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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 ...
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 ...
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 ...
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 fixed mapping between an initial state and a final state. Starting from an initial condition and moving forward in time, a deterministic process always generates the same trajectory, and no two trajectories cross in state space. Difference equations/Maps – discrete time, continuous state space.
Phylogenetic trees generated by computational phylogenetics can be either rooted or unrooted depending on the input data and the algorithm used. A rooted tree is a directed graph that explicitly identifies a most recent common ancestor (MRCA), [citation needed] usually an inputed sequence that is not represented in the input.
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 ...
The Smith-Waterman algorithm was an extension of a previous optimal method, the Needleman–Wunsch algorithm, which was the first sequence alignment algorithm that was guaranteed to find the best possible alignment. However, the time and space requirements of these optimal algorithms far exceed the requirements of BLAST.