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Related Concept Videos

Relative Risk01:12

Relative Risk

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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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The understanding of the concept of reference frames is essential to discuss relative motion in one or more dimensions. When we say that an object has a certain velocity, we must state the velocity with respect to a given reference frame. In most examples, this reference frame has been Earth. For instance, if a statement reads that a person is sitting in a train moving at 10 m/s east, then it implies that the person on the train is moving relative to the surface of Earth at this velocity,...
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The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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Relative Velocity in Two Dimensions01:11

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Relative velocity is the velocity of an object as observed from a particular reference frame, or the velocity of one reference frame with respect to another reference frame. The concept of relative velocity can be used to describe motion in two dimensions. Consider a particle P and two reference frames S and S′. The position of the origin of S′ as measured in S is , the position of P as measured in S′ is , and the position of P as measured in S is , which can be evaluated by utilizing...
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Relative Frequency Distribution00:55

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A relative frequency distribution is the proportion or fraction of times a value occurs in a data set. To find the relative frequencies, one can divide each frequency by the total number of data points in the sample. It is very similar to a regular frequency distribution, except that instead of reporting how many data values fall in a class, a relative frequency distribution reports the fraction of data values that fall in a class. These fractions or proportions are called relative frequencies...
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The relative stability of alkenes can be determined by comparing their heats of hydrogenation. The lower heat of hydrogenation indicates the more stable alkene.  The three main factors determining the relative stability of alkenes are i) the number of substituents attached to the double-bond carbon atoms, ii) hyperconjugation, and iii) the stereochemistry of the double bond.
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Behavioral Assessment of Manual Dexterity in Non-Human Primates
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DEXTER: Disease-Expression Relation Extraction from Text.

Samir Gupta1, Hayley Dingerdissen2, Karen E Ross3

  • 1Department of Computer and Information Sciences, University of Delaware, 18 Amstel Avenue, Newark, DE 19716, USA.

Database : the Journal of Biological Databases and Curation
|June 4, 2018
PubMed
Summary
This summary is machine-generated.

We developed DEXTER, an automated tool to extract gene and microRNA expression data from scientific literature, enhancing disease research databases. This tool achieved high accuracy, improving the BioXpress database with crucial expression-disease relationship information.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression is crucial for biological processes and disease development.
  • Existing gene expression databases often rely on manual curation of literature, which is time-consuming and struggles to keep pace with publications.
  • Scientific literature contains extensive gene expression-disease relationship data from both large-scale and small-scale studies.

Purpose of the Study:

  • To develop an automated text-mining tool, Disease-Expression Relation Extraction from Text (DEXTER), for extracting gene and microRNA expression information in the context of disease from literature.
  • To extend the BioXpress database, particularly its literature-based component, with up-to-date expression-disease relationship data.
  • To demonstrate DEXTER's capability in extracting rich expression information across various disease scenarios.

Main Methods:

  • Developed DEXTER, an automated text-mining tool utilizing natural language processing techniques.
  • Integrated DEXTER's extracted information into the literature-based portion of the BioXpress database.
  • Conducted two evaluations to assess the accuracy of the DEXTER tool, achieving average F-scores of 88.51% and 81.81%.

Main Results:

  • DEXTER successfully extracted differential expression information for 2024 genes in lung cancer, 115 glycosyltransferases in 62 cancers, and 826 microRNAs in 171 cancers.
  • The tool demonstrated high accuracy in extracting gene and microRNA expression-disease relationships from literature.
  • The literature-based BioXpress database was significantly enhanced with newly extracted information.

Conclusions:

  • Automated text mining using DEXTER is an effective method to rapidly extract valuable gene and microRNA expression data from scientific literature.
  • DEXTER significantly improves the comprehensiveness and timeliness of disease-focused gene expression databases like BioXpress.
  • The tool's high accuracy and broad applicability facilitate deeper insights into gene expression-disease connections for clinical research and diagnostics.