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Related Concept Videos

How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Data: Types and Distribution01:19

Data: Types and Distribution

In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
Functional Classification of Joints01:09

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
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Related Experiment Video

Updated: May 9, 2026

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
08:09

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Managing the data deluge: data-driven GO category assignment improves while complexity of functional annotation

Julien Gobeill1, Emilie Pasche, Dina Vishnyakova

  • 1Library and Information Sciences, University of Applied Sciences - HEG, CH-1227 Geneva, Switzerland. Julien.gobeill@hesge.ch

Database : the Journal of Biological Databases and Curation
|July 12, 2013
PubMed
Summary
This summary is machine-generated.

Machine learning text mining significantly improves Gene Ontology (GO) functional profiling of publications compared to thesaurus-based methods. This advancement aids biologists in accessing curated knowledge more efficiently.

Related Experiment Videos

Last Updated: May 9, 2026

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
08:09

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Text Mining

Background:

  • Curated biological data often lags behind current scientific literature.
  • Text mining offers a solution for identifying and accessing biological knowledge within publications.
  • Gene Ontology (GO) category assignment is crucial for various applications, including search engines and curation support.

Purpose of the Study:

  • To compare the effectiveness of a state-of-the-art thesaurus-based system with a machine learning system for GO functional profiling of MEDLINE abstracts.
  • To evaluate the evolution of system performance and resource utilization over time.

Main Methods:

  • A machine learning system (k-Nearest Neighbours) was developed and compared against a thesaurus-based system.
  • Systems were trained and evaluated using GO Annotations (GOA) and MEDLINE abstracts.
  • Performance was measured by the ability to predict GO terms used in curation (Recall at 20 - R20).

Main Results:

  • The thesaurus-based system showed constant effectiveness (R20: 0.28-0.31) despite significant expansion of GO synonyms.
  • The machine learning system demonstrated steady improvement, with R20 increasing from 0.38 in 2006 to 0.56 in 2012, driven by knowledge base growth.
  • The machine learning approach significantly outperformed the thesaurus-based method in GO term prediction accuracy.

Conclusions:

  • Machine learning approaches, leveraging growing curated knowledge bases, are superior to thesaurus-based methods for GO functional profiling.
  • These advanced text mining systems enhance the efficiency of assisting biologists in knowledge discovery and curation.