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|Superheat form mining bitcoins||A professional data warehouse DWH established in hosts all of the residents' data. Spark Text can be extended to other areas of biomedical research. Combining and expanding these apple cup 2021 betting odds well-developed areas superheat form mining bitcoins research, we applied the text mining to structural modeling of protein-protein complexes protein docking. Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise. The precision of the classifier was calculated doing the comparison among the assigned topics manually and automated obtaining The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important.|
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It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community.
Within the last decade text mining , i. The automated textual analysis of law corpora is highly valuable because of its impact on a company's legal options and the raw amount of available jurisdiction. The study of supreme court jurisdiction and international law corpora is equally important due to its effects on business sectors. In this paper we use text mining methods to investigate Au Frontiers of biomedical text mining : current progress.
It is now almost 15 years since the publication of the first paper on text mining in the genomics domain, and decades since the first paper on text mining in the medical domain. Enormous progress has been made in the areas of information retrieval, evaluation methodologies and resource construction. Some problems, such as abbreviation-handling, can essentially be considered solved problems, and others, such as identification of gene mentions in text , seem likely to be solved soon.
However, a number of problems at the frontiers of biomedical text mining continue to present interesting challenges and opportunities for great improvements and interesting research. Most of the research in this direction has used the numbers quantitative information i. In this study we propose a text mining approach for detecting financial statement frau Text mining resources for the life sciences. Text mining is a powerful technology for quickly distilling key information from vast quantities of biomedical literature.
However, to harness this power the researcher must be well versed in the availability, suitability, adaptability, interoperability and comparative accuracy of current text mining resources. In this survey, we give an overview of the text mining resources that exist in the life sciences to help researchers, especially those employed in biocuration, to engage with text mining in their own work. We categorize the various resources under three sections: Content Discovery looks at where and how to find biomedical publications for text mining ; Knowledge Encoding describes the formats used to represent the different levels of information associated with content that enable text mining , including those formats used to carry such information between processes; Tools and Services gives an overview of workflow management systems that can be used to rapidly configure and compare domain- and task-specific processes, via access to a wide range of pre-built tools.
We also provide links to relevant repositories in each section to enable the reader to find resources relevant to their own area of interest. Throughout this work we give a special focus to resources that are interoperable-those that have the crucial ability to share information, enabling smooth integration and reusability.
Published by Oxford University Press. Chapter text mining for translational bioinformatics. Text mining for translational bioinformatics is a new field with tremendous research potential. It is a subfield of biomedical natural language processing that concerns itself directly with the problem of relating basic biomedical research to clinical practice, and vice versa.
Applications of text mining fall both into the category of T1 translational research-translating basic science results into new interventions-and T2 translational research, or translational research for public health. Potential use cases include better phenotyping of research subjects, and pharmacogenomic research. A variety of methods for evaluating text mining applications exist, including corpora, structured test suites, and post hoc judging. Two basic principles of linguistic structure are relevant for building text mining applications.
One is that linguistic structure consists of multiple levels. The other is that every level of linguistic structure is characterized by ambiguity. There are two basic approaches to text mining : rule-based, also known as knowledge-based; and machine-learning-based, also known as statistical. Many systems are hybrids of the two approaches.
Shared tasks have had a strong effect on the direction of the field. Like all translational bioinformatics software, text mining software for translational bioinformatics can be considered health-critical and should be subject to the strictest standards of quality assurance and software testing. Throughout this work we give a special focus to resources that are interoperable—those that have the crucial ability to share information, enabling smooth integration and reusability.
Hirschman, Lynette; Burns, Gully A. Text mining patents for biomedical knowledge. Biomedical text mining of scientific knowledge bases, such as Medline, has received much attention in recent years. Given that text mining is able to automatically extract biomedical facts that revolve around entities such as genes, proteins, and drugs, from unstructured text sources, it is seen as a major enabler to foster biomedical research and drug discovery.
In contrast to the biomedical literature, research into the mining of biomedical patents has not reached the same level of maturity. Here, we review existing work and highlight the associated technical challenges that emerge from automatically extracting facts from patents. We conclude by outlining potential future directions in this domain that could help drive biomedical research and drug discovery.
All rights reserved. Text mining with R a tidy approach. Much of the data available today is unstructured and text -heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you'll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr.
You'll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You'll also learn how to integrate natural language processing NLP into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media.
Learn how to apply the tidy text format to NLP Use sentiment analysis to mine the emotional content of text Identify a document's most important terms with frequency measurements E Benchmarking infrastructure for mutation text mining. Experimental research on the automatic extraction of information about mutations from texts is greatly hindered by the lack of consensus evaluation infrastructure for the testing and benchmarking of mutation text mining systems.
We propose a community-oriented annotation and benchmarking infrastructure to support development, testing, benchmarking, and comparison of mutation text mining systems. The design is based on semantic standards, where RDF is used to represent annotations, an OWL ontology provides an extensible schema for the data and SPARQL is used to compute various performance metrics, so that in many cases no programming is needed to analyze results from a text mining system.
While large benchmark corpora for biological entity and relation extraction are focused mostly on genes, proteins, diseases, and species, our benchmarking infrastructure fills the gap for mutation information. The core infrastructure comprises 1 an ontology for modelling annotations, 2 SPARQL queries for computing performance metrics, and 3 a sizeable collection of manually curated documents, that can support mutation grounding and mutation impact extraction experiments.
We have developed the principal infrastructure for the benchmarking of mutation text mining tasks. The infrastructure is suitable for out-of-the-box use in several important scenarios and is ready, in its current state, for initial community adoption. Background Experimental research on the automatic extraction of information about mutations from texts is greatly hindered by the lack of consensus evaluation infrastructure for the testing and benchmarking of mutation text mining systems.
Results We propose a community-oriented annotation and benchmarking infrastructure to support development, testing, benchmarking, and comparison of mutation text mining systems. Conclusion We have developed the principal infrastructure for the benchmarking of mutation text mining tasks.
This thesis is about Text Mining. Extracting important information from literature. In the last years, the number of biomedical articles and journals is growing exponentially. Scientists might not find the information they want because of the large number of publications.
Therefore a system was. Monitoring interaction and collective text production through text mining. Full Text Available This article presents the Concepts Network tool, developed using text mining technology. The main objective of this tool is to extract and relate terms of greatest incidence from a text and exhibit the results in the form of a graph.
The Network was implemented in the Collective Text Editor CTE which is an online tool that allows the production of texts in synchronized or non-synchronized forms. This article describes the application of the Network both in texts produced collectively and texts produced in a forum. The purpose of the tool is to offer support to the teacher in managing the high volume of data generated in the process of interaction amongst students and in the construction of the text. The results suggest that the Concepts Network can aid the teacher, as it provides indicators of the quality of the text produced.
Moreover, messages posted in forums can be analyzed without their content necessarily having to be pre-read. Using ontology network structure in text mining. Statistical text mining treats documents as bags of words, with a focus on term frequencies within documents and across document collections. Unlike natural language processing NLP techniques that rely on an engineered vocabulary or a full-featured ontology, statistical approaches do not make use of domain-specific knowledge.
The freedom from biases can be an advantage, but at the cost of ignoring potentially valuable knowledge. The approach proposed here investigates a hybrid strategy based on computing graph measures of term importance over an entire ontology and injecting the measures into the statistical text mining process.
As a starting point, we adapt existing search engine algorithms such as PageRank and HITS to determine term importance within an ontology graph. The graph-theoretic approach is evaluated using a smoking data set from the i2b2 National Center for Biomedical Computing, cast as a simple binary classification task for categorizing smoking-related documents, demonstrating consistent improvements in accuracy. Methods for Mining and Summarizing Text Conversations. Due to the Internet Revolution, human conversational data -- in written forms -- are accumulating at a phenomenal rate.
At the same time, improvements in speech technology enable many spoken conversations to be transcribed. Individuals and organizations engage in email exchanges, face-to-face meetings, blogging, texting and other social media activities. The advances in natural language processing provide ample opportunities for these "informal documents" to be analyzed and mined , thus creating numerous new and valuable applications. This book presents a set of computational methods. Identifying child abuse through text mining and machine learning.
In this paper, we describe how we used text mining and analysis to identify and predict cases of child abuse in a public health institution. Such institutions in the Netherlands try to identify and prevent different kinds of abuse. A significant part of the medical data that the institutions have on.
Mining knowledge from text repositories using information extraction Information extraction IE ; text mining ; text repositories; knowledge discovery from Text Mining the History of Medicine. Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts.
However, it can be difficult for researchers to explore and search such large volumes of data in an efficient manner. Text mining TM methods can help, through their ability to recognise various types of semantic information automatically, e.
TM analysis allows search systems to incorporate functionality such as automatic suggestions of synonyms of user-entered query terms, exploration of different concepts mentioned within search results or isolation of documents in which concepts are related in specific ways. However, applying TM methods to historical text can be challenging, according to differences and evolutions in vocabulary, terminology, language structure and style, compared to more modern text.
In this article, we present our efforts to overcome the various challenges faced in the semantic analysis of published historical medical text dating back to the mid 19th century. Firstly, we used evidence from diverse historical medical documents from different periods to develop new resources that provide accounts of the multiple, evolving ways in which concepts, their variants and relationships amongst them may be expressed. These resources were employed to support the development of a modular processing pipeline of TM tools for the robust detection of semantic information in historical medical documents with varying characteristics.
We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system. The novel resources are available for research purposes, while. We have developed a simple text mining algorithm that allows us to identify surface area and pore volumes of metal-organic frameworks MOFs using manuscript html files as inputs. The algorithm searches for common units e. Further application to a test set of randomly chosen MOF html files yielded Most of the errors stem from unorthodox sentence structures that made it difficult to identify the correct data as well as bolded notations of MOFs e.
These types of tools will become useful when it comes to discovering structure-property relationships among MOFs as well as collecting a large set of data for references. With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published.
Text mining techniques enable the extraction of unknown knowledge from unstructured documents. This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail.
Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise. Mining highly stressed areas, part 2. Full Text Available A questionnaire related to mining at great depth and in very high stress conditions has been completed with the assistance of mine rock mechanics personnel on over twenty mines in all mining districts, and covering all deep level mines Text mining in livestock animal science: introducing the potential of text mining to animal sciences.
In biological research, establishing the prior art by searching and collecting information already present in the domain has equal importance as the experiments done. To obtain a complete overview about the relevant knowledge, researchers mainly rely on 2 major information sources: i various biological databases and ii scientific publications in the field.
The major difference between the 2 information sources is that information from databases is available, typically well structured and condensed. The information content in scientific literature is vastly unstructured; that is, dispersed among the many different sections of scientific text.
The traditional method of information extraction from scientific literature occurs by generating a list of relevant publications in the field of interest and manually scanning these texts for relevant information, which is very time consuming. It is more than likely that in using this "classical" approach the researcher misses some relevant information mentioned in the literature or has to go through biological databases to extract further information.
Text mining and named entity recognition methods have already been used in human genomics and related fields as a solution to this problem. These methods can process and extract information from large volumes of scientific text. Text mining is defined as the automatic extraction of previously unknown and potentially useful information from text.
In animal sciences, text mining and related methods have been briefly used in murine genomics and associated fields, leaving behind other fields of animal sciences, such as livestock genomics. The aim of this work was to develop an information retrieval platform in the livestock domain focusing on livestock publications and the recognition of relevant data from.
The customers, with their preferences, determine the success or failure of a company. In order to know opinions of the customers we can use technologies available from the web 2. From these web sites, useful information must be extracted, for strategic purposes, using techniques of sentiment analysis or opinion mining.
Text mining meets workflow: linking U-Compare with Taverna. Summary: Text mining from the biomedical literature is of increasing importance, yet it is not easy for the bioinformatics community to create and run text mining workflows due to the lack of accessibility and interoperability of the text mining resources. The U-Compare system provides a wide range of bio text mining resources in a highly interoperable workflow environment where workflows can very easily be created, executed, evaluated and visualized without coding.
We have linked U-Compare to Taverna, a generic workflow system, to expose text mining functionality to the bioinformatics community. Path Text : a text mining integrator for biological pathway visualizations. Motivation: Metabolic and signaling pathways are an increasingly important part of organizing knowledge in systems biology. They serve to integrate collective interpretations of facts scattered throughout literature. Biologists construct a pathway by reading a large number of articles and interpreting them as a consistent network, but most of the models constructed currently lack direct links to those articles.
Biologists who want to check the original articles have to spend substantial amounts of time to collect relevant articles and identify the sections relevant to the pathway. Furthermore, with the scientific literature expanding by several thousand papers per week, keeping a model relevant requires a continuous curation effort. In this article, we present a system designed to integrate a pathway visualizer, text mining systems and annotation tools into a seamless environment.
This will enable biologists to freely move between parts of a pathway and relevant sections of articles, as well as identify relevant papers from large text bases. Contact: brian monrovian. Biomedical text mining and its applications in cancer research. Cancer is a malignant disease that has caused millions of human deaths. Its study has a long history of well over years.
There have been an enormous number of publications on cancer research. This integrated but unstructured biomedical text is of great value for cancer diagnostics, treatment, and prevention. The immense body and rapid growth of biomedical text on cancer has led to the appearance of a large number of text mining techniques aimed at extracting novel knowledge from scientific text.
Biomedical text mining on cancer research is computationally automatic and high-throughput in nature. However, it is error-prone due to the complexity of natural language processing. In this review, we introduce the basic concepts underlying text mining and examine some frequently used algorithms, tools, and data sets, as well as assessing how much these algorithms have been utilized.
We then discuss the current state-of-the-art text mining applications in cancer research and we also provide some resources for cancer text mining. With the development of systems biology, researchers tend to understand complex biomedical systems from a systems biology viewpoint. Thus, the full utilization of text mining to facilitate cancer systems biology research is fast becoming a major concern.
To address this issue, we describe the general workflow of text mining in cancer systems biology and each phase of the workflow. We hope that this review can i provide a useful overview of the current work of this field; ii help researchers to choose text mining tools and datasets; and iii highlight how to apply text mining to assist cancer systems biology research.
Cultural text mining : using text mining to map the emergence of transnational reference cultures in public media repositories. This paper discusses the research project Translantis, which uses innovative technologies for cultural text mining to analyze large repositories of digitized public media, such as newspapers and journals.
Env Mine : A text-mining system for the automatic extraction of contextual information. Full Text Available Abstract Background For ecological studies, it is crucial to count on adequate descriptions of the environments and samples being studied. Such a description must be done in terms of their physicochemical characteristics, allowing a direct comparison between different environments that would be difficult to do otherwise. Also the characterization must include the precise geographical location, to make possible the study of geographical distributions and biogeographical patterns.
Currently, there is no schema for annotating these environmental features, and these data have to be extracted from textual sources published articles. So far, this had to be performed by manual inspection of the corresponding documents. To facilitate this task, we have developed Env Mine , a set of text-mining tools devoted to retrieve contextual information physicochemical variables and geographical locations from textual sources of any kind. Results Env Mine is capable of retrieving the physicochemical variables cited in the text , by means of the accurate identification of their associated units of measurement.
Also a Bayesian classifier was tested for distinguishing parts of the text describing environmental characteristics from others dealing with, for instance, experimental settings. The identification of a location includes also the determination of its exact coordinates latitude and longitude, thus allowing the calculation of distance between the individual locations.
Conclusion Env Mine is a very efficient method for extracting contextual information from different text sources, like published articles or web pages. This tool can help in determining the precise location and physicochemical. Text mining of web-based medical content. Text Mining of Web-Based Medical Content examines web mining for extracting useful information that can be used for treating and monitoring the healthcare of patients.
This work provides methodological approaches to designing mapping tools that exploit data found in social media postings. Specific linguistic features of medical postings are analyzed vis-a-vis available data extraction tools for culling useful information. At present, social media and networks act as one of the main platforms for sharing information, idea, thought and opinions.
Many people share their knowledge and express their views on the specific topics or current hot issues that interest them. The social media texts have rich information about the complaints, comments, recommendation and suggestion as the automatic reaction or respond to government initiative or policy in order to overcome certain issues.
This study examines the sentiment from netizensas part of citizen who has vocal sound about the implementation of UU ITE as the first cyberlaw in Indonesia as a means to identify the current tendency of citizen perception. To perform text mining techniques, this study used Twitter Rest API while R programming was utilized for the purpose of classification analysis based on hierarchical cluster. Text mining for biology--the way forward. This article collects opinions from leading scientists about how text mining can provide better access to the biological literature, how the scientific community can help with this process, what the next steps are, and what role future BioCreative evaluations can play.
The responses identify Text mining in cancer gene and pathway prioritization. Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed.
A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on the analysis of their text mining components. We explain the relatively slow progress of text mining in gene prioritization, identify several challenges to current text mining methods, and highlight a few directions where more effective text mining algorithms may improve the overall prioritization task and where prioritizing the pathways may be more desirable than prioritizing only genes.
Data mining of text as a tool in authorship attribution. It is common that text documents are characterized and classified by keywords that the authors use to give them. Visa et al. The prototype is an interesting document or a part of an extracted, interesting text. This prototype is matched with the document database of the monitored document flow. The new methodology is capable of extracting the meaning of the document in a certain degree. Our claim is that the new methodology is also capable of authenticating the authorship.
To verify this claim two tests were designed. The test hypothesis was that the words and the word order in the sentences could authenticate the author. In the first test three authors were selected. Three texts from each author were examined. Every text was one by one used as a prototype. The two nearest matches with the prototype were noted. The second test uses the Reuters financial news database. A group of 25 short financial news reports from five different authors are examined.
Our new methodology and the interesting results from the two tests are reported in this paper. In the first test, for Shakespeare and for Poe all cases were successful. For Shaw one text was confused with Poe. In the second test the Reuters financial news were identified by the author relatively well. The resolution is that our text mining methodology seems to be capable of authorship attribution. Application of text mining in the biomedical domain.
In recent years the amount of experimental data that is produced in biomedical research and the number of papers that are being published in this field have grown rapidly. In order to keep up to date with developments in their field of interest and to interpret the outcome of experiments in light of all available literature, researchers turn more and more to the use of automated literature mining. As a consequence, text mining tools have evolved considerably in number and quality and nowadays can be used to address a variety of research questions ranging from de novo drug target discovery to enhanced biological interpretation of the results from high throughput experiments.
In this paper we introduce the most important techniques that are used for a text mining and give an overview of the text mining tools that are currently being used and the type of problems they are typically applied for. Application of text mining for customer evaluations in commercial banking. Nowadays customer attrition is increasingly serious in commercial banks. To combat this problem roundly, mining customer evaluation texts is as important as mining customer structured data.
In order to extract hidden information from customer evaluations, Textual Feature Selection, Classification and Association Rule Mining are necessary techniques. This paper presents all three techniques by using Chinese Word Segmentation, C5. Results, consequent solutions, some advice for the commercial bank are given in this paper. Text mining for traditional Chinese medical knowledge discovery: a survey.
Extracting meaningful information and knowledge from free text is the subject of considerable research interest in the machine learning and data mining fields. Text data mining or text mining has become one of the most active research sub-fields in data mining. Significant developments in the area of biomedical text mining during the past years have demonstrated its great promise for supporting scientists in developing novel hypotheses and new knowledge from the biomedical literature.
Traditional Chinese medicine TCM provides a distinct methodology with which to view human life. It is one of the most complete and distinguished traditional medicines with a history of several thousand years of studying and practicing the diagnosis and treatment of human disease. It has been shown that the TCM knowledge obtained from clinical practice has become a significant complementary source of information for modern biomedical sciences. TCM literature obtained from the historical period and from modern clinical studies has recently been transformed into digital data in the form of relational databases or text documents, which provide an effective platform for information sharing and retrieval.
This motivates and facilitates research and development into knowledge discovery approaches and to modernize TCM. In order to contribute to this still growing field, this paper presents 1 a comparative introduction to TCM and modern biomedicine, 2 a survey of the related information sources of TCM, 3 a review and discussion of the state of the art and the development of text mining techniques with applications to TCM, 4 a discussion of the research issues around TCM text mining and its future directions.
Copyright Elsevier Inc. The potential of the system goes beyond text retrieval. It may also be used to compare entities of the same type such as pairs of drugs or pairs of procedures et OntoGene web services for biomedical text mining. Text mining services are rapidly becoming a crucial component of various knowledge management pipelines, for example in the process of database curation, or for exploration and enrichment of biomedical data within the pharmaceutical industry.
Traditional architectures, based on monolithic applications, do not offer sufficient flexibility for a wide range of use case scenarios, and therefore open architectures, as provided by web services, are attracting increased interest.
We present an approach towards providing advanced text mining capabilities through web services, using a recently proposed standard for textual data interchange BioC. The web services leverage a state-of-the-art platform for text mining OntoGene which has been tested in several community-organized evaluation challenges,with top ranked results in several of them.
Text mining in the classification of digital documents. Full Text Available Objective: Develop an automated classifier for the classification of bibliographic material by means of the text mining. Methodology: The text mining is used for the development of the classifier, based on a method of type supervised, conformed by two phases; learning and recognition, in the learning phase, the classifier learns patterns across the analysis of bibliographical records, of the classification Z, belonging to library science, information sciences and information resources, recovered from the database LIBRUNAM, in this phase is obtained the classifier capable of recognizing different subclasses LC.
In the recognition phase the classifier is validated and evaluates across classification tests, for this end bibliographical records of the classification Z are taken randomly, classified by a cataloguer and processed by the automated classifier, in order to obtain the precision of the automated classifier. Results: The application of the text mining achieved the development of the automated classifier, through the method classifying documents supervised type. The precision of the classifier was calculated doing the comparison among the assigned topics manually and automated obtaining Conclusions: The application of text mining facilitated the creation of automated classifier, allowing to obtain useful technology for the classification of bibliographical material with the aim of improving and speed up the process of organizing digital documents.
This article presents 34 characteristics of texts and tasks " text features" that can make continuous prose , noncontinuous document , and quantitative texts easier or more difficult for adolescents and adults to comprehend and use. The text features were identified by examining the assessment tasks and associated texts in the national…. Full Text Available Research on publication trends in journal articles on sleep disorders SDs and the associated methodologies by using text mining has been limited.
The present study involved text mining for terms to determine the publication trends in sleep-related journal articles published during and to identify associations between SD and methodology terms as well as conducting statistical analyses of the text mining findings. SD and methodology terms were extracted from 3, sleep-related journal articles in the PubMed database by using MetaMap. The extracted data set was analyzed using hierarchical cluster analyses and adjusted logistic regression models to investigate publication trends and associations between SD and methodology terms.
MetaMap had a text mining precision, recall, and false positive rate of 0. The most common SD term was breathing-related sleep disorder, whereas narcolepsy was the least common. Cluster analyses showed similar methodology clusters for each SD term, except narcolepsy. The logistic regression models showed an increasing prevalence of insomnia, parasomnia, and other sleep disorders but a decreasing prevalence of breathing-related sleep disorder during Different SD terms were positively associated with different methodology terms regarding research design terms, measure terms, and analysis terms.
Insomnia-, parasomnia-, and other sleep disorder-related articles showed an increasing publication trend, whereas those related to breathing-related sleep disorder showed a decreasing trend. Furthermore, experimental studies more commonly focused on hypersomnia and other SDs and less commonly on insomnia, breathing-related sleep disorder, narcolepsy, and parasomnia.
Thus, text mining may facilitate the exploration of the publication trends in SDs and the associated methodologies. Facilitating class discussions effectively is a critical yet challenging component of instruction, particularly in online environments where student and faculty interaction is limited. Our goals in this research were to identify facilitation strategies that encourage productive discussion, and to explore text mining techniques that can help…. The aim of this paper is to present a methodological concept in business research that has the potential to become one of the most powerful methods in the upcoming years when it comes to research qualitative phenomena in business and society.
It presents a selection of algorithms as well elaborat Kostoff, Ronald N. Antonio; Humenik, James A. Discusses the importance of identifying the users and impact of research, and describes an approach for identifying the pathways through which research can impact other research, technology development, and applications. Describes a study that used citation mining , an integration of citation bibliometrics and text mining , on articles from the….
Research on publication trends in journal articles on sleep disorders SDs and the associated methodologies by using text mining has been limited. Text mining improves prediction of protein functional sites. The structure analysis was carried out using Dynamics Perturbation Analysis DPA, which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important.
We assessed the significance of each of these methods by analyzing their performance in finding known functional sites specifically, small-molecule binding sites and catalytic sites in about , publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. The text -based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site.
The overlap of predictions with annotations improved when the text -based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions.
Mining biological networks from full- text articles. The study of biological networks is playing an increasingly important role in the life sciences. Many different kinds of biological system can be modelled as networks; perhaps the most important examples are protein-protein interaction PPI networks, metabolic pathways, gene regulatory networks, and signalling networks. Although much useful information is easily accessible in publicly databases, a lot of extra relevant data lies scattered in numerous published papers.
Hence there is a pressing need for automated text-mining methods capable of extracting such information from full- text articles. Here we present practical guidelines for constructing a text-mining pipeline from existing code and software components capable of extracting PPI networks from full- text articles. This approach can be adapted to tackle other types of biological network. Mining highly stressed areas, part 1. Full Text Available The aim of this long-term project has been to focus on the extreme high-stress end of the mining spectrum.
Such high stress conditions will prevail in certain ultra-deep mining operation of the near future, and are already being experienced The structure analysis was carried out using Dynamics Perturbation Analysis DPA , which predicts functional sites at control points where interactions greatly perturb protein vibrations. Sep 29, This is the most applied task. Empirical advances with text mining of electronic health records.
Korian is a private group specializing in medical accommodations for elderly and dependent people. A professional data warehouse DWH established in hosts all of the residents' data. Inside this information system IS , clinical narratives CNs were used only by medical staff as a residents' care linking tool. The objective of this study was to show that, through qualitative and quantitative textual analysis of a relatively small physiotherapy and well-defined CN sample, it was possible to build a physiotherapy corpus and, through this process, generate a new body of knowledge by adding relevant information to describe the residents' care and lives.
Another step involved principal components and multiple correspondence analyses, plus clustering on the same residents' sample as well as on other health data using a health model measuring the residents' care level needs. By combining these techniques, physiotherapy treatments could be characterized by a list of constructed keywords, and the residents' health characteristics were built. Feeding defects or health outlier groups could be detected, physiotherapy residents' data and their health data were matched, and differences in health situations showed qualitative and quantitative differences in physiotherapy narratives.
This textual experiment using a textual process in two stages showed that text mining and data mining techniques provide convenient tools to improve residents' health and quality of care by adding new, simple, useable data to the electronic health record EHR. When used with a normalized physiotherapy problem list, text mining through information extraction IE , named entity recognition NER and data mining DM can provide a real advantage to describe health care, adding new medical material and.
Imitating manual curation of text-mined facts in biomedicine. Full Text Available Text-mining algorithms make mistakes in extracting facts from natural-language texts. In biomedical applications, which rely on use of text-mined data, it is critical to assess the quality the probability that the message is correctly extracted of individual facts--to resolve data conflicts and inconsistencies.
Using a large set of almost , manually produced evaluations most facts were independently reviewed more than once, producing independent evaluations, we implemented and tested a collection of algorithms that mimic human evaluation of facts provided by an automated information-extraction system.
The performance of our best automated classifiers closely approached that of our human evaluators ROC score close to 0. Our hypothesis is that, were we to use a larger number of human experts to evaluate any given sentence, we could implement an artificial-intelligence curator that would perform the classification job at least as accurately as an average individual human evaluator.
We illustrated our analysis by visualizing the predicted accuracy of the text-mined relations involving the term cocaine. In this chapter, we explain how text mining can support the curation of molecular biology databases dealing with protein functions. We also show how curated data can play a disruptive role in the developments of text mining methods. We review a decade of efforts to improve the automatic assignment of Gene Ontology GO descriptors, the reference ontology for the characterization of genes and gene products.
We argue that automatic text categorization functions can ultimately be embedded into a Question-Answering QA system to answer questions related to protein functions. Because GO descriptors can be relatively long and specific, traditional QA systems cannot answer such questions. A new type of QA system, so-called Deep QA which uses machine learning methods trained with curated contents, is thus emerging.
Finally, future advances of text mining instruments are directly dependent on the availability of high-quality annotated contents at every curation step. Databases workflows must start recording explicitly all the data they curate and ideally also some of the data they do not curate. Text mining and visualization case studies using open-source tools.
The contributors-all highly experienced with text mining and open-source software-explain how text data are gathered and processed from a wide variety of sources, including books, server access logs, websites, social media sites, and message boards. Each chapter presents a case study that you can follow as part of a step-by-step, reproducible example. You can also easily apply and extend the techniques to other problems.
All the examples are available on a supplementary website. The book shows you how to exploit your text data, offering successful application examples and blueprints for you to tackle your text mining tasks and benefit from open and freely available tools. It gets you up to date on the latest and most powerful tools, the data mining process, and specific text mining activities. According to the National Institutes of Health NIH , precision medicine is "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person.
Biomedical hypothesis generation by text mining and gene prioritization. Text mining methods can facilitate the generation of biomedical hypotheses by suggesting novel associations between diseases and genes. The proposed enhanced RaJoLink rare-term model combines text mining and gene prioritization approaches. Hot complaint intelligent classification based on text mining.
Full Text Available The complaint recognizer system plays an important role in making sure the correct classification of the hot complaint,improving the service quantity of telecommunications industry. The paper presents a model of complaint hot intelligent classification based on text mining ,which can classify the hot complaint in the correct level of the complaint navigation. The examples show that the model can be efficient to classify the text of the complaint.
Korean government provides classification services to exporters. It is simple to copy technology such as documents and drawings. Moreover, it is also easy that new technology derived from the existing technology.
The diversity of technology makes classification difficult because the boundary between strategic and nonstrategic technology is unclear and ambiguous. Reviewers should consider previous classification cases enough. However, the increase of the classification cases prevent consistent classifications.
This made another innovative and effective approaches necessary. IXCRS consists of and expert system, a semantic searching system, a full text retrieval system, and image retrieval system and a document retrieval system. It is the aim of the present paper to observe the document retrieval system based on text mining and to discuss how to utilize the system.
This study has demonstrated how text mining technique can be applied to export control. The document retrieval system supports reviewers to treat previous classification cases effectively. Especially, it is highly probable that similarity data will contribute to specify classification criterion. However, an analysis of the system showed a number of problems that remain to be explored such as a multilanguage problem and an inclusion relationship problem.
Further research should be directed to solve problems and to apply more data mining techniques so that the system should be used as one of useful tools for export control. Text mining a self-report back-translation. There are several recommendations about the routine to undertake when back translating self-report instruments in cross-cultural research.
However, text mining methods have been generally ignored within this field. This work describes a text mining innovative application useful to adapt a personality questionnaire to 12 different languages. The method is divided in 3 different stages, a descriptive analysis of the available back-translated instrument versions, a dissimilarity assessment between the source language instrument and the 12 back-translations, and an item assessment of item meaning equivalence.
The suggested method contributes to improve the back-translation process of self-report instruments for cross-cultural research in 2 significant intertwined ways. First, it defines a systematic approach to the back translation issue, allowing for a more orderly and informed evaluation concerning the equivalence of different versions of the same instrument in different languages.
In addition, this procedure can be extended to the back-translation of self-reports measuring psychological constructs in clinical assessment. Future research works could refine the suggested methodology and use additional available text mining tools. Systematic reviews SRs involve the identification, appraisal, and synthesis of all relevant studies for focused questions in a structured reproducible manner. Image Credit: thinkstockphotos.
Scientists at Harvard have presented a new method for studying superheated materials in the moments before, during, and after bubble nucleation. There are various ways to study superheating liquids and bubble nucleation. This method involves studying individual bubbles rather than a large number all at once. The scientists began by creating a single nanopore in a membrane and surrounding it in a sodium-chloride solution. A small voltage pulse passed across the membrane and rapidly heated the surrounding liquid.
A single bubble of vapor evenly formed in the center of the small pore of the membrane before collapsing approximately 16 nanoseconds ns later. Another bubble formed ns after that. A cross section of the experimental setup. The gap between the two plates is the nanopore where the bubbles are formed. Image Credit: Phys. When the bubble formed on the membrane, it temporarily blocked the flow of ions across the pore.
While measuring the electrical signal, a laser pointed at the center of the nanopore and scattered when a bubble formed. As scientists continue to study bubble nucleation, one of the most exciting future applications of knowing more about bubble nucleation are using bubbles as lenses.
In using bubbles, there's the potential to move or change a lens to rapidly refocus light. Learning more about bubble nucleation also has applications in chemistry, electronics, acoustics, and characterizing certain states of matter.
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