Articles tagged with Machine Learning
Researchers developed ChemCrow, an AI-powered tool that integrates expertly designed software tools to autonomously perform chemical synthesis tasks. The system enables plan-and-execute approach with reduced hallucinations and practical application, accelerating research and development in pharmaceuticals and materials science.
A team at the University of Münster developed an improved method for explaining machine predictions of chemical reactions, using mechanisms such as reproduction, mutation and selection. The algorithm creates customised molecular fingerprints that predict chemical reactions with surprising accuracy, suitable for predicting quantum chemi...
Researchers have developed a new imaging technique that rapidly and accurately identifies cancerous tissues in breast samples. The method uses machine learning algorithms trained on hyperspectral dark-field microscopy data to pinpoint regions of invasive ductal carcinoma and invasive mucinous carcinoma.
A study by Albert-László Barabási and colleagues used machine learning to identify phrases representing allusions to foundational papers in the physics literature. The findings suggest that hidden citations obscure true impact in science, with influential ideas becoming so familiar that researchers stop citing their sources.
Researchers from GIST and MIT CSAIL develop a MultiSenseBadmpton dataset to enhance badminton performance analysis and AI-based coaching. The dataset captures joint movements, muscle signals, and gaze movements of badminton players.
Researchers at Iowa State University are developing an AI tool that can identify agricultural pests from photos, including insects and weeds. The project will use a supercomputer to train ensemble models that can analyze images quickly and provide pest-control strategies.
Researchers created GraSSRep and rhea, tools that outperform current methods for handling repeats and structural variants in metagenomic data. These methods use self-supervised learning and graph neural networks to analyze microbiome data, offering new insights into biological processes and potential applications in antibiotic resistance.
A new AI model developed by researchers at the University of British Columbia can accurately predict if a patient receiving cancer care will require mental health services. The AI analyzes oncologist's notes and identifies subtle clues that suggest a patient may benefit from early psychiatric or counselling interventions.
Researchers found a strong association between favorable survival outcomes and high populations of tissue-resident memory T cells in melanoma patients. The study identified 11 distinct gene signatures that correlate with T cell abundance and patient survival, suggesting a crucial role for T cells in immunomodulation.
Researchers developed a new AI algorithm called Maximum Diffusion Reinforcement Learning (MaxDiff RL) to improve robot reliability. The algorithm enables robots to learn complex skills more efficiently by encouraging exploration of their environments.
A massive open dataset, OpenDAC, has been created to accelerate direct air capture technology development while reducing costs. The database enables the training of an AI model that predicts material interactions with high accuracy, significantly faster than traditional chemistry simulations.
A new AI method developed by Swedish researchers can identify toxic substances based on their chemical structure, potentially replacing animal testing. The method has been shown to be more accurate and broadly applicable than existing computational tools, offering a promising alternative for environmental research and authorities.
Researchers designed a new AI model that accurately predicts treatment outcomes for stroke prevention in people with heart disease. The model, called CURE, was trained on a massive dataset of de-identified patient information and fine-tuned to focus on specific health conditions and treatments.
An interdisciplinary team has published guidelines for responsible use of machine learning in science, focusing on transparency and reproducibility. The checklist includes detailed descriptions of machine learning models, data, and experimental designs to ensure integrity and validity.
A machine learning tool called PheNet can identify patients with rare, undiagnosed diseases years earlier, improving outcomes and reducing cost and morbidity. By analyzing patterns in electronic health records, the tool ranks patients by likelihood of having a disorder like common variable immunodeficiency.
A new computer model uses improved artificial intelligence to predict snow and water availability more accurately, considering both time and space. This information can help farmers and water planners make better decisions about water allocation.
FathomVerse combines real underwater images, interactive gameplay, and AI training to improve ocean research. The game engages players in labeling images for machine learning models, contributing to a broader audience in ocean exploration.
Researchers trained a quadruped robot using deep reinforcement learning to learn gait transitions on challenging terrain. The robot transitioned from walking to trotting and then to pronking to avoid falls, demonstrating the emergence of animal-like locomotion.
Researchers found that chatbots like ChatGPT can provide accurate information on climate change-related topics, but not all responses are reliable. The study compared the chatbots' answers to hazard risk indices generated by the Intergovernmental Panel on Climate Change and found more accuracy with tropical storms than droughts.
Researchers developed a device with AI algorithms to analyze suckling strength and pattern in newborns. The system showed improved accuracy over subjective clinician assessments, identifying abnormal patterns that may indicate the need for surgical intervention or improved feeding practices.
Researchers created new maps that accurately describe phosphorus levels in Amazonian soil using artificial intelligence, revealing the region's low concentration of the mineral. This information is crucial for understanding how tropical forests will react to climate change.
A new study by UNC Charlotte Professor Abigail Leavitt LaBella and colleagues reveals that yeasts do not fit the 'jack of all trades, master of none' adage. They found that yeasts with a broad ability to metabolize different carbon sources are actually efficient growers.
A machine learning system called AI-MARRVEL, developed by Baylor College of Medicine, has shown promising results in diagnosing rare Mendelian disorders. The system consistently ranked diagnosed genes as the No. 1 candidate in twice as many cases than other benchmark methods.
A new branch of AI, causal machine learning (ML), can estimate treatment outcomes better than traditional methods. This enables personalized treatment strategies and improved patient health.
Researchers from Drexel University have developed a machine-learning algorithm that can extract and recognize digital fingerprints of AI-generated video, including Stable Video Diffusion, Video-Crafter, and Cog-Video. The algorithm can be trained on just a few examples of new AI generators to detect them.
A Cleveland Clinic study uses reinforcement learning to design antibiotic regimens that minimize resistance and maximize susceptibility. The AI model predicts the most efficient treatment plans for multiple strains of E. coli, informing hospital-wide infection management.
Researchers at the University of Pennsylvania are working on a decision-support system to help clinicians navigate conflicting clinical trials. The system will use artificial intelligence to analyze study results and provide recommendations, aiming to improve patient outcomes and inform treatment decisions.
Researchers at ETH Zurich have developed an AI algorithm that can design new active pharmaceutical ingredients by analyzing protein structures. The algorithm generates blueprints for potential drug molecules that increase or inhibit protein activity, reducing the need for lengthy discovery processes and minimizing side effects.
A new study from the University of Illinois explores the use of hyperspectral imaging and explainable artificial intelligence to assess sweet potato attributes, leading to more informed decision-making and higher-quality products. The results can help industry professionals understand the significance of different features in predictin...
Researchers developed an AI technique to estimate brain age based on EEG scans, making early screening for degenerative brain diseases more accessible. The technology uses machine learning to analyze brain waves and can be used as a low-cost screening tool for large numbers of people.
Using AI software called SLEAP, scientists at Salk Institute are designing climate-saving plants with optimized root systems that can store more carbon. This approach enables researchers to analyze plant features and connect desirable traits to targetable genes, accelerating the development of carbon-capturing plants.
Researchers built more accurate cardiovascular risk models using machine learning, finding that women are underdiagnosed due to sex-neutral criteria. The study used the UK Biobank dataset and found that electrocardiogram (EKG) tests were most effective in improving detection of cardiovascular disease in both men and women.
A new framework mitigates bias in machine classification by evaluating under different fairness metrics and inferring specific bias terms from data. The framework substantially reduces bias in classification outcomes while preserving accuracy across seven datasets and 21 classifiers.
A new AI model generates realistic images of single cells, which are used as synthetic data to train an AI model for better cell segmentation. The researchers found that providing a more diverse dataset during training improves performance.
Researchers used machine learning to analyze over 1.09 million potential metabolite-receptor pairs and predict the likelihood that each interaction contributed to Alzheimer's disease. The study found a protective metabolite called agmatine interacting with a receptor called CA3R in brain cells, reducing CA3R levels and lowering phospho...
A Wayne State University researcher is working to transform code review tools and workflows to address participants' challenges in understanding and communicating code changes. The goal is to produce high-quality feedback using empirical methods, machine learning, and natural language techniques.
A new neural network-based system called BONES improves video streaming quality by up to 13% while reducing download size, according to NJIT researcher Jacob Chakareski. The system uses a mathematical function to optimize data transmission and can be applied to various platforms, including popular video conferencing services.
A team of researchers at MIT has developed a new method to model the irrational behavior of humans, which can be used to predict their future actions. By analyzing an agent's previous decisions, the technique infers its computational constraints and adapts to human collaborators' weaknesses.
A new AI-based approach, PERCEPTION, uses single-cell transcriptomics to predict patient response to cancer drugs and monitor resistance. The approach was validated in three clinical trials for multiple myeloma, breast, and lung cancer, with promising results.
Researchers at the University of Cologne found that training can optimize word recognition, leading to improved reading efficiency. The 'Lexical Categorization Model' uses behavioral findings to predict brain activation and separates known words from unknown letter combinations.
Scientists have found evidence of a Bragg glass phase in a crystal using machine learning and X-ray technology. The study provides insight into the nature of glasses and their unique properties, which could inform material design.
Researchers at NC State University developed a fabric-based touch sensor that can control electronic devices through touch, utilizing machine learning algorithms to improve accuracy. The device, integrated into clothing, activates and controls functions like mobile apps, passwords, and video games with gestures on the sensor.
Researchers have developed an active learning strategy to accelerate the synthesis of high-performance engineered biochar with enhanced CO2 uptake. The approach nearly doubled CO2 capture performance, showcasing its transformative impact.
Researchers from the University of Cambridge used AI to identify compounds that block alpha-synuclein aggregation, a key step in treating Parkinson's disease. This breakthrough could lead to faster development of new treatments for the condition, which affects over six million people worldwide.
Researchers have created a probabilistic computer prototype that combines CMOS with stochastic nanomagnets, achieving superior computational performance and energy-efficiency. The new technology reduces area and energy consumption by four and three orders of magnitude compared to current CMOS circuits.
A research team has successfully created a new dimension in photonic machine learning by incorporating sound waves, enabling the creation of reconfigurable neuromorphic building blocks. This innovation has the potential to revolutionize computing tasks by providing high-speed and large-capacity solutions.
A new study developed two machine learning models to quantify CD8+ cell positivity and classify the immunophenotype of cancer specimens in patients with non-small cell lung cancer. The models hold promise for identifying patients who may benefit from immunotherapy.
Researchers found that ~60% of tissues exhibit a significant negative correlation between age and stemness score, indicating a pan-tissue decline in stemness. This study adds weight to the idea that stem cell deterioration contributes to human aging, with hematopoietic stem cells from older individuals showing higher stemness scores.
Researchers at Bar-Ilan University developed a new AI confidence measure that distinguishes between high- and low-confidence decision making in deep learning architectures. This breakthrough enables the creation of safer and more reliable autonomous vehicles by prioritizing human intervention when confidence levels are lower.
Scientists used a neural network to analyze massive particle collision data from the ATLAS detector, marking the first use of this technique in a collider experiment. The method identified an anomaly that may indicate the existence of an undiscovered particle.
A novel machine learning model has been developed to characterize material surfaces, accurately predicting key electronic properties. The model, which employs artificial neural networks and transfer learning, shows great promise for exploring new materials with superior properties.
A recent machine learning study has discovered a surprising link between wildfires in the western United States and hailstorms in the central US. The research, led by Jiwen Fan, used ML algorithms to analyze vast datasets spanning two decades, predicting hail storms with remarkable accuracy.
Lehigh University researcher A. Emrah Bayrak explores best practices for human-AI collaboration in complex design tasks, aiming to maximize productivity and job satisfaction. His project uses models that predict human decision-making and combines it with AI's training data analysis to determine strategies for division of labor.
Researchers created a system called Holodeck to generate interactive 3D environments, leveraging language models like ChatGPT to control it. The system outperformed earlier tools in evaluating realism and accuracy, with human evaluators preferring its outputs across various indoor environments.
Researchers used fMRI and predictive modeling to decode emotional dimensions of spontaneous thoughts, revealing the involvement of default mode, ventral attention, and frontoparietal networks. The study's findings hold promise for daydream decoding and potential applications in mental health.
A study found a significant association between hospital readmission after fracture surgery and underlying medical conditions. Gait analyses also offered valuable insights into injury impact on locomotion and recovery, optimizing rehabilitation strategies.
A new machine-learning approach outperforms human testers in generating diverse prompts that trigger a wider range of undesirable responses from chatbots. The technique provides a faster and more effective way to ensure the safety of large language models.
Chemists develop new reactions using model systems and substrates to demonstrate versatility. A new computer-aided method reduces subjective bias by analyzing real pharmaceutical compounds' complexity and structural properties. This improves data quality and facilitates machine learning applications.
A study from the University of Gothenburg found that patient images are severely lacking in scientific articles on atopic dermatitis. This lack of visual aids hinders patients' ability to make informed decisions about their care, as they struggle to understand complex medical terminology and figures. The absence of images also affects ...
A new study by Carey Morewedge and colleagues found that people are more likely to recognize bias in algorithmic decisions than their own. This is because algorithms can codify and amplify human bias, but also reveal structural biases in society. The research suggests ways to increase awareness of biases and correct them.