Combination of text-mining algorithms increases the performance

Malik, Rainer; Franke, Lude; Siebes, Arno

doi:10.1093/bioinformatics/btl281

Cited by 19 publications

(10 citation statements)

References 25 publications

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“…Evaluation of the accuracy of integrated data is a necessary step and generally is expressed in terms of precision (the ability to identify the correct entities in the abstracts relative to the number of all identified entities of the same type), recall (the ability to identify correct entities of specific type present in the abstracts relative to all such entities present in the abstracts) and F-measure [41]. Since it is not possible to evaluate every concept from each of the dictionaries across all 5,243 documents, we selected the SCN1A gene as the reference.…”

Section: Discussionmentioning

confidence: 99%

DDESC: Dragon database for exploration of sodium channels in human

et al. 2008

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Background: Sodium channels are heteromultimeric, integral membrane proteins that belong to a superfamily of ion channels. The mutations in genes encoding for sodium channel proteins have been linked with several inherited genetic disorders such as febrile epilepsy, Brugada syndrome, ventricular fibrillation, long QT syndrome, or channelopathy associated insensitivity to pain. In spite of these significant effects that sodium channel proteins/genes could have on human health, there is no publicly available resource focused on sodium channels that would support exploration of the sodium channel related information.

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Section: Discussionmentioning

confidence: 99%

DDESC: Dragon database for exploration of sodium channels in human

et al. 2008

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“…As the combination of relation extraction methods has previously been shown to increase overall system performance [23,27], we decided to combine both co-occurrence and NLP methods to enhance the overall performance. For the text retrieval, we apply co-occurrence to obtain high sensitivity, while in the relation extraction phase, we apply NLP and rule-based methods to gain high specificity.…”

Section: Methodsmentioning

confidence: 99%

Extracting causal relations on HIV drug resistance from literature

et al. 2010

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BackgroundIn HIV treatment it is critical to have up-to-date resistance data of applicable drugs since HIV has a very high rate of mutation. These data are made available through scientific publications and must be extracted manually by experts in order to be used by virologists and medical doctors. Therefore there is an urgent need for a tool that partially automates this process and is able to retrieve relations between drugs and virus mutations from literature.ResultsIn this work we present a novel method to extract and combine relationships between HIV drugs and mutations in viral genomes. Our extraction method is based on natural language processing (NLP) which produces grammatical relations and applies a set of rules to these relations. We applied our method to a relevant set of PubMed abstracts and obtained 2,434 extracted relations with an estimated performance of 84% for F-score. We then combined the extracted relations using logistic regression to generate resistance values for each pair. The results of this relation combination show more than 85% agreement with the Stanford HIVDB for the ten most frequently occurring mutations. The system is used in 5 hospitals from the Virolab project http://www.virolab.org to preselect the most relevant novel resistance data from literature and present those to virologists and medical doctors for further evaluation.ConclusionsThe proposed relation extraction and combination method has a good performance on extracting HIV drug resistance data. It can be used in large-scale relation extraction experiments. The developed methods can also be applied to extract other type of relations such as gene-protein, gene-disease, and disease-mutation.

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“…Text-mining is a simple process of extracting knowledge from text. In this research, we need text-mining algorithms as part of our suggested solution at the level of user interest classification [19,20]. This classification is based on the text in the tweets of users.…”

Section: Classification Of Twitter Usersmentioning

confidence: 99%

Classification of Arabic Twitter Users: A Study Based on User Behaviour and Interests

Alabdullatif

Shahzad

Alwagait

2016

Mobile Information Systems

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Social networks are among the most popular interactive media today due to their simplicity and their ability to break down the barriers of community rules and their speed and because of the increasing pressures of work environments that make it more difficult for people to visit or call friends. There are many social networking products available and they are widely used for social interaction. As the amount of threading data is growing, producing analysis from this large volume of communications is becoming increasingly difficult for public and private organisations. One of the important applications of this work is to determine the trends in social networks that depend on identifying relationships between members of a community. This is not a trivial task as it has numerous challenges. Information shared between social members does not have a formal data structure but is transmitted in the form of texts, emoticons, and multimedia. The inspiration for addressing this area is that if a company is advertising a sports product, for example, it has a difficulty in identifying targeted samples of Arab people on social networks who are interested in sports. In order to accomplish this, an experiment oriented approach is adopted in this study. A goal for this company is to discover users who have been interacting with other users who have the same interests, so they can receive the same type of message or advertisement. This information will help a company to determine how to develop advertisements based on Arab people’s interests. Examples of such work include the timely advertisement of the utilities that can be effectively marketed to increase the audience; for example, on the weekend days, the effective market approaches can yield considerable results in terms of increasing the sales and profits. In addition, finding an efficient way to recommend friends to a user based on interest similarity, celebrity degree, and online behaviour is of interest to social networks themselves. This problem is explored to establish and apply an efficient and easy way to classify a social network of Arab users based on their interests using available types of information, whether textual or nontextual, and to try to increase the accuracy of interest classification. Since most of the social networking is done from the mobiles nowadays, the efficient and reliable algorithm can help in developing a robust app that can perform the tweet classification on mobile phones.

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Combination of text-mining algorithms increases the performance

Abstract: The LDD corpus presented is available by request to the author. The system will be available shortly. For information and updates on CONAN please visit http://www.cs.uu.nl/people/rainer/conan.html.

Cited by 19 publications

References 25 publications

DDESC: Dragon database for exploration of sodium channels in human

DDESC: Dragon database for exploration of sodium channels in human

Extracting causal relations on HIV drug resistance from literature

Classification of Arabic Twitter Users: A Study Based on User Behaviour and Interests

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