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Related Concept Videos

Multiple Bar Graph01:07

Multiple Bar Graph

As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
Bar Graph01:07

Bar Graph

A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
Additional Subnuclear Structures02:10

Additional Subnuclear Structures

The eukaryotic nucleus is a double membrane-bound organelle that contains nearly all of the cell’s genetic material in the form of chromosomes. It is rightly called the “brain” of the cell as it shoulders the responsibility of responding to various physiological processes, stress, altered metabolic conditions, and other cellular signals. 
The nucleus contains many membrane-less subnuclear organelles or nuclear bodies, such as nucleoli, Cajal bodies, speckles, paraspeckles, etc. These nuclear...
Additional Subnuclear Structures02:10

Additional Subnuclear Structures

The eukaryotic nucleus is a double membrane-bound organelle that contains nearly all of the cell’s genetic material in the form of chromosomes. It is rightly called the “brain” of the cell as it shoulders the responsibility of responding to various physiological processes, stress, altered metabolic conditions, and other cellular signals. 
The nucleus contains many membrane-less subnuclear organelles or nuclear bodies, such as nucleoli, Cajal bodies, speckles, paraspeckles, etc. These nuclear...
Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

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Related Experiment Video

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Live Cell Imaging of Early Autophagy Events: Omegasomes and Beyond
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Live Cell Imaging of Early Autophagy Events: Omegasomes and Beyond

Published on: July 27, 2013

A stacked graphical model for associating sub-images with sub-captions.

Zhenzhen Kou1, William W Cohen, Robert F Murphy

  • 1Machine Learning Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. zkou@andrew.cmu.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|November 10, 2007
PubMed
Summary

Researchers developed a stacked graphical model to improve data mining from scientific articles. This new method enhances the accuracy of matching text descriptions with corresponding images, aiding biological information extraction.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Scientific Literature Mining

Background:

  • Extracting comprehensive biological data from full-text scientific articles is challenging.
  • Current methods for integrating information from text and images in research papers have limitations.

Purpose of the Study:

  • To develop an advanced system for extracting biological information from journal articles by effectively linking text and images.
  • To improve the accuracy of matching sub-figures within articles to their corresponding textual descriptions.

Main Methods:

  • Introduction of a stacked graphical model, a meta-learning approach designed to enhance a base learner.
  • The model expands features using related instances to accurately match labels between sub-figures and sentences.
  • The system, named SLIF (Subcellular Location Image Finder), utilizes this model for text-image association.

Main Results:

  • The stacked graphical model achieved a matching accuracy of 81.3%.
  • This represents a significant improvement over the relational dependency network (70.8%) and the existing SLIF algorithm (64.3%).

Conclusions:

  • The stacked graphical model offers a superior method for associating textual information with visual elements in scientific literature.
  • This advancement holds potential for more effective and accurate biological data mining from diverse research sources.