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Machine learning algorithms have been successfully applied to many bioinformatics problems.
CUDA, the programming platform for GPGPUs, has been used to solve many bioinformatics problems.
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Motivation: At the heart of many important bioinformatics problems, such as gene finding and function prediction, is the classification of biological sequences.
Evolutionary optimization techniques, of which genetic algorithms (GAs) are the most well known class of techniques, have thus been the method of choice for many of these bioinformatics problems.
"It puts them in a better position to tackle the large bioinformatics problems that are looming".
Many bioinformatics methods have been developed for predicting new AMPs.
Certain computational practices imprecisely simplify bioinformatics problems.
They are crucial in many bioinformatics workflows.
Advanced solutions have been introduced for well-known bioinformatics problems.
The support vector machine method (SVM) has been widely applied to many bioinformatics studies: protein-fold assignment [ 18, 19], subcellular localization prediction [ 20, 21], secondary-structure prediction [ 22– 24], and other biological pattern-classification problems [ 25– 28].
There are many Bioinformatics computer based sequencing tools.
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