To compare traditional statistics to ML approaches, we'll use a simulation of the expression of 40 genes in two phenotypes ( /). Mean gene expression will differ between phenotypes, but we'll set up the simulation so that the mean difference for the first 30 genes is not related to phenotype. The last ten genes will be dysregulated, with systematic differences in mean expression between phenotypes. To achieve this, we assign each gene an average log expression that is the same for both phenotypes. The dysregulated genes (31–40, labeled A–J) have their mean expression perturbed in the phenotype (Figure 1a).
We address the challenge of assessing conservation of gene expression in complex, non-homogeneous datasets. Recent studies have demonstrated the success of probabilistic models in studying the evolution of gene expression in simple eukaryotic organisms such as yeast, for which measurements are typically scalar and independent. Models capable of studying expression evolution in much more complex organisms such as vertebrates are particularly important given the medical and scientific interest in species such as human and mouse. We present a statistical model that makes a number of significant extensions to previous models to enable characterization of changes in expression among highly complex organisms. We demonstrate the efficacy of our method on a microarray dataset containing diverse tissues from multiple vertebrate species. We anticipate that the model will be invaluable in the study of gene expression patterns in other diverse organisms as well, such as worms and insects.
An eerie robot with the face of a small child can make realistic-looking facial expressions. Creepy footage shows Affetto, an android with just a head and no body mimic human expressions like smiling and frowning. The robot was made by researchers from Osaka University in Japan who say it could open the door for androids to have'deeper interactions with humans'. Affetto, who has flesh-coloured skin on its face, can mimic a range of human expressions with incredible accuracy. An eerie robot with the face of a small child can make realistic-looking facial expressions.
In 1862, French neurologist Guillaume-Benjamin-Amand Duchenne de Boulogne published The Mechanism of Human Facial Expression, a scientific and aesthetic text on the ways in which the muscles of the face create various expressions -- a dictionary, so to speak, of what he believed was a universal, God-given language. Duchenne had previously developed a number of therapeutic techniques involving the use of localized electric shocks to stimulate muscles. While conducting experiments for his text, he partnered with Adrien Tournachon, brother of the famed photographer Nadar, to document the expressions he induced in his models with targeted, painless shocks. He charitably described his primary model as "an old toothless man, with a thin face, whose features, without being absolutely ugly, approached ordinary triviality." Together, the physician and photographer documented a range of triggered expressions, from the sly and subtle to the horrific and grotesque.
Having insight into how someone is feeling is critical to understanding them. By using facial expression analysis, you are able to quantify not only facial expressions, but also the emotions of a respondent. There are several ways in which to do facial expression analysis, and the right one for you will depend on your needs, so we've put together an infographic to give you an idea of the methods and the process. Read on to learn about facial expression analysis! If you'd like to learn even more about facial expression analysis, then check out our free guide below!