TEXTAL: crystallographic protein model building using AI and pattern recognition.(artificial intelligence): An article from: AI Magazine
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This digital document is an article from AI Magazine, published by Thomson Gale on September 22, 2006. The length of the article is 6234 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.
From the author: TEXTAL is a computer program that automatically-interprets electron density maps to determine the atomic structures of proteins through X-ray crystallography. Electron density maps are traditionally interpreted by visually fitting atoms into density patterns. This manual process can be time-consuming and error prone, even for expert crystallographers. Noise in the data and limited resolution make map interpretation challenging. To automate the process, TEXTAL employs a variety of AI and pattern-recognition techniques that emulate the decision-making processes of domain experts. In this article, we discuss the various ways AI technology is used in TEXTAL, including neural networks, case-based reasoning, nearest neighbor learning and linear discriminant analysis. The AI and pattern-recognition approaches have proven to be effective for building protein models even with medium resolution data. TEXTAL is a successfully deployed application; it is being used in more than 100 crystallography labs from 20 countries.
Citation Details
Title: TEXTAL: crystallographic protein model building using AI and pattern recognition.(artificial intelligence)
Author: Kreshna Gopal
Publication:AI Magazine (Magazine/Journal)
Date: September 22, 2006
Publisher: Thomson Gale
Volume: 27 Issue: 3 Page: 15(10)
Distributed by Thomson Gale
From the author: TEXTAL is a computer program that automatically-interprets electron density maps to determine the atomic structures of proteins through X-ray crystallography. Electron density maps are traditionally interpreted by visually fitting atoms into density patterns. This manual process can be time-consuming and error prone, even for expert crystallographers. Noise in the data and limited resolution make map interpretation challenging. To automate the process, TEXTAL employs a variety of AI and pattern-recognition techniques that emulate the decision-making processes of domain experts. In this article, we discuss the various ways AI technology is used in TEXTAL, including neural networks, case-based reasoning, nearest neighbor learning and linear discriminant analysis. The AI and pattern-recognition approaches have proven to be effective for building protein models even with medium resolution data. TEXTAL is a successfully deployed application; it is being used in more than 100 crystallography labs from 20 countries.
Citation Details
Title: TEXTAL: crystallographic protein model building using AI and pattern recognition.(artificial intelligence)
Author: Kreshna Gopal
Publication:AI Magazine (Magazine/Journal)
Date: September 22, 2006
Publisher: Thomson Gale
Volume: 27 Issue: 3 Page: 15(10)
Distributed by Thomson Gale
