish. The findings suggest that the will of the political sphere and the activism of the triad composed by passionate teachers, civil society, and academia, are key factors for promoting the introduction of MIL in formal education. Likewise, it is underlined that the evaluation of this education policy requires special attention, in order to guarantee the analysis of its reach, effectiveness, and capacity to adapt against the challenges that emerge in the media ecosystem. Thus, the intention is to provide up-to-date information for the creation of policies, research studies, and curricular content on this subject.Funding for open access publishing: Universidad de Huelva/CBUA. This work is conducted within the support of the Agora Research Group (HUM-648) at University of Huelva, the Euro-American Inter-university Research Network on Media Literacy for Citizenship (Red Alfamed) and the R + D Project “Alfabetización mediática y digital en jóvenes y adolescentes: Diagnóstico y estrategias de innovación educativa para prevenir riesgos y fomentar buenas prácticas en la Red”, financed by the Consejería de Universidades, Igualdad, Cultura y Deporte of Gobierno de Cantabria. E. G. Rojas-Estrada (CVU 1229049) is thankful to CONACyT (Mexico) for the scholarship granted under the “Doctorados en Ciencias y Humanidades en el Extranjero 2022” call

New advances in artificial neural networks and machine learning techniques

Peer ReviewedPostprint (published version

Integration of RNA-Seq data with heterogeneous microarray data for breast cancer profiling

Background: Nowadays, many public repositories containing large microarray gene expression datasets are available. However, the problem lies in the fact that microarray technology are less powerful and accurate than more recent Next Generation Sequencing technologies, such as RNA-Seq. In any case, information from microarrays is truthful and robust, thus it can be exploited through the integration of microarray data with RNA-Seq data. Additionally, information extraction and acquisition of large number of samples in RNA-Seq still entails very high costs in terms of time and computational resources.This paper proposes a new model to find the gene signature of breast cancer cell lines through the integration of heterogeneous data from different breast cancer datasets, obtained from microarray and RNA-Seq technologies. Consequently, data integration is expected to provide a more robust statistical significance to the results obtained. Finally, a classification method is proposed in order to test the robustness of the Differentially Expressed Genes when unseen data is presented for diagnosis. Results: The proposed data integration allows analyzing gene expression samples coming from different technologies. The most significant genes of the whole integrated data were obtained through the intersection of the three gene sets, corresponding to the identified expressed genes within the microarray data itself, within the RNA-Seq data itself, and within the integrated data from both technologies. This intersection reveals 98 possible technology-independent biomarkers. Two different heterogeneous datasets were distinguished for the classification tasks: a training dataset for gene expression identification and classifier validation, and a test dataset with unseen data for testing the classifier. Both of them achieved great classification accuracies, therefore confirming the validity of the obtained set of genes as possible biomarkers for breast cancer. Through a feature selection process, a final small subset made up by six genes was considered for breast cancer diagnosis. Conclusions: This work proposes a novel data integration stage in the traditional gene expression analysis pipeline through the combination of heterogeneous data from microarrays and RNA-Seq technologies. Available samples have been successfully classified using a subset of six genes obtained by a feature selection method. Consequently, a new classification and diagnosis tool was built and its performance was validated using previously unseen samples.This work was supported by Project TIN2015-71873-R (Spanish Ministry of Economy and Competitiveness -MINECO- and the European Regional Development Fund -ERDF)

Intelligent system based on genetic programming for atrial fibrillation classification

This article focuses on the development of intelligent classifiers in the area of biomedicine, focusing on the problem of diagnosing cardiac diseases based on the electrocardiogram (ECG), or more precisely, on the differentiation of the types of atrial fibrillations. First of all, we will study the ECG, and the treatment of the ECG in order to work with it with this specific pathology. In order to achieve this we will study different ways of elimination, in the best possible way, of any activity that is not caused by the auriculars. We will study and imitate the ECG treatment methodologies and the characteristics extracted from the electrocardiograms that were used by the researchers who obtained the best results in the Physionet Challenge, where the classification of ECG recordings according to the type of atrial fibrillation (AF) that they showed, was realized. We will extract a great amount of characteristics, partly those used by these researchers and additional characteristics that we consider to be important for the distinction previously mentioned. A new method based on evolutionary algorithms will be used to realize a selection of the most relevant characteristics and to obtain a classifier that will be capable of distinguishing the different types of this pathology