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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.
In the genomic branch of bioinformatics, homology is used to predict the function of a gene: if the sequence of gene A, whose function is known, is homologous to the sequence of gene B, whose function is unknown, one could infer that B may share A's function. In structural bioinformatics, homology is used to determine which parts of a protein ...
Machine learning can be applied to perform the matching function between (groups of patients) and specific treatment modalities. [ 38 ] Computational techniques are used to solve other problems, such as efficient primer design for PCR , biological-image analysis and back translation of proteins (which is, given the degeneration of the genetic ...
In biology supervised learning can be helpful when we have data that we know how to categorize and we would like to categorize more data into those categories. Diagram showing a simple random forest. A common supervised learning algorithm is the random forest, which uses numerous decision trees to train a model to classify a dataset. Forming ...
A widely used type of composition is the nonlinear weighted sum, where () = (()), where (commonly referred to as the activation function [3]) is some predefined function, such as the hyperbolic tangent, sigmoid function, softmax function, or rectifier function. The important characteristic of the activation function is that it provides a smooth ...
Genetic algorithms deliver methods to model biological systems and systems biology that are linked to the theory of dynamical systems, since they are used to predict the future states of the system. This is just a vivid (but perhaps misleading) way of drawing attention to the orderly, well-controlled and highly structured character of ...
Lastly Hölder and Wilson in 2009 concluded using historical data that ants have evolved to function as a single "superogranism" colony. [9] A very important result since it suggested that group selection evolutionary algorithms coupled together with algorithms similar to the "ant colony" can be potentially used to develop more powerful algorithms.
Different algorithms in evolutionary computation may use different data structures to store genetic information, and each genetic representation can be recombined with different crossover operators. Typical data structures that can be recombined with crossover are bit arrays , vectors of real numbers, or trees .