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Bioinformatics is the name given to these mathematical and computing approaches used to glean understanding of biological processes. Common activities in bioinformatics include mapping and analyzing DNA and protein sequences, aligning DNA and protein sequences to compare them, and creating and viewing 3-D models of protein structures.
Computational anatomy is the study of anatomical shape and form at the visible or gross anatomical scale of morphology. It involves the development of computational mathematical and data-analytical methods for modeling and simulating biological structures.
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In bioinformatics, sequence analysis is the process of subjecting a DNA, RNA or peptide sequence to any of a wide range of analytical methods to understand its features, function, structure, or evolution. It can be performed on the entire genome, transcriptome or proteome of an organism, and can also involve only selected segments or regions ...
One example of the use of bioinformatics and the use of computational methods is the study of protein biomarkers. Computational predictive models [71] have shown that extensive and diverse feto-maternal protein trafficking occurs during pregnancy and can be readily detected non-invasively in maternal whole blood.
Computational genomics refers to the use of computational and statistical analysis to decipher biology from genome sequences and related data, [1] including both DNA and RNA sequence as well as other "post-genomic" data (i.e., experimental data obtained with technologies that require the genome sequence, such as genomic DNA microarrays).
In the past few decades, leaps in genomic research have led to massive amounts of biological data. As a result, bioinformatics was created as the convergence of genomics, biotechnology, and information technology, while concentrating on biological data. Biological data has also been difficult to define, as bioinformatics is a wide-encompassing ...
Metagenomics is the study of microbial communities from environmental DNA samples. [44] Currently, limitations and challenges predominate in the implementation of machine learning tools due to the amount of data in environmental samples. [45] Supercomputers and web servers have made access to these tools easier. [46]