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The primary objectives of this course are: 1) to understand the biological principles that underpin and are illuminated by specific genomic techniques; and 2) to be able to evaluate and utilize existing bioinformatics tools to work with genomic datasets.
Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The process of analyzing and interpreting data can sometimes be referred to as computational biology, however this distinction between the two terms is often ...
While each field is distinct, there may be significant overlap at their interface, [1] so much so that to many, bioinformatics and computational biology are terms that are used interchangeably. The terms computational biology and evolutionary computation have a similar name, but are not to be confused. Unlike computational biology, evolutionary ...
Conferences on bioinformatics and computational biology: CIBB - International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics; ISMB - Intelligent Systems for Molecular Biology; PSB - Pacific Symposium on Biocomputing; RECOMB - Research in Computational Molecular Biology
Research in Computational Molecular Biology (RECOMB) is an annual academic conference on the subjects of bioinformatics and computational biology.The conference has been held every year since 1997 and is widely considered as one of two best international conferences in computational biology publishing rigorously peer-reviewed papers, alongside the ISMB conference.
The concept of biological computation proposes that living organisms perform computations, and that as such, abstract ideas of information and computation may be key to understanding biology.
Clustering is central to much data-driven bioinformatics research and serves as a powerful computational method whereby means of hierarchical, centroid-based, distribution-based, density-based, and self-organizing maps classification, has long been studied and used in classical machine learning settings.
As such, computational genomics may be regarded as a subset of bioinformatics and computational biology, but with a focus on using whole genomes (rather than individual genes) to understand the principles of how the DNA of a species controls its biology at the molecular level and beyond. With the current abundance of massive biological datasets ...
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