Articles tagged with Machine Learning
LMU researchers created a tool that combines automated chemical synthesis, high-throughput characterization, and data-driven modeling to control nanocrystal growth. The Synthesizer platform enables precise predictions of material properties, such as color, brightness, or stability, for applications like LEDs, solar cells, and sensors.
Engineers at the University of Pennsylvania have discovered that foams exhibit internal motion resembling deep learning in AI systems. The study suggests a common mathematical principle underlying both foams and AI training, with implications for designing adaptive materials and understanding biological structures.
Researchers have decoded the logic of microRNA strand selection using AI, revealing a conserved and programmable mechanism governing gene regulation. The study found that this decision follows conserved rules rather than chance, with mammalian microRNAs showing a strong bias towards a single dominant strand.
Researchers developed a new technique called CLASSIC that enables large-scale testing of complex DNA circuits in human cells. The approach uses artificial intelligence and machine learning to analyze vast numbers of complete circuits at once, providing scientists with a clearer picture of the rules governing genetic part behavior.
Researchers from Korea University report a breakthrough in reviving an abandoned depression drug target by redesigning the molecular structure of neurokinin-1 receptor antagonists. New compounds exhibiting antidepressant-like effects have been identified, reducing depressive-like behavior and brain inflammation in mice.
Researchers at the University of Groningen developed an atomistic model that predicts the driving force for microstructural twinning in shape memory alloys. This discovery can lead to the creation of new crystalline materials with improved reversible deformations, vibration damping, and impact absorption.
A deep learning model trained on stage II colorectal cancer whole slide images accurately identified features linked to recurrence risk. The study found the model surpassed clinical prognostic parameters in predicting patient outcomes.
A team of researchers developed an AI algorithm capable of classifying complex topological phases of matter without relying on traditional mathematical tools. The breakthrough tackles the notoriously difficult realm of non-Hermitian systems and suggests that AI can surpass human capabilities in certain domains of abstract reasoning.
Researchers at Colorado State University used AI to modify antibodies into stable intrabodies that can visualize histone modifications in real-time. This allows for better understanding of gene expression and its relationship with cancer and other disorders. The team created 19 new antibody-based probes with a 70% success rate, signifi...
A machine-learning-based algorithm developed by Tokyo Metropolitan University researchers can accurately count sister chromatid exchanges (SCEs) in chromosomes, giving a more objective measurement. The accuracy rate is 84%, which could help diagnose disorders like Bloom syndrome with greater consistency.
Artificial intelligence is transforming water quality management by detecting subtle biological changes and triggering timely responses. AI-based sensors can analyze signals directly in the field for near real-time water quality assessment, predicting harmful algal blooms and pathogen outbreaks.
The Mount Sinai Health System has launched an AI-powered clinical trial-matching platform, PRISM, to help connect patients with life-saving trials. The platform uses machine learning to match patients with trials based on their electronic health records and diagnosis.
A new AI tool, CattleFever, uses artificial intelligence and thermal cameras to estimate cattle body temperature from a photo. The system can automatically determine an animal's body temperature within 1 degree of the reading from a thermometer.
Researchers at Princeton University developed a machine learning tool to predict the stability of MOF structures, allowing for faster discovery of advanced materials. The tool achieved accurate predictions 97% of the time and could lead to breakthroughs in battery chemistry, carbon capture, and clean water access.
A modular machine learning framework using LoRA fine-tuning can accurately predict single organic reactions and handle multiple reaction types. The model demonstrates high prediction accuracy comparable to expert experience, achieving results on classic reaction datasets and showing strong adaptability.
Researchers combined Dynamic Optical Contrast Imaging (DOCI) with machine learning to identify thyroid cancer during surgery. The AI analysis framework accurately classified samples across three categories and generated tumor probability maps for precise location identification.
Researchers at Mount Sinai have developed an AI-powered ECG analysis tool that shows promise in detecting Chronic Obstructive Pulmonary Disease (COPD) early. The model achieved high accuracy rates across diverse populations, including a subgroup with irregular heartbeat and smoking exposure.
Jundong Li, an associate professor at the University of Virginia, has received the 2025 Tao Li Award for his significant contributions to data mining and machine learning. His research focuses on developing models that can extract actionable insights from structured data, particularly graphs.
A study demonstrates how optimized genomic prediction models can reliably forecast complex agronomic traits in rapeseed. The research achieves high prediction accuracy for multiple economically important traits, particularly flowering time and yield components.
A comprehensive review outlines how ammonia disrupts biogas production and identifies practical strategies to prevent system failure. Emerging solutions include the integration of artificial intelligence and machine learning into digester monitoring.
The new framework reduces processing time by an average of 87.7% compared to the state-of-the-art approach, achieving cache hit rates between 70% and 90%. It demonstrates linear scalability, handling high query loads efficiently.
A study published in EMBO Molecular Medicine has identified a combination of statins and phenothiazines that shows promise in treating aggressive neuroblastoma. The drug combination was found to impede tumour growth and improve survival rates in laboratory trials with mice.
HSE economists found that AI models like ChatGPT and Claude tend to play 'too smart' and lose in strategic thinking games by assuming a higher level of logic in people than is actually present. The study replicated results from previous human participant experiments, showing LLMs adapt to opponents with varying levels of sophistication.
A newly developed AI model accurately predicts blood loss in patients undergoing high-volume liposuction, showing excellent agreement with estimated volumes. The model's 94% accuracy may help make surgery 'smarter and safer' by informing perioperative management and reducing the risk of complications.
A new study analyzed over 460,000 scientific abstracts to identify key themes, trends, and research gaps in aging research. The study found a growing separation between basic biological studies and clinical research, highlighting the need for integration to translate laboratory discoveries into medical applications.
The new program focuses on advancing foundational research in AI, including innovation in language models and algorithmic efficiency. Google will support research grants, scholarships for students, and educational initiatives at the TAU Center.
Researchers developed RNACOREX, a new open-source software tool that identifies gene regulation networks in cancer. The tool analyzes thousands of molecules simultaneously to detect key interactions, providing an interpretable molecular map that improves understanding of tumors.
Researchers developed a tool to quickly identify resistant strains of S. aureus using genomic profiles and machine-learning models. The approach is based on gene-content information rather than highly detailed genomic profiles, making it more practical for real-life clinical contexts.
A new study from the University of East Anglia finds that AI tools excel at speed and clarity, but human translators remain crucial for tone, cultural nuance, and natural sound. Generative AI can handle parts of video translation, but consumers still notice when something feels off.
A novel machine learning framework predicts protein binding on gold nanoclusters, revealing chemical principles governing biomolecule–gold interactions. The model enables scalable design of effective nanomaterials for biomedical applications.
Researchers developed an electronic nose that can detect and identify two common indoor mold species using nanowires. The e-nose measures changes in electrical resistance to gas molecules interacting with a sensing material, proving its potential for fast and objective monitoring of indoor air quality.
Researchers used machine learning to combine clinical data with biological markers, achieving 89.58% accuracy in predicting patient survival. The study identified key markers such as E2F8, WDR77, and hsa-miR-495-3p, which were associated with tumor growth and cancer development.
Researchers developed a machine learning framework, DeePKS, to improve density functional theory calculations for CO adsorption on metal surfaces. The framework achieves near-hybrid functional accuracy with high efficiency, enabling the exploration of complex catalytic systems.
Researchers from HIRI analyzed over 24,000 bacterial genomes to demonstrate that common machine learning methods can lead to biased results. They found that conventional approaches may fail to capture true resistance signals and limit accuracy in new strains.
A new AI framework uncovers simple, understandable rules governing complex dynamics in nature and technology. The AI generates equations that accurately describe complex systems, revealing hidden variables that govern their behavior. This approach offers scientists a new way to leverage AI for understanding complex systems.
A new study warns that AI is racing ahead of safety checks in GP clinics, putting patients at risk. The research found that many GPs use AI tools without thorough evaluation or regulatory oversight, carrying risks like automation bias and loss of important social details.
Researchers developed an AI model that can detect coronary microvascular dysfunction using a common electrocardiogram, outperforming previous models in diagnostic tasks. The model can accurately identify a condition often missed in emergency department visits, providing a cost-effective and non-invasive way to diagnose serious heart co...
An AI-driven robotic assembly system allows people to build physical objects by simply describing them in words. The system uses generative AI models to build a 3D representation of an object's geometry based on the user's prompt, and then iterates on the design based on feedback from the user.
Researchers developed a transfer learning-enabled framework to predict mechanical properties of particle-reinforced aluminum matrix composites. The model achieves high predictive accuracy, addressing the challenges of limited data availability in traditional machine learning algorithms.
Researchers have launched a new multimodal medical dataset, Observer, capturing anonymized, real-time interactions between patients and clinicians. The dataset links video, audio, transcripts, and electronic health records to study subtleties like body language and environmental factors affecting care.
A team of MIT engineers developed a deep-learning model that predicts how individual cells will fold, divide, and rearrange during a fruit fly's earliest stage of growth. The model achieved 90% accuracy in predicting the movement of 5,000 cells over the first hour of development.
The University of Oklahoma researcher is working on a project using inverse design techniques and AI to create advanced materials that can quickly switch between conductive and insulating states. The goal is to reduce uncertainty in the discovery process and create a scalable material-design methodology.
A Harvard-designed bio-logger captures high-fidelity audio of sperm whale codas, which are later analyzed by machine learning models to uncover structured communication. Recent results show that sperm whales have their own alphabet and use vowels and diphthongs in their language.
A new method has been developed to generate valid confidence intervals for problems involving data that vary across space, helping researchers trust the results of certain experiments. This work can be applied to fields like environmental science and epidemiology.
A new dataset and model improve the efficiency of machine-learning interatomic potentials and their applicability to different chemical elements and material classes. The PET-MAD model uses a compact and denser dataset of 95,595 structures and an original neural network architecture, achieving robust simulations with minimal fine-tuning.
Researchers developed an AI tool to identify patients with undiagnosed Alzheimer's disease using electronic health records, addressing underdiagnosis and healthcare inequities. The model achieved sensitivity rates of 77-81% across diverse populations, promoting fairness while maintaining high accuracy.
A new study used deep learning and large-scale computer simulations to identify structural differences in synthetic cannabinoid molecules that cause them to bind to human brain receptors differently from classical cannabinoids. Researchers found that these substances often trigger the beta arrestin pathway, leading to more severe psych...
Researchers at Florida Atlantic University have developed a deep learning model that detects and evaluates Alzheimer's disease (AD) and frontotemporal dementia (FTD) using EEG brainwave analysis. The model achieved over 90% accuracy in distinguishing individuals with dementia from cognitively normal participants.
Researchers have developed an AI-driven method to uncover genetic interactions driving cancer progression. The approach highlights complex interplay between genes allowing malignant cells to gain momentum and reveals previously hidden cancer drivers.
Researchers at the University of Washington have developed smart headphones that use AI to detect conversation cadence and isolate participants in a noisy soundscape. The prototype was tested with 11 participants, who rated filtered audio more than twice as favorably as baseline sound.
The University of Texas at Dallas has partnered with Tech Mahindra to facilitate collaboration on artificial intelligence (AI) innovation, skill development, and research. The partnership will provide opportunities for students and faculty to advance AI technologies, data science, and cybersecurity.
A breakthrough AI system called OmniPredict can predict human pedestrian behaviors with unprecedented accuracy, revolutionizing self-driving cars and urban mobility. The model combines visual cues with contextual information to anticipate pedestrians' next moves, reducing the risk of accidents and improving traffic safety.
A new advance in EEG imaging technology has improved mapping for epilepsy surgery, providing an accurate and non-invasive method. Pathological HFOs were found to be the most accurate biomarker for identifying epileptogenic brain regions, allowing for precise localization of seizure onset.
The Jackson Laboratory's CARDIOVERSE project uses AI, stem cells, and genetic variation to predict drug safety before human trials. The initiative aims to reduce cardiotoxicity and improve patient stratification in clinical trials.
A Kobe University team has introduced a new method using deep learning for creating tailored simulations that respect physical laws while being computationally efficient. The approach shows superior accuracy in simulating diverse physical systems, including those with chaotic behavior.
Researchers at MD Anderson have made significant discoveries in the treatment of rare bile duct cancers, with zanidatamab showing promising results. Additionally, a study identified RASH3D19 as a target to overcome treatment resistance in KRAS-mutant cancers.
The MIT team developed a new AI-based controller that enables the robot to follow gymnastic flight paths, such as executing continuous body flips. The robot's speed and acceleration increased by 450% and 250%, respectively, compared to previous demonstrations, making it comparable to insects in terms of agility.
Researchers developed instance-adaptive scaling framework that uses process reward model to estimate difficulty of question, enabling LLMs to spend more compute on promising solution paths. This approach achieves comparable accuracy with existing methods while reducing computational cost by up to one-half.
A new study by Cambridge University Press & Assessment and Microsoft Research found that traditional learning activities like making notes remain critical for students' reading comprehension and retention. Note-taking, either alone or combined with large language models (LLMs), was more effective in helping students understand and reme...
The EBRAINS Summit 2025 will bring together experts to assess how neuroscience can drive medical progress, digital innovation, and responsible data use. Preliminary results from the EPINOV clinical trial, integrating virtual brain technology for epilepsy surgery planning, will be presented.