Articles tagged with Machine Learning
A new study led by Worcester Polytechnic Institute aims to determine whether AI can help doctors predict which patients will benefit from mindfulness-based stress reduction in managing chronic lower back pain. The research uses machine learning and physiological data from fitness sensors to detect patterns that may not be apparent to d...
A new study using a combination of traditional and machine learning techniques found a previously unknown theropod species in the famous Kem Kem beds of Morocco. The analysis confirmed the presence of Noasauridae, a rare group of small theropods with long necks.
Researchers are developing a new framework to integrate renewable energy sources with the power grid using machine learning. The goal is to ensure efficient and stable operations while maximizing wind and solar power usage.
Researchers at the University of California - San Diego developed a mathematical formula that reveals how neural networks learn to detect relevant patterns in data. The Average Gradient Outer Product (AGOP) formula helps interpret which features the network is using to make predictions, improving the accuracy and reliability of AI syst...
Researchers developed an AI framework that combines dimension reduction techniques with a new clustering algorithm to quickly identify groups of viral genomes at risk. This enables proactive response measures like tailored vaccine development, potentially eliminating emerging variants before they spread.
TaskMatrix.AI uses APIs to connect general-purpose foundation models with specialized models for specific tasks. The tool can perform digital and physical tasks, provide interpretable responses, and learn continuously.
Researchers develop framework to assess relative value of rules and data in AI models, improving efficiency and accuracy in scientific problems. The framework optimizes model training by tweaking the influence of different rules, filtering out redundant ones, and identifying synergistic relationships between rules.
New research combines images with computer-enabled analysis to tackle biological questions globally. Imageomics aims to improve image classification and analysis using machine learning and computer vision, enabling faster scientific discoveries.
Researchers at Carnegie Mellon University have created a new machine learning model that can simulate reactive processes in diverse organic materials and conditions. The model, called ANI-1xnr, performs simulations with significantly less computing power and time than traditional quantum mechanics models.
Researchers developed a machine learning model to assess the quality of health news stories, outperforming laypeople in evaluating their accuracy. The model used expert criteria to classify articles as 'satisfactory' or 'not satisfactory', highlighting the need for multiple criteria in evaluating news.
Researchers at XPANCEO and Nobel laureate Konstantin S. Novoselov unveil new properties of rhenium diselenide and rhenium disulfide, enabling novel light-matter interaction. This breakthrough has huge potential for integrated photonics, healthcare and AR applications.
A study using machine learning classifies galaxy mergers and finds that mergers are not strongly associated with black-hole growth. Cold gas at the center of the host galaxy is necessary for rapid growth, suggesting a more complex relationship between galaxy evolution and supermassive black holes.
Researchers develop AI model to predict combinations of gene perturbations that can transform cell type or restore diseased cells. The study's findings have potential applications in regrowing injured tissues and transforming cancer cells back into normal cells.
The new AI model uses a visual map to explain each diagnosis, helping doctors follow its line of reasoning and check for accuracy. The tool aims to catch diseases in their earliest stages, making it easier on doctors and patients alike.
A new machine learning approach using Alu elements in blood plasma has improved the detection of cancer early by reaching 98.9% specificity. The test can catch 41% more cancer cases than existing biomarkers and is expected to complement other cancer tests.
Researchers discovered similar genetic elements underlying vocal learning in humans, bats, whales, and seals. AI-powered analysis identified 50 gene regulatory elements associated with vocalization and autism in multiple mammalian species.
Researchers argue that current automated toxicity detection methods can improve but require human intervention to review decisions. Companies can take steps to improve working conditions and platform cultures that prioritize kindness and respect.
An international team of scientists developed AI technology to analyze limited data on rare diseases. The method uses multi-layer networks to explore relationships between genes in patients, revealing genetic causes and severity. This breakthrough opens new avenues for treating rare diseases, including myasthenic-congenital syndromes.
A new American Heart Association scientific statement highlights the value of AI technology in improving cardiovascular disease patient outcomes. Despite its potential, AI applications face gaps and challenges, including robust clinical validation and addressing biases.
Researchers at RMIT University have developed a reprogrammable light-based processor that could enable efficient quantum computations. The device, which uses photons to carry information, reduces 'light losses', a critical factor in maintaining computation accuracy.
A new AI model developed by MIT researchers breaks down complex warehouse navigation into smaller chunks, identifying optimal areas for decongesting robots. The technique improves efficiency by nearly four times, opening up potential applications in other complex planning tasks.
A new process using artificial intelligence (AI) predicts carbon cycles in agroecosystems, surpassing traditional models in accuracy and speed. This breakthrough enables fair and accurate compensation for farmers, fostering trust in carbon markets and promoting sustainable practices.
A groundbreaking technology recognizes human emotions in real time, combining verbal and non-verbal expression data for accurate emotional information extraction. The system features a personalized skin-integrated facial interface that enables self-powered, flexible, and transparent emotion recognition.
Researchers have developed a method to decode mouse neural activity, enabling accurate determination of location and direction within an open environment. This breakthrough could inform the design of intelligent machines that navigate autonomously without GPS or satellite guidance.
The USC team created a low-cost, customizable learning kit for students to build their own 'robot friend' using the Blossom robot. The three-part module provides hands-on experience and instruction on various AI aspects, including robotics, machine learning, and software engineering.
Researchers developed a novel machine learning-based approach to analyze diffuse reflectance spectroscopy data, achieving higher accuracies and speeds than existing methods. The 'wavelength-independent regressor' model overcomes use-error limitations by incorporating diverse datasets, making it suitable for clinical settings.
A team of scientists proposed a general deep learning framework based on DQN algorithm to efficiently design wavelength-selective thermal emitters (WS-TEs) with excellent performance for different applications. The framework autonomously selects materials and optimizes structural parameters for optimal emissivity spectra.
A new machine learning model predicts how ingested drugs interact with transport proteins in the body, identifying previously unknown interactions and potential dangers.
A new emulator model improves auroral current system simulations, enabling faster and more efficient space weather forecasts. The Surrogate Model for REPPU Auroral Ionosphere version 2 (SMRAI2) is a million times faster than physics-based simulations and incorporates seasonal effects.
Researchers at UCSF used clinical data and a precision medicine approach to identify early risk factors for Alzheimer’s disease, predicting its onset with 72% accuracy. High cholesterol, osteoporosis, and erectile dysfunction were found to be predictive factors in both men and women.
Researchers at the University of Manchester have developed new methods to simulate blood flow, enabling faster and more accurate modeling of vascular diseases. These advancements have the potential to transform medical treatment and device innovation, providing real-time insights during surgical procedures and improving patient outcomes.
Researchers developed a multipronged strategy to identify transporters used by different drugs, revealing potential interactions between commonly prescribed antibiotics and blood thinners. The approach has the potential to improve patient treatment and predict potential toxicities.
Scientists at Tokyo University of Science used deep learning to predict single-molecule magnets from a pool of 20,000 metal complexes, identifying 70% accuracy in distinguishing between SMMs and non-SMMs.
Researchers are using machine learning tools to understand biological traits from images, enabling new discoveries about life on Earth. Imageomics is analyzing the relationship between observable phenotypes and genome, leading to a better understanding of direct connections.
Researchers found that widely used machine learning tools produce biased results for immunotherapy research, as they rely heavily on datasets from higher-income communities. This can lead to ineffective treatments for lower-income populations. The study highlights the need for accurate and unbiased data in machine learning models.
A recent study found that a commercial machine learning tool was moderately successful in predicting hospitalization-related kidney injury, but struggled to identify high-risk patients. The tool performed better for low-risk patients and Stage 1 HA-AKI cases.
Researchers developed a framework for standardizing biomarker development and validation to improve the prediction of age-related health outcomes. The study highlights the need for expanded focus on functional decline, frailty, chronic disease, and disability, and calls for harmonization of omic data to enhance reliability.
A KAUST research team has developed a machine-learning approach that balances privacy preservation and model performance using ensemble privacy-preserving algorithms. The approach, called PPML-Omics, achieves better model performance while keeping the same level of privacy protection compared to previous methods.
Researchers at North Carolina State University are developing a suite of performance metrics to standardize the evaluation of self-driving labs in chemistry and materials science. These metrics aim to compare different lab technologies and identify areas for improvement, ultimately advancing the field and accelerating discovery.
Researchers from Argonne National Laboratory and the University of Illinois Urbana-Champaign used generative AI to quickly assemble over 120,000 new MOF candidates for carbon capture. The approach combines AI with high-throughput screening, molecular dynamics simulations and theory-based design to identify optimal materials.
A study published in Oncotarget has identified specific mutational and therapeutic landscapes of pancreatic cancer in the Russian population. By applying machine learning models to full exome individual data, researchers received personalized recommendations for targeted treatment options for each clinical case.
A new study reveals that online images reinforce powerful gender stereotypes, with female and male associations being more extreme among Google Images than within text. The study also found that bias in images is more psychologically potent than in text, leading to stronger biases even three days later.
Researchers used data from 9,300 miles of Greek roads to develop a machine-learning model predicting crash sites. The model identified key features such as abrupt speed limit changes and incomplete lane markings as predictors of crashes. The study's findings have implications for improving road safety globally.
Researchers use machine learning to combine mismatched datasets and reduce variation by over 95%, retaining meaningful differences. The approach has potential to provide deeper understanding of normal metabolism and identify biomarkers for disease.
Researchers at UC Santa Cruz find that popular link prediction metrics are flawed and do not accurately measure algorithm performance. They recommend using a new metric, VCMPR, to benchmark link prediction tasks and highlight the importance of accurate metrics in machine learning decision-making.
Researchers developed sensors using aerogels to detect formaldehyde, a common indoor air pollutant, with real-time detection capabilities. The sensors require minimal power and can distinguish between different gases.
A new paper argues that LLMs can interpret and analyze neuroscientific data, unlocking new insights and potential treatments. Lead author Danilo Bzdok suggests that scientists may not always fully understand the mechanism behind biological processes discovered by LLMs.
A new depth from focus/defocus approach, DDFS, combines model-based and learning-based strategies to achieve notable improvements in performance and applicability. The proposed method outperformed state-of-the-art methods in various metrics for several image datasets.
A machine learning study of 22,000 surgical cases found that models can inform individual prognosis and aid in decision-making to reduce ineffective spine care. The findings suggest that these models may improve outcomes for patients undergoing lumbar disc herniation surgery.
Researchers developed a machine learning framework that encodes images like a retina, reducing sensory encoding challenges in neural prostheses. The actor-model approach produced images eliciting a neuronal response more akin to the original image response.
Assistant Professor Santiago Segarra at Rice University has won the NSF CAREER Award to develop a new approach for AI-powered climate prediction by leveraging structural properties in real-world data. The research aims to create more effective learning algorithms for structured domains.
Researchers have created a genAI model called 'drugAI' that can generate unique molecular structures for potential drugs with high binding affinity and efficacy. The model outperforms traditional methods in terms of speed and cost, opening up new possibilities for disease treatment.
A new study uses GPT-3 to simplify chemical analysis, achieving accuracy surpassing state-of-the-art models. The approach fine-tunes the language model with curated Q&As, enabling easy and fast discovery in low-data chemistry.
Researchers from the University of Rochester's Laboratory for Laser Energetics demonstrated an effective 'spark plug' for direct-drive methods of inertial confinement fusion (ICF), achieving a plasma hot enough to initiate fusion reactions. The successful experiments use the OMEGA laser system, with the goal of eventually producing fus...
Researchers have developed a novel approach to determine the age of mosquitoes, which could help improve pesticide strategies and reduce the spread of diseases like malaria. The method uses surface-enhanced Raman spectroscopy (SERS) to analyze biomolecules in mosquito water extract.
Researchers developed an AI algorithm that evaluates potential working dogs' personalities using data from nearly 8,000 C-BARQ responses. The algorithm clusters responses into five personality types and can help shelters reduce animal returns by matching them with suitable adoptive families.
Researchers developed a molecular predictor of radiation response using cell line data and machine learning-based approaches, capturing a wider range of biological processes. The new gene signature has the potential to aid decision-making, personalize treatments, and improve outcomes for various types of cancers.
Researchers at University of Virginia Health System developed a new approach to machine learning that identifies drugs minimizing harmful scarring after heart attacks. The tool predicts and explains drug effects for other diseases as well.
A machine learning technique called LASSO was used to analyze blood samples from six countries, identifying seven genes that can predict the risk of developing a secondary respiratory bacterial infection. The findings aim to guide clinicians in making more informed decisions about antibiotic use.
The team proposed a novel machine learning model with data augmentation, which accurately predicts the plastic anisotropic properties of wrought Mg alloys. The model showed significantly better robustness and generalizability than other models, paving the way for improved design and manufacturing of metal products.