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Related Concept Videos

Types of Selection01:46

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
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Improving Translational Accuracy02:07

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Related Experiment Video

Updated: May 10, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

GeneRIF indexing: sentence selection based on machine learning.

Antonio J Jimeno-Yepes1, J Caitlin Sticco, James G Mork

  • 1National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. antonio.jimeno@gmail.com

BMC Bioinformatics
|June 4, 2013
PubMed
Summary
This summary is machine-generated.

Machine learning methods can automate the selection of sentences for Gene Reference Into Function (GeneRIF) annotation, achieving performance comparable to human annotators. This approach aids in identifying novel gene functions from scientific literature.

Related Experiment Videos

Last Updated: May 10, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene Reference Into Function (GeneRIF) entries describe novel gene functions and are manually curated by the National Center for Biotechnology Information (NCBI).
  • Automating GeneRIF creation requires identifying relevant genes and sentences describing novel functions within MEDLINE citations.

Purpose of the Study:

  • To develop and evaluate machine learning methods for supporting the creation of GeneRIF entries.
  • To automate the sentence selection process for GeneRIF annotation.

Main Methods:

  • Compared various machine learning algorithms and sentence features extracted from MEDLINE.
  • Utilized features such as sentence location, discourse labels, and functional terminology.
  • Evaluated Naïve Bayes classifier with optimized feature sets.

Main Results:

  • Machine learning approaches with specific feature combinations achieved performance close to human annotators.
  • Naïve Bayes demonstrated strong performance with features including sentence location, discourse, and functional terminology.
  • The developed methods show potential for automating GeneRIF sentence selection.

Conclusions:

  • Machine learning can automate sentence selection for GeneRIF annotation, reaching human-level performance.
  • Current methods are validated for the human species.
  • Future work includes extending the methodology to other species and improving gene mention normalization.