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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.
Machine learning methods for the analysis of neuroimaging data are used to help diagnose stroke. Historically multiple approaches to this problem involved neural networks. [55] [56] Multiple approaches to detect strokes used machine learning. As proposed by Mirtskhulava, [57] feed-forward networks were tested to detect strokes using neural imaging.
Bioinformatics: Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data.
In structural bioinformatics, de novo modeling, also known as ab initio modeling, refers to approaches for obtaining three-dimensional structures from sequences without the necessity of a homologous known 3D structure. Despite the new algorithms and methods proposed in the last years, de novo protein structure prediction is still considered one ...
In bioinformatics, alignment-free sequence analysis approaches to molecular sequence and structure data provide alternatives over alignment-based approaches. [1]The emergence and need for the analysis of different types of data generated through biological research has given rise to the field of bioinformatics. [2]
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).
When applied to gene prediction, neural networks can be used alongside other ab initio methods to predict or identify biological features such as splice sites. [18] One approach [19] involves using a sliding window, which traverses the sequence data in an overlapping manner. The output at each position is a score based on whether the network ...
In academia, computational immunology is a field of science that encompasses high-throughput genomic and bioinformatics approaches to immunology.The field's main aim is to convert immunological data into computational problems, solve these problems using mathematical and computational approaches and then convert these results into immunologically meaningful interpretations.