Denise Fukumi Tsunoda

Automatic motif discovery in an enzyme database using a genetic algorithm-based approach

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 HMMER 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.

An evolutionary approach for motif discovery and transmembrane protein classification

Proteins can be grouped into families according to their biological functions. This paper presents a system, named GAMBIT, which discovers motifs (particular sequences of amino acids) that occur very often in proteins of a given family but rarely occur in proteins of other families. These motifs are used to classify unknown proteins, that is, to predict their function by analyzing the primary structure. To search for motifs in proteins, we developed a GA with specially tailored operators for the problem. GAMBIT was compared with MEME, a web tool for finding motifs in the TransMembrane Protein DataBase. Motifs found by both methods were used to build a decision tree and classification rules, using, respectively, C4.5 and Prism algorithms. Motifs found by GAMBIT led to significantly better results, when compared with those found by MEME, using both classification algorithms.

MAHATMA: A Genetic Programming-Based Tool for Protein Classification

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 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) and specificity (Sp). The best results obtained for the enzyme dataset suggest that the proposed evolutionary computation method is very effective to find predictive features (motifs) for protein classification.

A Proposed Model for Consumer-Based Brand Equity Analysis on Social Media Using Data Mining and Social Network Analysis

ABSTRACT Online social networks have expanded their “virtual borders,” making the Internet more like an environment of social interaction than a business tool. However, even before the emergence and expansion of social media, marketing professionals were interested in identifying consumers perceptions about brands. Thus, operational models have been proposed to facilitate such a task. Those models, however, can be expensive and inconvenient, since the models use questionnaires for data collection. To help overcome this problem, this article proposes a model for brand equity analysis from the consumer perspective expressed in social networks using opinion mining techniques and social network analysis. The application of the proposed model on data collected from Twitter made it possible to analyze five brand equity dimensions: brand awareness, brand loyalty, perceived sentiment, perceived quality, and brand associations. The results reached by the application of the model show that brand equity can be analyzed from data retrieved from virtual social networks, disclosing how consumers perceive brands in such an environment, without using questionnaires, enabling different brands in different contexts. Those data can be analyzed under both objective and replicable criteria for each of the brand equity elements that make up the model.

Elementos da teoria da representação social em sistemas colaborativos de marcação

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.Keywords: Information representation. Information sharing styles. Social bookmarking systems. Social representation theory.

Methodology for monitoring the difusion of innovation in scientific communication channels

Introduccion: la difusion de las innovaciones de productos o tecnologias se basa en el uso de diferentes canales de comunicacion. Las adaptaciones o optimizaciones derivadas de las innovaciones se difunden entre los participantes de estos canales y se pueden agregar en categorias bajo cuatro condiciones de adopcion (existente, proactiva, pretension, reactiva). El monitoreo y visualizacion de esas condiciones contribuiria a la exploracion del estado del arte de la innovacion en seguimientos periodicos o basado en un ambito geografico especifico y aplicado a algunos o multiples canales. Objectivos: presenta una propuesta metodologica para monitorear la difusion de la innovacion y sus condiciones de adopcion en canales de comunicacion cientifica. Procedimientos metodologicos: describe los principales elementos de la Teoria de La Difusion de Innovaciones y su modelo conceptual derivado. La metodologia propuesta presenta dos etapas sucesivas probadas en corpora de documentos presentados en eventos nacionales e internacionales relacionados con el Sistema de Gestion de Revistas Electronicas/Open Journal System. Resultados: aplicado al entorno de prueba, la metodologia propuesta llevo a la creacion de veintinueve categorias relacionadas con las condiciones de adopcion de la innovacion. Utilizaranse el UCINET 6,434 y el Netdraw para manipular tres matrices de correlacion y generar graficos. El analisis se baso en escalonamiento multidimensional (MDS) y las frecuencias absolutas Conclusiones: la metodologia fue validada en el entorno de prueba, con algunas observaciones acerca de las etapas y analisis de datos. Si se hace regularmente, podria ser posible identificar tendencias o predecir la adopcion de la innovacion como una tarea fundamental en el proceso de monitoreo.

Metodologia para o monitoramento da difusão de inovação em canais de comunicação científica

Introduccion: la difusion de las innovaciones de productos o tecnologias se basa en el uso de diferentes canales de comunicacion. Las adaptaciones o optimizaciones derivadas de las innovaciones se difunden entre los participantes de estos canales y se pueden agregar en categorias bajo cuatro condiciones de adopcion (existente, proactiva, pretension, reactiva). El monitoreo y visualizacion de esas condiciones contribuiria a la exploracion del estado del arte de la innovacion en seguimientos periodicos o basado en un ambito geografico especifico y aplicado a algunos o multiples canales. Objectivos: presenta una propuesta metodologica para monitorear la difusion de la innovacion y sus condiciones de adopcion en canales de comunicacion cientifica. Procedimientos metodologicos: describe los principales elementos de la Teoria de La Difusion de Innovaciones y su modelo conceptual derivado. La metodologia propuesta presenta dos etapas sucesivas probadas en corpora de documentos presentados en eventos nacionales e internacionales relacionados con el Sistema de Gestion de Revistas Electronicas/Open Journal System. Resultados: aplicado al entorno de prueba, la metodologia propuesta llevo a la creacion de veintinueve categorias relacionadas con las condiciones de adopcion de la innovacion. Utilizaranse el UCINET 6,434 y el Netdraw para manipular tres matrices de correlacion y generar graficos. El analisis se baso en escalonamiento multidimensional (MDS) y las frecuencias absolutas Conclusiones: la metodologia fue validada en el entorno de prueba, con algunas observaciones acerca de las etapas y analisis de datos. Si se hace regularmente, podria ser posible identificar tendencias o predecir la adopcion de la innovacion como una tarea fundamental en el proceso de monitoreo.

Metodologia para o monitoramento da difusão de inovação em canais de comunicação científica; Metodología para el seguimiento de la difusión de la innovación en canales de comunicación científica

Introduccion: la difusion de las innovaciones de productos o tecnologias se basa en el uso de diferentes canales de comunicacion. Las adaptaciones o optimizaciones derivadas de las innovaciones se difunden entre los participantes de estos canales y se pueden agregar en categorias bajo cuatro condiciones de adopcion (existente, proactiva, pretension, reactiva). El monitoreo y visualizacion de esas condiciones contribuiria a la exploracion del estado del arte de la innovacion en seguimientos periodicos o basado en un ambito geografico especifico y aplicado a algunos o multiples canales. Objectivos: presenta una propuesta metodologica para monitorear la difusion de la innovacion y sus condiciones de adopcion en canales de comunicacion cientifica. Procedimientos metodologicos: describe los principales elementos de la Teoria de La Difusion de Innovaciones y su modelo conceptual derivado. La metodologia propuesta presenta dos etapas sucesivas probadas en corpora de documentos presentados en eventos nacionales e internacionales relacionados con el Sistema de Gestion de Revistas Electronicas/Open Journal System. Resultados: aplicado al entorno de prueba, la metodologia propuesta llevo a la creacion de veintinueve categorias relacionadas con las condiciones de adopcion de la innovacion. Utilizaranse el UCINET 6,434 y el Netdraw para manipular tres matrices de correlacion y generar graficos. El analisis se baso en escalonamiento multidimensional (MDS) y las frecuencias absolutas Conclusiones: la metodologia fue validada en el entorno de prueba, con algunas observaciones acerca de las etapas y analisis de datos. Si se hace regularmente, podria ser posible identificar tendencias o predecir la adopcion de la innovacion como una tarea fundamental en el proceso de monitoreo.

A genetic programming method for protein motif discovery and protein classification

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, specificity 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.

A Hybrid Evolutionary Approach for the Protein Classification Problem

This paper proposes a hybrid algorithm that combines characteristics of both Genetic Programming (GP) and Genetic Algorithms (GAs), for discovering motifs in proteins and predicting their functional classes, based on the discovered motifs. In this algorithm, individuals are represented as IF-THEN classification rules. The rule antecedent consists of a combination of motifs automatically extracted from protein sequences. The rule consequent consists of the functional class predicted for a protein whose sequence satisfies the combination of motifs in the rule antecedent. The system can be used in two different ways. First, as a stand-alone classification system, where the evolved classification rules are directly used to predict the functional classes of proteins. Second, the system can be used just as an attribute construction method, discovering motifs that are given, as predictor attributes, to another classification algorithm. In this usage of the system, a classical decision tree induction algorithm was used as the classifier. The proposed system was evaluated in these two scenarios and compared with another Genetic Algorithm designed specifically for the discovery of motifs --- and therefore used only as an attribute construction algorithm. This comparison was performed by mining an enzyme data set extracted from the Protein Data Bank. The best results were obtained when using the proposed hybrid GP/GA as an attribute construction algorithm and performing the classification (using the constructed attributes) with the decision tree induction algorithm.