Realiza comparação entre softwares para auxiliar no processo de seleção das ferramentas adequadas para pesquisas bibliométricas. A partir de uma lista com 16 ferramentas apresentadas em publicações precedentes, seleciona quatro soluções com base em critérios de exclusão predefinidos pelos autores. A partir da exploração das funcionalidades presentes em cada uma das ferramentas, os resultados indicam que, destas quatro, o Biblioshiny apresenta o maior número de possibilidades em análises, embora apresente limitações nos relatórios visuais. O VOSviewer adota uma interface limpa e simples focada na visualização de redes bibliométricas. O Publish or Perish exibe a maior restrição em relação aos critérios comparativos, mas sua integração com o Google Acadêmico lhe confere vantagem em relação aos demais. Por fim, o CiteSpace oferece uma grande quantidade de parâmetros para formar suas visualizações de redes, mas sua interface desorganizada prejudica a experiência dos usuários. De acordo com o conteúdo analisado, constata um processo de renovação dos softwares bibliométricos. Conclui que não há uma única solução para as principais demandas da bibliometria e que a combinação entre as ferramentas tende a oferecer o melhor resultado aos pesquisadores.Palavras-chave: Bibliometria. Comparação de ferramentas. Ferramentas bibliométricas. Redes bibliométricas.

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This article discusses the information representation process based on the Moscovici's Social Representation Theory and domain analysis in Information Science. The aim was to identify mechanisms and constituent dimensions of social representation in collaborative tagging systems/social bookmarking systems. Scientific knowledge was defined as the object/phenomenon of representation in these systems; and the tag as the shareable structure of meaning that connects participants and resources. The empirical research involved descriptive statistical techniques applied to a corpora of tags available in CiteULike, which is a social tagging system developed for the academic community. The data analysis, performed in a sample of groups derived from the dataset, showed that the users' reuse of their own tags resembles the anchorage mechanism. The reuse of tags by other participants -in the same group -reveals some evidence of the objectification mechanism. Some speculation arose about the cognitive effort made by the individual, under group influence, with regard to the tagging activity, user's choice of resources, and sharing styles. Further studies on social bookmarking systems depend both on a "gain scale" of users and items tagged, requiring techniques and procedures redesigned by Information Science, Statistics, Network Analysis, Linguistics/Sociolinguistics and Social Psychology. Resumo O artigo discute um recorte na temática de representação da informação explorando a Teoria das Representações Sociais e a abordagem da análise de domínio da Ciência da Informação. Teve como objetivo geral identificar mecanismos e dimensões constituintes da representação social em grupos de participantes de sistemas de colaborativos de marcação (ou sistemas de marcação social). Definiu--se o conhecimento científico como objeto/fenômeno de representação em tais sistemas; delimitou-se a tag/marcação como unidade de registro e de significado; e se considerou os usuários, o conjunto total de tags, e os itens marcados como unidade de contexto. A pesquisa empírica envolveu técnicas de estatística descritiva aplicada à corpora de tags disponíveis em datasets do

Proteins can be grouped into families according to some features such as hydrophobicity, composition or structure, aiming to establish common biological functions. This paper presents MAHATMA -Memetic Algorithm-based Highly Adapted Tool for Motif Ascertainment -a system that was conceived to discover features (particular sequences of amino acids, or motifs) that occur very often in proteins of a given family but rarely occur in proteins of other families. These features can be used for the classification of unknown proteins, that is, to predict their function by analyzing their primary structure. Experiments were done with a set of enzymes extracted from the Protein Data Bank. The heuristic method used was based on Genetic Programming using operators specially tailored for the target problem. The final performance was measured using sensitivity (Se), specificity (Sp) and hit rate. The best results obtained for the enzyme dataset suggest that the proposed evolutionary computation method is effective in finding predictive features (motifs) for protein classification.

Proteins can be grouped into families according to some features such as hydrophobicity, composition or structure, aiming to establish the common biological functions. This paper presents a system that was conceived to discover features (particular sequences of amino acids, or motifs) that occur very often in proteins of a given family but rarely occur in proteins of other families. These features can be used for the classification of unknown proteins, that is, to predict their function by analyzing the primary structure. Runnings were done with the enzymes subset extracted from the Protein Data Bank. The heuristic method used was based on a genetic algorithm using specially tailored operators for the problem. Motifs found were used to build a decision tree using the C4.5 algorithm. The results were compared with motifs found by MEME, a freely available web tool. Another comparison was made with classification results of other two systems: a neural network-based tool and a hidden Markov model-based tool. The final performance was measured using sensitivity (Se) and specificity (Sp): similar results were obtained for the proposed tool (78.79 and 95.82) and the neural network-based tool (74.65 and 94.80, respectively), while MEME and HM-MER resulted in an inferior performance. The proposed system has the advantage of giving comprehensible rules when compared with the other approaches. These results obtained for the enzyme dataset suggest that the evolutionary computation method proposed is very efficient to find patterns for protein classification.

Recognized as a public health issue, self-medication is employed for pain ceasing and disease prevention, or even for aesthetic purposes, although it can compromise a person's health. Part of the information that leads to self-medication is disseminated via the Internet, in online discussion groups like the ones of Facebook, the major social media platform in the world and in Brazil. This paperwork aims at analyzing if, within such groups, there are incentives to self-medication for weight loss, and how such information could be classified for better understanding. The study is a descriptive survey, with a non-experimental approach. For data treatment and analysis is employed the Descending Hierarchical Classification (DHC or Reinert's Method), using IRaMuTeQ software. Results encompass the identification of classes of weight loss-related themes, which can contribute to self-medication with said intent. It is possible to apply methods of this study to other social media sites and other platforms of interaction throughout the Internet.

Almeja identificar métodos de machine learning empregados na automatização de revisões sistemáticas. Analisa, baseado na recomendação Preferred Reporting Items for Systematic Reviews, 29 de 211 documentos científicos recuperados das bases Web of Science e Scopus, sem restrição de idioma ou recorte temporal. Demonstra a tendência de crescimento da produção relacionada ao tema, com 65,51% dos registros publicados após 2016. Indica o interesse dos pesquisadores em técnicas de text mining, sendo a palavra-chave mais utilizada pelos autores. Em relação aos métodos encontrados, evidencia o algoritmo Support Vector Machine como o mais frequente, sendo utilizado em oito trabalhos, seguido pelas heurísticas Redes Neurais Artificiais e Naïve Bayes, com duas aplicações cada. Ressalta a aplicação majoritária dos métodos à área médica. Conclui, entretanto, que nenhuma das ferramentas identificadas oferece uma solução aplicável a qualquer área do conhecimento.