Researchers have developed a software tool that identify the regulators of genes. The system leverages a machine learning algorithm to predict which transcription factors are most likely to be active in individual cells.
A team of researchers from Sandia National Laboratories and the U.S. Naval Research Laboratory have developed a new system to model the likelihood of finding methane hydrate and methane gas that was tested in a region of seafloor off the coast of North Carolina. This test was published on March 14 in the scientific journal Geochemistry, Geophysics, Geosystems.
University of Washington researchers have developed a new skill for a smart speaker that for the first time monitors both regular and irregular heartbeats without physical contact.
Linear accelerator operators use computer algorithms to automate some parts of the machine tuning process. These algorithms make fast decisions, but they have not previously incorporated fundamental physics or learned from past mistakes. A new machine learning algorithm learns both from experience and physics simulations to reduce the time needed for a part of the machine tuning process by 65 percent.
Using public data from the entire 1,500-square-mile Los Angeles metropolitan area, PNNL researchers reduced the time needed to create a traffic congestion model by an order of magnitude, from hours to minutes.
PNNL researchers have shown an improved binarized neural network can deliver a low-cost and low-energy computation to help the performance of smart devices and the power grid.
Experts trained a computer to tell which skin cancer patients may benefit from drugs that keep tumors from shutting down the immune system’s attack on them, a new study finds.
In an article published in Nature on October 14, 2020, scientists at Princess Margaret Cancer Centre, University of Toronto, Stanford University, Johns Hopkins, Harvard School of Public Health, Massachusetts Institute of Technology, and others, challenge scientific journals to hold computational researchers to higher standards of transparency, and call for their colleagues to share their code, models and computational environments in publications.
In a study, a team of Penn State researchers report that an algorithm they developed may be able to spot illicit online pharmacies that could be providing customers with substandard medications without their knowledge, among other potential problems.
Berkeley Lab’s Kristin Persson shares her thoughts on what inspired her to launch the Materials Project online database, the future of materials research and machine learning, and how she found her own way into a STEM career.
Scientists may be a step closer to solving some of anthropology’s biggest mysteries thanks to a machine learning algorithm that can scour through remote sensing data, such as satellite imagery, looking for signs of human settlements, according to an international team of researchers.
As a staff member in the Theory and Computation Group at Brookhaven Lab’s Center for Functional Nanomaterials, Qu applies various approaches in artificial intelligence to analyze experimental and computational nanoscience data.
A computer-based method could make it less labor-intensive to determine the crystal structures of various materials and molecules, including alloys, proteins and pharmaceuticals. The method uses a machine learning algorithm, similar to the type used in facial recognition and self-driving cars, to independently analyze electron diffraction patterns, and do so with at least 95% accuracy.
The newly developed system prioritizes patients so that cancer doctors have conversations about their values and goals before it is too late.
The following news release was issued on Aug. 26, 2019 by the U.S. Department of Energy (DOE). It announces funding that DOE has awarded for research in quantum information science related to particle physics and fusion energy sciences. Scientists at DOE’s Brookhaven National Laboratory are principal investigators on two of the 21 funded projects.