How Scientists Are Accelerating Chemistry Discoveries With Automation

Researchers have developed an automated workflow that could accelerate the discovery of new pharmaceutical drugs and other useful products. The new approach could enable real-time reaction analysis and identify new chemical-reaction products much faster than current laboratory methods.

Q&A: How to train AI when you don’t have enough data

As researchers explore potential applications for AI, they have found scenarios where AI could be really useful but there’s not enough data to accurately train the algorithms. Jenq-Neng Hwang, University of Washington professor of electrical and computer and engineering, specializes in these issues.

The Time Is Now for Artificial Intelligence, Machine Learning

From artificial intelligence (AI) and data integration to natural language processing and statistics, the Cedars-Sinai Department of Computational Biomedicine is utilizing the latest technological advances to find solutions to some of the most complex healthcare issues.

Machine learning algorithm identifies individuals who experience the largest reduction in depression risk from Medicaid coverage

Previous research has demonstrated that Medicaid coverage reduces the risk for developing depression among recipients, but the question is who benefits most from coverage. Using a tool called machine learning causal forest to analyze data from the Oregon Health Insurance…

JMIR Neurotechnology Invites Submissions on Brain-Computer Interfaces (BCIs)

JMIR Publications is pleased to announce a new theme issue in JMIR Neurotechnology exploring brain-computer interfaces (BCIs) that represent the transformative convergence of neuroscience, engineering, and technology.

Imageomics poised to enable new understanding of life

Imageomics, a new field of science, has made stunning progress in the past year and is on the verge of major discoveries about life on Earth, according to one of the founders of the discipline.
Tanya Berger-Wolf, faculty director of the Translational Data Analytics Institute at The Ohio State University, outlined the state of imageomics in a presentation at the annual meeting of the American Association for the Advancement of Science.

Shuffling the deck for privacy

By integrating an ensemble of privacy-preserving algorithms, a KAUST research team has developed a machine-learning approach that addresses a significant challenge in medical research: How to use the power of artificial intelligence (AI) to accelerate discovery from genomic data while protecting the privacy of individuals

Researchers Characterize the Immune Landscape in Cancer

Researchers from the Icahn School of Medicine at Mount Sinai, in collaboration with the Clinical Proteomic Tumor Analysis Consortium of the National Institutes of Health, The University of Texas MD Anderson Cancer Center, Sylvester Comprehensive Cancer Center at the University of Miami Miller School of Medicine, and others, have unveiled a detailed understanding of immune responses in cancer, marking a significant development in the field. The findings were published in the February 14 online issue of Cell. Utilizing data from more than 1,000 tumors across 10 different cancers, the study is the first to integrate DNA, RNA, and proteomics (the study of proteins), revealing the complex interplay of immune cells in tumors. The data came from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), a program under the National Cancer Institute.

Argonne scientists use AI to identify new materials for carbon capture

Researchers at the U.S. Department of Energy’s Argonne National Laboratory have used new generative AI techniques to propose new metal-organic framework materials that could offer enhanced abilities to capture carbon

Argonne training program alumni find success in extreme-scale computing

Past attendees of the annual Argonne Training Program on Extreme-Scale Computing are thriving in careers across the field of high performance computing.

Mount Sinai Researchers Awarded $4.1 Million NIH Grant to Advance Understanding of Sleep Apnea Using Artificial Intelligence

Machine-learning method aims to predict consequences of serious sleep disorder impacting millions in the U.S.

Cedars-Sinai High-Risk Pregnancy Experts Share Latest Research at Annual Scientific Meeting

High-risk pregnancy specialists from Cedars-Sinai will share their research findings at the Society for Maternal-Fetal Medicine 2024 Pregnancy Meeting, Feb.10-14, in National Harbor, Maryland.

Artificially intelligent software provides a detailed look at jets of plasma used to treat cancer

Artificially intelligent software has been developed to enhance medical treatments that use jets of electrified gas known as plasma. Developed by researchers at Princeton Plasma Physics Laboratory and the George Washington University, the computer code predicts the chemicals emitted by cold atmospheric plasma devices, which can be used to treat cancer and sterilize surfaces.

New AI Technique Significantly Boosts Medicare Fraud Detection

In Medicare insurance fraud detection, handling imbalanced big data and high dimensionality remains a significant challenge. Systematically testing two imbalanced big Medicare datasets, researchers demonstrate that intelligent data reduction techniques improve the classification of high imbalanced big Medicare data.

American nuclear power plants are among the most secure in the world — what if they could be less expensive, too?

Argonne collaborates with Purdue University on new research aimed at lowering the cost of developing small nuclear reactors.

Argonne researchers to present cutting-edge work at SC23 conference

Argonne scientists recognized for use of exascale computing tools to achieve high-fidelity simulations of advanced nuclear reactor systems and high-resolution simulations that reduce uncertainty in climate model predictions.

Advances in machine learning for nuclear power operations spell a brighter future for carbon-free energy

Researchers at Argonne are harnessing the power of machine learning to enhance the safety and efficiency of next-generation nuclear reactors. Using a specialized model, researchers may be able to detect anomalies in reactor operations even when they are masked by other noises, ensuring a safer energy future.

UAlbany Expert Available to Discuss President Biden’s Executive Order on AI

ALBANY, N.Y. (Nov. 1, 2023) — On Monday, President Biden issued a new executive order on “Safe, Secure, and Trustworthy Artificial Intelligence,” aimed at ensuring the United States leads the way in leveraging the promise of the technology, while also…

The AI Revolution: Surgeons Share Insights on Integrating AI into Surgical Care

A panel of leading surgeons convened recently to discuss the transformative role of artificial intelligence (AI) in modern surgical practices. The surgeons, all pioneers in adopting AI into their work and studying potential applications, illustrated how this technology is revolutionizing patient care before, during, and after surgery.

Artificial intelligence may help predict infection risks after implant-based breast reconstruction

Artificial intelligence (AI) techniques may provide a more accurate approach to predicting the risk of periprosthetic infection after implant-based breast reconstruction, reports a study in the November issue of Plastic and Reconstructive Surgery®, the official medical journal of the American Society of Plastic Surgeons (ASPS).

A Cancer Survival Calculator Is Being Developed Using Artificial Intelligence

Researchers have developed an artificial intelligence (AI)–based tool for estimating a newly diagnosed cancer patient’s chances for surviving long term, according to a study presented at the American College of Surgeons (ACS) Clinical Congress 2023.

A revolution in the making

Argonne National Laboratory is shaping Industry 4.0 with groundbreaking research into advanced ways of making things more effective, efficient and economical, using the most cutting-edge materials and processes, with the lowest possible environmental impact.

Researchers create a neural network for genomics—one that explains how it achieves accurate predictions

A team of New York University computer scientists has created a neural network that can explain how it reaches its predictions. The work reveals what accounts for the functionality of neural networks—the engines that drive artificial intelligence and machine learning—thereby illuminating a process that has largely been concealed from users.

Using artificial intelligence, Argonne scientists develop self-driving microscopy technique

Argonne researchers have tapped into the power of AI to create a new form of autonomous microscopy.

Department of Energy Announces $16 Million for Research on the DIII-D National User Facility and Small-scale Experiments

Today, the U.S. Department of Energy (DOE) announced $16 million in funding for nine projects that are focused on advancing innovative fusion technology and collaborative research on small-scale experiments and on the DIII-D National Fusion Facility, an Office of Science scientific user facility. The projects will be executed under 16 awards at 13 institutions across the nation.