The Experts below are selected from a list of 276 Experts worldwide ranked by ideXlab platform

Russ B. Altman - One of the best experts on this subject based on the ideXlab platform.

  • Using ODIN for a PharmGKB revalidation experiment
    Database : the journal of biological databases and curation, 2012
    Co-Authors: Fabio Rinaldi, Simon Clematide, Yael Garten, Michelle Whirl-carrillo, Li Gong, Joan M. Hebert, Katrin Sangkuhl, Caroline F. Thorn, Teri E. Klein, Russ B. Altman
    Abstract:

    The need for efficient Text-Mining Tools that support curation of the biomedical literature is ever increasing. In this article, we describe an experiment aimed at verifying whether a Text-Mining Tool capable of extracting meaningful relationships among domain entities can be successfully integrated into the curation workflow of a major biological database. We evaluate in particular (i) the usability of the system's interface, as perceived by users, and (ii) the correlation of the ranking of interactions, as provided by the Text-Mining system, with the choices of the curators.

  • Pharmspresso: a Text Mining Tool for extraction of pharmacogenomic concepts and relationships from full Text.
    BMC Bioinformatics, 2009
    Co-Authors: Yael Garten, Russ B. Altman
    Abstract:

    Background Pharmacogenomics studies the relationship between genetic variation and the variation in drug response phenotypes. The field is rapidly gaining importance: it promises drugs targeted to particular subpopulations based on genetic background. The pharmacogenomics literature has expanded rapidly, but is dispersed in many journals. It is challenging, therefore, to identify important associations between drugs and molecular entities – particularly genes and gene variants, and thus these critical connections are often lost. Text Mining techniques can allow us to convert the free-style Text to a computable, searchable format in which pharmacogenomic concepts (such as genes, drugs, polymorphisms, and diseases) are identified, and important links between these concepts are recorded. Availability of full Text articles as input into Text Mining engines is key, as literature abstracts often do not contain sufficient information to identify these pharmacogenomic associations.

  • Pharmspresso: a Text Mining Tool for extraction of pharmacogenomic concepts and relationships from full Text
    BMC Bioinformatics, 2009
    Co-Authors: Yael Garten, Russ B. Altman
    Abstract:

    Abstract Background Pharmacogenomics studies the relationship between genetic variation and the variation in drug response phenotypes. The field is rapidly gaining importance: it promises drugs targeted to particular subpopulations based on genetic background. The pharmacogenomics literature has expanded rapidly, but is dispersed in many journals. It is challenging, therefore, to identify important associations between drugs and molecular entities – particularly genes and gene variants, and thus these critical connections are often lost. Text Mining techniques can allow us to convert the free-style Text to a computable, searchable format in which pharmacogenomic concepts (such as genes, drugs, polymorphisms, and diseases) are identified, and important links between these concepts are recorded. Availability of full Text articles as input into Text Mining engines is key, as literature abstracts often do not contain sufficient information to identify these pharmacogenomic associations. Results Thus, building on a Tool called Textpresso, we have created the Pharmspresso Tool to assist in identifying important pharmacogenomic facts in full Text articles. Pharmspresso parses Text to find references to human genes, polymorphisms, drugs and diseases and their relationships. It presents these as a series of marked-up Text fragments, in which key concepts are visually highlighted. To evaluate Pharmspresso, we used a gold standard of 45 human-curated articles. Pharmspresso identified 78%, 61%, and 74% of target gene, polymorphism, and drug concepts, respectively. Conclusion Pharmspresso is a Text analysis Tool that extracts pharmacogenomic concepts from the literature automatically and thus captures our current understanding of gene-drug interactions in a computable form. We have made Pharmspresso available at http://pharmspresso.stanford.edu.

Mateus David Finco - One of the best experts on this subject based on the ideXlab platform.

  • using a Text Mining Tool to support Text summarization
    International Conference on Advanced Learning Technologies, 2012
    Co-Authors: Eliseo Berni Reategui, Miriam Klemann, Mateus David Finco
    Abstract:

    This paper presents a Mining Tool that is able to extract graphs from Texts, and proposes their use in helping students to write summaries. The Text summarization method is based on the use of the graphs as graphic organizers, leading students to further reflect about the main ideas of the Text before getting to the actual task of writing. An experiment carried out demonstrated that the Tool helped students reflect about the main ideas of the Text and supported the writing of the summaries.

  • ICALT - Using a Text Mining Tool to Support Text Summarization
    2012 IEEE 12th International Conference on Advanced Learning Technologies, 2012
    Co-Authors: Eliseo Berni Reategui, Miriam Klemann, Mateus David Finco
    Abstract:

    This paper presents a Mining Tool that is able to extract graphs from Texts, and proposes their use in helping students to write summaries. The Text summarization method is based on the use of the graphs as graphic organizers, leading students to further reflect about the main ideas of the Text before getting to the actual task of writing. An experiment carried out demonstrated that the Tool helped students reflect about the main ideas of the Text and supported the writing of the summaries.

Yael Garten - One of the best experts on this subject based on the ideXlab platform.

  • Using ODIN for a PharmGKB revalidation experiment
    Database : the journal of biological databases and curation, 2012
    Co-Authors: Fabio Rinaldi, Simon Clematide, Yael Garten, Michelle Whirl-carrillo, Li Gong, Joan M. Hebert, Katrin Sangkuhl, Caroline F. Thorn, Teri E. Klein, Russ B. Altman
    Abstract:

    The need for efficient Text-Mining Tools that support curation of the biomedical literature is ever increasing. In this article, we describe an experiment aimed at verifying whether a Text-Mining Tool capable of extracting meaningful relationships among domain entities can be successfully integrated into the curation workflow of a major biological database. We evaluate in particular (i) the usability of the system's interface, as perceived by users, and (ii) the correlation of the ranking of interactions, as provided by the Text-Mining system, with the choices of the curators.

  • Pharmspresso: a Text Mining Tool for extraction of pharmacogenomic concepts and relationships from full Text.
    BMC Bioinformatics, 2009
    Co-Authors: Yael Garten, Russ B. Altman
    Abstract:

    Background Pharmacogenomics studies the relationship between genetic variation and the variation in drug response phenotypes. The field is rapidly gaining importance: it promises drugs targeted to particular subpopulations based on genetic background. The pharmacogenomics literature has expanded rapidly, but is dispersed in many journals. It is challenging, therefore, to identify important associations between drugs and molecular entities – particularly genes and gene variants, and thus these critical connections are often lost. Text Mining techniques can allow us to convert the free-style Text to a computable, searchable format in which pharmacogenomic concepts (such as genes, drugs, polymorphisms, and diseases) are identified, and important links between these concepts are recorded. Availability of full Text articles as input into Text Mining engines is key, as literature abstracts often do not contain sufficient information to identify these pharmacogenomic associations.

  • Pharmspresso: a Text Mining Tool for extraction of pharmacogenomic concepts and relationships from full Text
    BMC Bioinformatics, 2009
    Co-Authors: Yael Garten, Russ B. Altman
    Abstract:

    Abstract Background Pharmacogenomics studies the relationship between genetic variation and the variation in drug response phenotypes. The field is rapidly gaining importance: it promises drugs targeted to particular subpopulations based on genetic background. The pharmacogenomics literature has expanded rapidly, but is dispersed in many journals. It is challenging, therefore, to identify important associations between drugs and molecular entities – particularly genes and gene variants, and thus these critical connections are often lost. Text Mining techniques can allow us to convert the free-style Text to a computable, searchable format in which pharmacogenomic concepts (such as genes, drugs, polymorphisms, and diseases) are identified, and important links between these concepts are recorded. Availability of full Text articles as input into Text Mining engines is key, as literature abstracts often do not contain sufficient information to identify these pharmacogenomic associations. Results Thus, building on a Tool called Textpresso, we have created the Pharmspresso Tool to assist in identifying important pharmacogenomic facts in full Text articles. Pharmspresso parses Text to find references to human genes, polymorphisms, drugs and diseases and their relationships. It presents these as a series of marked-up Text fragments, in which key concepts are visually highlighted. To evaluate Pharmspresso, we used a gold standard of 45 human-curated articles. Pharmspresso identified 78%, 61%, and 74% of target gene, polymorphism, and drug concepts, respectively. Conclusion Pharmspresso is a Text analysis Tool that extracts pharmacogenomic concepts from the literature automatically and thus captures our current understanding of gene-drug interactions in a computable form. We have made Pharmspresso available at http://pharmspresso.stanford.edu.

Eliseo Berni Reategui - One of the best experts on this subject based on the ideXlab platform.

  • uma ferramenta de mineracao de Texto para apoio a leitura e escrita autoral a Text Mining Tool to support reading and authorial writing
    2016
    Co-Authors: Alexandra Lorandi Macedo, Francieli Gracioli, Eliseo Berni Reategui, Patricia Alejandra Behar, Vinicius Hartmann Ferreira
    Abstract:

    This article presents a study on the potential of a Text Mining Tool to assist students in reading and writing. In the literate world, reading and writing are directly linked to learning processes, favoring the development of critical and argumentative thinking. Statistics show that in developing countries, such as Brazil, students show great difficulty writing or interpreting Texts. In order to minimize these difficulties, this study presents a proposal that combines teaching practices and technology to contribute to the processes of reading and writing. The article presents a study in which 22 students participated in reading and writing activities, in a 30 hours course. The results of the study, based on a qualitative perspective, allowed us to conclude that the Tools and strategies proposed contributed to the development of an authorial presence in their writing.

  • using a Text Mining Tool to support Text summarization
    International Conference on Advanced Learning Technologies, 2012
    Co-Authors: Eliseo Berni Reategui, Miriam Klemann, Mateus David Finco
    Abstract:

    This paper presents a Mining Tool that is able to extract graphs from Texts, and proposes their use in helping students to write summaries. The Text summarization method is based on the use of the graphs as graphic organizers, leading students to further reflect about the main ideas of the Text before getting to the actual task of writing. An experiment carried out demonstrated that the Tool helped students reflect about the main ideas of the Text and supported the writing of the summaries.

  • ICALT - Using a Text Mining Tool to Support Text Summarization
    2012 IEEE 12th International Conference on Advanced Learning Technologies, 2012
    Co-Authors: Eliseo Berni Reategui, Miriam Klemann, Mateus David Finco
    Abstract:

    This paper presents a Mining Tool that is able to extract graphs from Texts, and proposes their use in helping students to write summaries. The Text summarization method is based on the use of the graphs as graphic organizers, leading students to further reflect about the main ideas of the Text before getting to the actual task of writing. An experiment carried out demonstrated that the Tool helped students reflect about the main ideas of the Text and supported the writing of the summaries.

José Antonio Jiménez-quintero - One of the best experts on this subject based on the ideXlab platform.

  • Text Mining social media for competitive analysis
    Tourism & Management Studies, 2015
    Co-Authors: German Gemar, José Antonio Jiménez-quintero
    Abstract:

    Social media are utilised widely. Companies increasingly use social media to communicate and interact with customers. Much information is thereby generated and is available to everybody, including competitors. Firms need to analyse what their customers say and interact with them. Using Text Mining Tools, companies can know where they are in relation to their competitors and control the behaviour of these. Transforming Text into data and data into knowledge can be vital to make the right decisions and improving the competitive strategy of companies. This study used a Text Mining Tool to analyse the primary social media sites, including Twitter, Facebook, LinkedIn, YouTube and others, with a focus on a sample of hotels. The dimensions analysed were sentiments, passion and reach. A dependence was found between several variables obtained through Text Mining and financial performance. The results indicate that analysis of social media using these techniques can be a method to improve financial performance.

  • Text Mining social media for competitive analysis
    2014
    Co-Authors: German Gemar, José Antonio Jiménez-quintero
    Abstract:

    Social media are utilised widely. Companies increasingly use social media to communicate and interact with customers. Much information is thereby generated and is available to everybody, including competitors. Firms need to analyse what their customers say and interact with them. Using Text Mining Tools, companies can know where they are in relation to their competitors and control the behaviour of these. Transforming Text into data and data into knowledge can be vital to making the right decisions and improving the competitive strategy of companies. This study used a Text Mining Tool to analyse the primary social media sites, including Twitter, Facebook, LinkedIn, YouTube and others, with a focus on a sample of hotels. The dimensions analysed were sentiments, passion and reach. A dependence was found between several variables obtained through Text Mining and financial performance. The results indicate that analysis of social media using these techniques can be a method to improve financial performance.