Implementing a webserver for managing and detecting viral fusion proteins

Dissertação de mestrado em BioinformáticaViral fusion proteins are essential to allow enveloped viruses (such as Influenza, Dengue, HIV and SARS-CoV-2) to enter their hosts’ cells, in a mechanism referred to as membrane fusion. This makes these proteins (with special relevance to their fusion peptides, the com ponent of the protein that can insert into the host’s membrane by itself) interesting potential therapeutic targets for preventing or treating for some well-known diseases. However, there is no centralized data repository containing all the relevant information regarding viral fusion proteins. With that in mind, the main purpose of this work is to develop a CRUD (Create, Read, Update and Delete) web server that will allow researchers to find all the necessary data regarding viral fusion proteins, through an easy-to-use web interface. The web application will also contain other bioinformatics functionalities, such as sequence alignment (through BLAST, Clustal and Weblogo) to allow researchers to retrieve key pieces of information regarding a fusion protein, as well as machine learning models capable of predicting the location of fusion peptides inside the viral fusion protein sequence. The implementation of the server used Django as its back-end, retrieving the data from a MySQL database, and Angular as its front-end. The main result of the work is, therefore, a working webserver, with a web interface available online through the URL: https://viralfp.bio.di.uminho.pt/. The web application allows users to explore the gathered data related to viral fusion proteins in a user-friendly way. This tool contains all the proposed functionalities and machine learning models. As expected in an application’s development, there are several aspects that require future work to improve the usefulness of this tool to the scientific community.Proteínas virais de fusão são essenciais para que vírus encapsulados (tais como Influenza, Dengue, HIV e SARS-CoV-2) sejam capazes de se inserir nos seus hospedeiros, num mecanismo conhecido como fusão membranar. Por este motivo, estas proteínas (com especial relevância para os seus péptidos de fusão, que são a parte da proteína que se insere na membrana do hospedeiro por si mesma) são potenciais alvos terapêuticos interessantes para prevenir ou tratar algumas doenças bem conhecidas. No entanto, não existe nenhuma fonte de dados centralizada disponível que contenha toda a informação relativa a proteínas virais de fusão. Sabendo isto, o propósito primário deste trabalho é desenvolver um web server CRUD (Create, Read, Update and Delete) que permitira investigadores encontrar toda a informação necessária relacionada com proteínas virais de fusão, através de um interface user-friendly. Este web server incluirá outras funcionalidades bioinformáticas, tais como ferramentas de alinhamento de sequências (como BLAST, Clustal e Weblogo), que permitirá investigadores extrair informações importantes acerca de uma proteína de fusão. Por fim, incluir a modelos de machine learning capazes de prever a localização de péptidos de fusão na sequência da proteína de fusão. A implementação do servidor usou Django como seu back-end, que permite extrair a informação da base de dados MySQL, e Angular como front-end. O principal resultado deste trabalho é, portanto, um web server funcional, com a interface web disponível através do URL: https://viralfp.bio.di.uminho.pt/. Esta aplicação web permite que utilizadores possam explorar a informação acumulada acerca de proteínas virais de fusão através de uma interface user-friendly. Esta ferramenta contém todas as funcionalidades e modelos de machine learning propostos. Como seria de esperar no desenvolvimento de uma aplicação, existem vários aspetos que requerem trabalho futuro para melhorar a utilidade desta ferramenta para a comunidade científica.First and foremost, this dissertation is funded by COMPETE 2020, Portugal 2020 and FCT - Fundação para a Ciência e a Tecnologia, under the project ”Using computational and experimental methods to provide a global characterization of viral fusion peptides”, through the funding program ”02/SAICT/2017 - Projetos de Investigação Científica e Desenvolvimento Tecnologico (IC&DT)”, with the reference ”NORTE-01-0145-FEDER-028200”, who I would like to thank for their trust

24th International Conference on Information Modelling and Knowledge Bases

In the last three decades information modelling and knowledge bases have become essentially important subjects not only in academic communities related to information systems and computer science but also in the business area where information technology is applied. The series of European – Japanese Conference on Information Modelling and Knowledge Bases (EJC) originally started as a co-operation initiative between Japan and Finland in 1982. The practical operations were then organised by professor Ohsuga in Japan and professors Hannu Kangassalo and Hannu Jaakkola in Finland (Nordic countries). Geographical scope has expanded to cover Europe and also other countries. Workshop characteristic - discussion, enough time for presentations and limited number of participants (50) / papers (30) - is typical for the conference. Suggested topics include, but are not limited to: 1. Conceptual modelling: Modelling and specification languages; Domain-specific conceptual modelling; Concepts, concept theories and ontologies; Conceptual modelling of large and heterogeneous systems; Conceptual modelling of spatial, temporal and biological data; Methods for developing, validating and communicating conceptual models. 2. Knowledge and information modelling and discovery: Knowledge discovery, knowledge representation and knowledge management; Advanced data mining and analysis methods; Conceptions of knowledge and information; Modelling information requirements; Intelligent information systems; Information recognition and information modelling. 3. Linguistic modelling: Models of HCI; Information delivery to users; Intelligent informal querying; Linguistic foundation of information and knowledge; Fuzzy linguistic models; Philosophical and linguistic foundations of conceptual models. 4. Cross-cultural communication and social computing: Cross-cultural support systems; Integration, evolution and migration of systems; Collaborative societies; Multicultural web-based software systems; Intercultural collaboration and support systems; Social computing, behavioral modeling and prediction. 5. Environmental modelling and engineering: Environmental information systems (architecture); Spatial, temporal and observational information systems; Large-scale environmental systems; Collaborative knowledge base systems; Agent concepts and conceptualisation; Hazard prediction, prevention and steering systems. 6. Multimedia data modelling and systems: Modelling multimedia information and knowledge; Contentbased multimedia data management; Content-based multimedia retrieval; Privacy and context enhancing technologies; Semantics and pragmatics of multimedia data; Metadata for multimedia information systems. Overall we received 56 submissions. After careful evaluation, 16 papers have been selected as long paper, 17 papers as short papers, 5 papers as position papers, and 3 papers for presentation of perspective challenges. We thank all colleagues for their support of this issue of the EJC conference, especially the program committee, the organising committee, and the programme coordination team. The long and the short papers presented in the conference are revised after the conference and published in the Series of “Frontiers in Artificial Intelligence” by IOS Press (Amsterdam). The books “Information Modelling and Knowledge Bases” are edited by the Editing Committee of the conference. We believe that the conference will be productive and fruitful in the advance of research and application of information modelling and knowledge bases. Bernhard Thalheim Hannu Jaakkola Yasushi Kiyok

Literature Review of the Recent Trends and Applications in various Fuzzy Rule based systems

Fuzzy rule based systems (FRBSs) is a rule-based system which uses linguistic fuzzy variables as antecedents and consequent to represent human understandable knowledge. They have been applied to various applications and areas throughout the soft computing literature. However, FRBSs suffers from many drawbacks such as uncertainty representation, high number of rules, interpretability loss, high computational time for learning etc. To overcome these issues with FRBSs, there exists many extensions of FRBSs. This paper presents an overview and literature review of recent trends on various types and prominent areas of fuzzy systems (FRBSs) namely genetic fuzzy system (GFS), hierarchical fuzzy system (HFS), neuro fuzzy system (NFS), evolving fuzzy system (eFS), FRBSs for big data, FRBSs for imbalanced data, interpretability in FRBSs and FRBSs which use cluster centroids as fuzzy rules. The review is for years 2010-2021. This paper also highlights important contributions, publication statistics and current trends in the field. The paper also addresses several open research areas which need further attention from the FRBSs research community.Comment: 49 pages, Accepted for publication in ijf

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality

Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data

Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques. Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic