Difference between revisions of "Team:NTNU Trondheim/Software/iGEM Matchmaker"

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   <img class="img-responsive" style="max-width:500px" src="https://static.igem.org/mediawiki/2015/9/95/NTNU_Trondheim_Matchmaker.png" alt="Figure 1. Plate reader results. Measurement of Fluorescence by OD600 = 0.5
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   <figcaption>The new version of the iGEM Matchmaker automatically matches teams based on the keyword analysis of their project abstract and website.</figcaption>
 
   <figcaption>The new version of the iGEM Matchmaker automatically matches teams based on the keyword analysis of their project abstract and website.</figcaption>
 
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<p>Keywords are extracted from the project abstract and description pages using keyword extraction algorithms. Based on training data from the food and drug administration, <a href="https://github.com/zelandiya/maui">MAUI</a> uses machine learning to extract the most relevant keywords for the teams and measures the weight of the keyword.</p>
 
<p>Keywords are extracted from the project abstract and description pages using keyword extraction algorithms. Based on training data from the food and drug administration, <a href="https://github.com/zelandiya/maui">MAUI</a> uses machine learning to extract the most relevant keywords for the teams and measures the weight of the keyword.</p>
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  <img class="img-responsive" style="max-width:500px" src="https://static.igem.org/mediawiki/2015/5/5f/NTNU_Trondheim_Matchmaker_visits.png" alt="">
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  <figcaption>Number of unique team visits to the iGEM Matchmaker websites one week within the announcement of the 3.0 version.</figcaption>
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<h3>Matchmaking</h3>
 
<h3>Matchmaking</h3>

Revision as of 13:04, 18 September 2015



iGEM Matchmaker

Software repository

Our software is available on the iGEM GitHub software repository!

We have released a new version of the iGEM Matchmaker. New features include automatic matchmaking of iGEM teams using machine learning and keyword extraction. The website has been upgraded to use Bootstrap 3.0.


The new version of the iGEM Matchmaker automatically matches teams based on the keyword analysis of their project abstract and website.

Code

The code is available on GitHub.

Keyword analysis

Keywords are extracted from the project abstract and description pages using keyword extraction algorithms. Based on training data from the food and drug administration, MAUI uses machine learning to extract the most relevant keywords for the teams and measures the weight of the keyword.

Number of unique team visits to the iGEM Matchmaker websites one week within the announcement of the 3.0 version.

Matchmaking

The principle of matchmaking is as follows:

  • A team will be helped on a topic by a team for which this topic is more relevant.
  • A team will be better at the main topics of their project, and will need help for the least important topics.