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In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. [1] It is part of the families of probabilistic graphical models and variational Bayesian methods .
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An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.
The Variant Call Format or VCF is a standard text file format used in bioinformatics for storing gene sequence or DNA sequence variations. The format was developed in 2010 for the 1000 Genomes Project and has since been used by other large-scale genotyping and DNA sequencing projects.
A key step in the derivation of the binary power law by Hughes and Madden was the observation made by Patil and Stiteler [61] that the variance-to-mean ratio used for assessing over-dispersion of unbounded counts in a single sample is actually the ratio of two variances: the observed variance and the theoretical variance for a random ...
The introduction of ASV methods was marked by a debate about their utility. Although OTUs do not provide such precise and accurate measurements of sequence variation, they are still an acceptable and valuable approach.
An ancestral graph is a further extension, having directed, bidirected and undirected edges. [4] Random field techniques A Markov random field, also known as a Markov network, is a model over an undirected graph. A graphical model with many repeated subunits can be represented with plate notation.
Graph neural networks are one of the main building blocks of AlphaFold, an artificial intelligence program developed by Google's DeepMind for solving the protein folding problem in biology. AlphaFold achieved first place in several CASP competitions.