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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 ...
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 ...
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 ...
Computational phylogenetics, phylogeny inference, or phylogenetic inference focuses on computational and optimization algorithms, heuristics, and approaches involved in phylogenetic analyses. The goal is to find a phylogenetic tree representing optimal evolutionary ancestry between a set of genes, species, or taxa.
Mathematical and theoretical biology, or biomathematics, is a branch of biology which employs theoretical analysis, mathematical models and abstractions of living organisms to investigate the principles that govern the structure, development and behavior of the systems, as opposed to experimental biology which deals with the conduction of ...
Development of new mathematical algorithms and statistical measures to assess relationships among members of large data sets. For example, there are methods to locate a gene within a sequence, to predict protein structure and/or function, and to cluster protein sequences into families of related sequences.
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.