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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Pattern recognition in bioinformatics.

Dick de Ridder¹, Jeroen de Ridder, Marcel J T Reinders

¹Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. Tel.: +31 15 2785114; Fax: +31 15 2787022; d.deridder@tudelft.nl.

Briefings in Bioinformatics

|April 6, 2013

Summary

This summary is machine-generated.

Pattern recognition, including classification and clustering, is essential for bioinformatics. This review outlines a pattern recognition course curriculum for bioinformaticians, covering common challenges in data analysis and interpretation.

Keywords:

bioinformatics classification clustering dimensionality reduction pattern recognition

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Area of Science:

Bioinformatics
Computer Science
Life Sciences

Background:

Pattern recognition systems learn from feature-represented instances for tasks like clustering and classification.
Statistical pattern recognition algorithms are widely used in bioinformatics for analyzing high-throughput data.
Applications include marker selection, phenotype prediction, gene grouping, and predicting biological interactions.

Purpose of the Study:

To review the core elements of a pattern recognition course tailored for bioinformatics.
To provide a curriculum framework for undergraduate and graduate students in bioinformatics, computer science, and life sciences.
To highlight common challenges and interpretation pitfalls in applying pattern recognition to biological data.

Main Methods:

The review synthesizes material from existing pattern recognition and machine learning courses.
Curriculum content is structured for BSc, MSc, and PhD levels.
Focus is placed on practical application and interpretation of results in a biological context.

Main Results:

A comprehensive outline of essential pattern recognition topics for bioinformaticians is presented.
The review emphasizes the integration of pattern recognition into bioinformatics education.
Common pitfalls in data analysis and result interpretation are identified.

Conclusions:

Pattern recognition and machine learning are fundamental to modern bioinformatics education.
A structured course is crucial for equipping students with necessary analytical skills.
Understanding application-specific challenges enhances the effective use of pattern recognition in biological research.