Reconstructing transcriptional regulatory networks using data integration and text mining

Transcriptional Regulatory Networks (TRNs) are powerful tool for representing several interactions that occur within a cell. Recent studies have provided information to help researchers in the tasks of building and understanding these networks. One of the major sources of information to build TRNs is biomedical literature. However, due to the rapidly increasing number of scientific papers, it is quite difficult to analyse the large amount of papers that have been published about this subject. This fact has heightened the importance of Biomedical Text Mining approaches in this task. Also, owing to the lack of adequate standards, as the number of databases increases, several inconsistencies concerning gene and protein names and identifiers are common. In this work, we developed an integrated approach for the reconstruction of TRNs that retrieve the relevant information from important biological databases and insert it into a unique repository, named KREN. Also, we applied text mining techniques over this integrated repository to build TRNs. However, was necessary to create a dictionary of names and synonyms associated with these entities and also develop an approach that retrieves all the abstracts from the related scientific papers stored on PubMed, in order to create a corpora of data about genes. Furthermore, these tasks were integrated into @Note, a software system that allows to use some methods from the Biomedical Text Mining field, including an algorithms for Named Entity Recognition (NER), extraction of all relevant terms from publication abstracts, extraction relationships between biological entities (genes, proteins and transcription factors). And finally, extended this tool to allow the reconstruction Transcriptional Regulatory Networks through using scientific literature.

Text mining na análise de sentimentos em contextos de big data

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Mechanism of extracellular silver nanoparticles synthesis by Stereum hirsutum and Fusarium oxysporum

The increasing interest for greener and biological methods of synthesis has led to the development of non-toxic and comparatively more bioactive nanoparticles. Unlike physical and chemical methods of nanoparticle synthesis, microbial synthesis in general and mycosynthesis in particular is cost-effective and environment-friendly. However, different aspects, such as the rate of synthesis, monodispersity and downstream processing, need to be improved. Many fungal-based mechanisms have been proposed for the formation of silver nanoparticles (AgNPs), mainly those involving the presence of nitrate reductase, which has been detected in filtered fungus cell used for AgNPs production. There is a general acceptance that nitrate reductase is the main responsible for the reduction of Ag ions for the formation of AgNPs. However, this generally accepted mechanism for fungal AgNPs production is not totally understood. In order to elucidate the molecules participating in the mechanistic formation of metal nanoparticles, the current study is focused on the enzymes and other organic compounds involved in the biosynthesis of AgNPs. The use of each free fungal mycelium of both Stereum hirsutum and Fusarium oxysporum will be assessed. In order to identify defective mutants on the nitrate reductase structural gene niaD, fungal cultures of S.hirsutum and F.oxysporum will be selected by chlorate resistance. In addition, in order to verify if each compound identified as key-molecule influenced on the production of nanoparticles, an in vitro assay using different nitrogen sources will be developed. Lately, fungal extracellular enzymes will be measured and an in vitro assay will be done. Finally, The nanoparticle formation and its characterization will be evaluated by UV-visible spectroscopy, electron microscopy (TEM), X-ray diffraction analysis (XRD), Fourier transforms infrared spectroscopy (FTIR), and LC-MS/MS.

Column-based databases: estudo exploratório no âmbito das bases de dados NoSQL

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Metaphorisms in programming

This paper introduces the metaphorism pattern of relational specification and addresses how specification following this pattern can be refined into recursive programs. Metaphorisms express input-output relationships which preserve relevant information while at the same time some intended optimization takes place. Text processing, sorting, representation changers, etc., are examples of metaphorisms. The kind of metaphorism refinement proposed in this paper is a strategy known as change of virtual data structure. It gives sufficient conditions for such implementations to be calculated using relation algebra and illustrates the strategy with the derivation of quicksort as example.

Escrever para aprender através dos textos literários e não literários

Relatório de estágio de mestrado em Educação Pré-Escolar e Ensino do 1.º Ciclo do Ensino Básico