In a first for machine-learning algorithms, a new piece of software developed at Caltech can predict behavior of bacteria by reading the content of a gene. The breakthrough could have significant implications for our understanding of bacterial biochemistry and for the development of new medications. One thrust of modern pharmacology is focused on alleviating ailments by developing drugs that target specific proteins that reside in the membranes of our bodies' cells. These proteins, known as integral membrane proteins (IMP), act as receptors or "gates" that allow materials into and out of cells. Examples of IMPs are G-protein-coupled receptors, which relay information to a cell about its environment, and ion channels, which control the interior environment of a cell by acting as gatekeepers that selectively allow ions to pass in and out of the cell. IMPs are the targets of nearly 50 percent of all drugs on the market.

In a first for machine-learning algorithms, a new piece of software developed at Caltech can predict behavior of bacteria by reading the content of a gene. The breakthrough could have significant implications for our understanding of bacterial biochemistry and for the development of new medications. One thrust of modern pharmacology is focused on alleviating ailments by developing drugs that target specific proteins that reside in the membranes of our bodies' cells. These proteins, known as integral membrane proteins (IMP), act as receptors or "gates" that allow materials into and out of cells. Examples of IMPs are G-protein-coupled receptors, which relay information to a cell about its environment, and ion channels, which control the interior environment of a cell by acting as gatekeepers that selectively allow ions to pass in and out of the cell. IMPs are the targets of nearly 50 percent of all drugs on the market.

Los Angeles, CA – After 4 years of Research and Development, Stock Circles announces an Artificial Intelligence Breakthrough in Stock Investing. "Today we are pleased to announce that Smart Auto-Trading, the first stock investing trading platform to use Artificial Intelligence natively to screen, monitor and auto-trade the S&P 500, yielded conclusive test results by consistently outperforming the benchmark by 2X for the past 2 years" says Jana Clemons, Stock Circles' CMO. According to Clemons, the technology does not make use of traditional Technical Analysis to trade stocks. "Instead, it uses Behavioral Sciences, Machine Learning and Statistical Models to screen, monitor and auto-trade the S&P 500 ". "Stock Circles started verifying the hypothesis that Artificial Intelligence stock investing would produce a higher return than the S&P500 in 2014", says Clemons. "To test the hypothesis, we had to build an entirely new kind of trading platform designed to ingest and process multiple, non- traditional data sources to extract actionable information".