Protein function predictionProtein function prediction methods are techniques that bioinformatics researchers use to assign biological or biochemical roles to proteins. These proteins are usually ones that are poorly studied or predicted based on genomic sequence data. These predictions are often driven by data-intensive computational procedures. Information may come from nucleic acid sequence homology, gene expression profiles, protein domain structures, text mining of publications, phylogenetic profiles, phenotypic profiles, and protein-protein interaction.
Variational autoencoderIn machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. It is part of the families of probabilistic graphical models and variational Bayesian methods. Variational autoencoders are often associated with the autoencoder model because of its architectural affinity, but with significant differences in the goal and mathematical formulation. Variational autoencoders are probabilistic generative models that require neural networks as only a part of their overall structure.
1970s energy crisisThe 1970s energy crisis occurred when the Western world, particularly the United States, Canada, Western Europe, Australia, and New Zealand, faced substantial petroleum shortages as well as elevated prices. The two worst crises of this period were the 1973 oil crisis and the 1979 energy crisis, when, respectively, the Yom Kippur War and the Iranian Revolution triggered interruptions in Middle Eastern oil exports. The crisis began to unfold as petroleum production in the United States and some other parts of the world peaked in the late 1960s and early 1970s.
