Articles tagged with Machine Learning
Comprehensive exploration of living organisms, biological systems, and life processes across all scales from molecules to ecosystems. Encompasses cutting-edge research in biology, genetics, molecular biology, ecology, biochemistry, microbiology, botany, zoology, evolutionary biology, genomics, and biotechnology. Investigates cellular mechanisms, organism development, genetic inheritance, biodiversity conservation, metabolic processes, protein synthesis, DNA sequencing, CRISPR gene editing, stem cell research, and the fundamental principles governing all forms of life on Earth.
Comprehensive medical research, clinical studies, and healthcare sciences focused on disease prevention, diagnosis, and treatment. Encompasses clinical medicine, public health, pharmacology, epidemiology, medical specialties, disease mechanisms, therapeutic interventions, healthcare innovation, precision medicine, telemedicine, medical devices, drug development, clinical trials, patient care, mental health, nutrition science, health policy, and the application of medical science to improve human health, wellbeing, and quality of life across diverse populations.
Comprehensive investigation of human society, behavior, relationships, and social structures through systematic research and analysis. Encompasses psychology, sociology, anthropology, economics, political science, linguistics, education, demography, communications, and social research methodologies. Examines human cognition, social interactions, cultural phenomena, economic systems, political institutions, language and communication, educational processes, population dynamics, and the complex social, cultural, economic, and political forces shaping human societies, communities, and civilizations throughout history and across the contemporary world.
Fundamental study of the non-living natural world, matter, energy, and physical phenomena governing the universe. Encompasses physics, chemistry, earth sciences, atmospheric sciences, oceanography, materials science, and the investigation of physical laws, chemical reactions, geological processes, climate systems, and planetary dynamics. Explores everything from subatomic particles and quantum mechanics to planetary systems and cosmic phenomena, including energy transformations, molecular interactions, elemental properties, weather patterns, tectonic activity, and the fundamental forces and principles underlying the physical nature of reality.
Practical application of scientific knowledge and engineering principles to solve real-world problems and develop innovative technologies. Encompasses all engineering disciplines, technology development, computer science, artificial intelligence, environmental sciences, agriculture, materials applications, energy systems, and industrial innovation. Bridges theoretical research with tangible solutions for infrastructure, manufacturing, computing, communications, transportation, construction, sustainable development, and emerging technologies that advance human capabilities, improve quality of life, and address societal challenges through scientific innovation and technological progress.
Study of the practice, culture, infrastructure, and social dimensions of science itself. Addresses how science is conducted, organized, communicated, and integrated into society. Encompasses research funding mechanisms, scientific publishing systems, peer review processes, academic ethics, science policy, research institutions, scientific collaboration networks, science education, career development, research programs, scientific methods, science communication, and the sociology of scientific discovery. Examines the human, institutional, and cultural aspects of scientific enterprise, knowledge production, and the translation of research into societal benefit.
Comprehensive study of the universe beyond Earth, encompassing celestial objects, cosmic phenomena, and space exploration. Includes astronomy, astrophysics, planetary science, cosmology, space physics, astrobiology, and space technology. Investigates stars, galaxies, planets, moons, asteroids, comets, black holes, nebulae, exoplanets, dark matter, dark energy, cosmic microwave background, stellar evolution, planetary formation, space weather, solar system dynamics, the search for extraterrestrial life, and humanity's efforts to explore, understand, and unlock the mysteries of the cosmos through observation, theory, and space missions.
Comprehensive examination of tools, techniques, methodologies, and approaches used across scientific disciplines to conduct research, collect data, and analyze results. Encompasses experimental procedures, analytical methods, measurement techniques, instrumentation, imaging technologies, spectroscopic methods, laboratory protocols, observational studies, statistical analysis, computational methods, data visualization, quality control, and methodological innovations. Addresses the practical techniques and theoretical frameworks enabling scientists to investigate phenomena, test hypotheses, gather evidence, ensure reproducibility, and generate reliable knowledge through systematic, rigorous investigation across all areas of scientific inquiry.
Study of abstract structures, patterns, quantities, relationships, and logical reasoning through pure and applied mathematical disciplines. Encompasses algebra, calculus, geometry, topology, number theory, analysis, discrete mathematics, mathematical logic, set theory, probability, statistics, and computational mathematics. Investigates mathematical structures, theorems, proofs, algorithms, functions, equations, and the rigorous logical frameworks underlying quantitative reasoning. Provides the foundational language and tools for all scientific fields, enabling precise description of natural phenomena, modeling of complex systems, and the development of technologies across physics, engineering, computer science, economics, and all quantitative sciences.
A new quantum-classical approach has been developed for designing photochromic materials, accelerating the discovery of novel compounds. The method identified five promising candidates with key properties essential for photopharmacology applications.
The SETI Institute invites researchers to apply for a postdoctoral fellowship focused on developing advanced AI tools for exoplanet discovery. The successful candidate will work with Dr. Vishal Gajjar and his team to uncover subtle signals in massive datasets using machine learning and anomaly-detection techniques.
A recent study emphasizes the urgent need to address bias in generative AI systems, which can distort outcomes and erode public trust. The research suggests that developing and deploying ethical, explainable AI is crucial to ensure fairness and transparency in critical decision-making areas.
Researchers used AI to find adalimumab as potential treatment for iMCD, a rare disease with poor survival rate and few treatment options. The treatment has shown promising results in saving the life of a patient who was on hospice care.
Yann LeCun, NYU's Courant Institute of Mathematical Sciences professor, has been selected as a winner of the 2025 Queen Elizabeth Prize for Engineering for his groundbreaking research on artificial neural networks. His work enabled machines to process and learn from vast amounts of data in ways previously unimaginable.
Researchers found that a machine learning predictive model leveraging EHR data outperformed screening surveys in identifying patients with health-related social needs. The models demonstrated biases, particularly towards White, non-Hispanic patients.
Researchers developed a machine learning tool that screens for comorbid depression and anxiety disorders using acoustic voice signals. The study confirmed that a one-minute verbal fluency test can reliably identify subjects with comorbid AD/MDD, who tend to use simpler words and exhibit reduced variability in phonemic word length.
Dental implant surgeries require optimal mechanical stress levels for successful bone healing and long-term implant success. Researchers are developing a hybrid biomechanical model using machine learning to provide precise, patient-specific predictions of mechanical stress.
A team of MIT engineers has developed a training method for multiagent systems that can guarantee their safe operation in crowded environments. The method enables agents to continually map their safety margins, allowing them to scale up to any number of agents while maintaining system safety.
Researchers developed SPACIER, an open-source software that integrates machine learning with molecular simulations to design high-performance optical polymers. The tool surpassed the empirical limits of refractive index and Abbe number in a proof-of-concept study, demonstrating its practical potential.
A new machine learning approach developed by UCR researchers enables the detection of environmental patterns in large datasets generated by LIGO, improving data quality and reducing noise. This technology has potential applications in particle accelerator experiments and complex industrial systems.
Dr. Girish N. Nadkarni, a pioneering physician-scientist, has been named Chair of the Windreich Department of Artificial Intelligence and Human Health and Director of the Hasso Plattner Institute for Digital Health at Mount Sinai. He will lead efforts to advance AI research, education, and clinical translation.
Researchers developed ProtET, an AI model leveraging multi-modal learning to controllably edit proteins through text-based instructions. This approach enhances functional protein design across domains like enzyme activity, stability, and antibody binding, promising real-world applicability in biomedical research.
Researchers found that training AI agents in a less noisy environment can lead to better performance than traditional methods. The indoor training effect suggests that constructing simulated environments with specific noise levels can improve AI learning.
Researchers developed MUNIS, a deep learning tool that predicts CD8+ T cell epitopes with high accuracy, potentially accelerating vaccine development. The tool was validated using experimental data from influenza, HIV, and EBV, demonstrating its potential to streamline vaccine design.
The open-source AI model analyzes medical images, generates detailed reports, and answers clinical questions to streamline diagnostics and improve accuracy. BiomedGPT aims to democratize healthcare and reduce disparities amongst patients by providing easily accessible data to bolster underserved hospitals.
A collaborative study investigates how advanced AI tools can make it easier to evaluate interventions for ageing, providing personalised recommendations. The researchers identified eight critical requirements for effective AI-based evaluations and found that following specific guidelines improved the quality of the recommendations.
Vision-Language Models (VLMs) inherit biases from uncurated datasets, leading to poor group robustness and biased predictions. Researchers are exploring strategies to mitigate these biases in discriminative models, but generative tasks like image captioning and image generation require attention.
Neuromorphic computing is poised to emerge into full-scale commercial use, driven by the need for energy-efficient solutions. The review article proposes strategies for building large-scale neuromorphic systems that can tackle complex real-world challenges.
Researchers designed nano-architected materials with exceptional strength-to-weight and stiffness-to-weight ratios, overcoming stress concentration issues. Machine learning optimized geometries led to over double the strength of existing designs.
A recent study has employed machine learning algorithms to improve the accuracy of flood season rainfall predictions. The findings show that combining climate system numerical models with ML-based correction methods results in substantial improvements, increasing prediction scores by up to 7.87%.
Researchers applied machine learning methods to analyze large datasets of individual cells, achieving better results than classical learning methods. Self-supervised learning improved performance, especially in applications with large single-cell data sets.
A new hybrid machine learning model predicts ultimate axial strength of CFRP-strengthened CFST columns with high accuracy, enabling safer and more efficient designs. The model can be used to optimize construction processes and enhance the safety of structures at a lower cost.
A groundbreaking AI model developed by researchers at Emory University accurately predicts the likelihood of blood transfusion in non-traumatic ICU patients, addressing longstanding challenges in predicting transfusion needs. The model achieved exceptional performance metrics, including an AUROC of 0.97 and an accuracy rate of 0.93.
Researchers developed a machine learning model using hyperspectral imaging to assess pre-harvest tomato quality, predicting key parameters like weight, firmness, and lycopene content. The approach enables real-time monitoring of fruit development, improving crop quality and reducing waste.
BioChatter bridges the gap between large language models and biomedical research by providing a transparent and adaptable framework for custom research tasks. The platform can integrate with knowledge graphs and bioinformatics tools, making it easier for researchers to analyze complex datasets.
The new tool will enhance the DLA's supply chain management capabilities, reduce operational disruptions, and bolster readiness. Quantum Research Sciences' technology will provide predictive capabilities and automate obsolescence management processes.
A new study assesses the historical knowledge of AI chatbots like ChatGPT-4 and finds they struggle with nuanced, PhD-level inquiry. The models performed best on legal systems and social complexity but struggled with topics such as discrimination and social mobility.
An English literature graduate has developed a new method for large language models to understand and analyze short text chunks, such as those on social media profiles. The method successfully grouped nearly 40,000 Twitter user biographies from accounts tweeting about US President Donald Trump into 10 categories.
Professors Scott Acton and Mathews Jacob of UVA's Charles L. Brown Department of Electrical and Computer Engineering were named to the IEEE Signal Processing Society's 2025 Class of Distinguished Lecturers for their groundbreaking work in signal processing, artificial intelligence, and medical imaging.
Researchers at University of Birmingham have discovered three new protein biomarkers TFF3, LCN2, and CEACAM5 that show strong predictive potential for colorectal cancer. These biomarkers are linked to cell adhesion and inflammation, processes closely associated with cancer development.
Zhu has made groundbreaking contributions to earthquake monitoring using deep-learning models like PhaseNet, DeepDenoiser, and GaMMA. His work has led to breakthroughs in seismic phase picking, denoising, and phase association.
A brain-computer interface has enabled a person with tetraplegia to control a virtual quadcopter by thinking about moving their unresponsive fingers. This technology provides unprecedented control, allowing the user to maneuver through a virtual obstacle course and potentially enabling remote work and social interactions.
Researchers at NIMS developed a next-generation AI device leveraging ion-controlled spin wave interference in magnetic materials, outperforming conventional devices by up to 10 times. The technology enables energy-efficient computations with minimal degradation when miniaturized, opening doors for various industrial applications.
Researchers introduced a novel approach to enhance reservoir computing, incorporating a generalized readout that offers improved accuracy and robustness compared to conventional methods. The new method uses a nonlinear combination of reservoir variables to uncover deeper patterns in input data.
The study utilizes infrared spectroscopy and a machine-learned protocol to map spectroscopic fingerprints to atomistic structures. The authors demonstrate the accuracy of their network in predicting local atomistic structures and energetic variations, enabling the tracking of dynamic C–C coupling on Cu surfaces.
Researchers at TU Graz are developing a self-learning AI system to position individual molecules quickly and autonomously, enabling the construction of highly complex molecular structures. The goal is to build logic circuits in the nanometre range using quantum corrals made from complex-shaped molecules.
A new study by researchers at the University of Minnesota found that the benefits of corn-soybean crop rotation are extremely sensitive to climate change. The study suggests that increasing crop rotation can improve overall yields and highlight its potential as a climate adaptation strategy in the US Midwest.
A new study uses machine learning models to identify women experiencing severe subjective cognitive decline during the menopause transition, associated with aging, hypertension, obesity, and depression. This predictive model allows for early intervention to protect cognitive health, a novel guidance for interventions designed to preser...
Researchers have found evidence of songbirds forming social connections and potentially exchanging information about their migration routes through vocalizations. The study suggests that social cues play a significant role in shaping migration behaviors, particularly for young birds learning from observing other birds.
A new automated job hazard analysis tool promises to significantly reduce workplace accidents and improve safety in the construction industry. The University of South Australia's research team has built a 'knowledge graph' to predict hazards, which can be analysed in real-time to identify potential risks and control measures.
Temperate savannas in eastern China have been mapped for the first time, revealing their geographical distribution and extent. The research provides precise information on the spatial characteristics of these ecosystems, supporting conservation and utilization efforts.
DNNs have an inbuilt 'Occam's razor,' favouring simpler solutions that fit training data. This bias helps them generalize well on simple patterns but may struggle with complex data, aligning with real-world data characteristics.
Researchers developed an AI model to detect brain cancer spread in surrounding tissue using MRI scans, showing 85-per-cent accuracy. This non-surgical method offers insights into patients' cancer without aggressive surgery, potentially improving treatment and survival.
Researchers developed a machine learning model to identify defective products in semi-solid die casting by analyzing injection pressure. The model achieved high accuracy and revealed mechanisms behind defect formation, providing a foundation for optimizing manufacturing processes.
The BiliSG app uses machine learning to analyze skin color and predict bilirubin levels in newborns, offering a convenient alternative to traditional testing methods. With 100% sensitivity, the app has shown promising results in monitoring neonatal jaundice and reducing the risk of brain damage.
A new AI system analyzed electronic health records of long-COVID patients to identify four sub-populations with specific needs, including those with asthma or mental health conditions. The study found that these sub-populations require more specialized care and pointed toward updated profiles for hospitals to better address their needs.
TabPFN learns causal relationships from synthetic data, making correct predictions more likely than existing algorithms. The model requires fewer resources and data, making it ideal for small companies and teams.
Researchers at the University of New Hampshire developed an AI-powered algorithm to categorize over 706 million aurora images from NASA's THEMIS data set. This labeled database can help scientists better understand and forecast geomagnetic storms that disrupt vital communications and security infrastructure.
A pioneering new mathematical model developed by Oxford researchers could help assess the risks posed by AI and protect people's privacy. The method provides a robust scientific framework for evaluating identification techniques, including browser fingerprinting.
A series of small earthquakes in Surrey in 2018 and 2019 may have been triggered by oil extraction from a nearby well, according to a new study. The research used mathematical modeling to predict the frequency of earthquakes based on oil extraction timing and volume, finding a rough match with observed seismic activity.
Researchers developed an AI-based method to analyze CEO depression from vocal acoustic features in conference calls. The study found that CEOs with higher levels of depression tend to receive larger compensation packages and are more responsive to negative feedback.
A new AI model developed by researchers at Penn State College of Medicine can predict the progression of autoimmune disease among those with preclinical symptoms up to 1,000% more accurately. The GPS model integrates data from large genetic studies and electronic health records to identify individuals at high risk of disease progression.
Positive Phase 1 trial results suggest ISM5411's gut-restrictive property and favorable pharmacokinetic profile, validating its potential for treating inflammatory bowel disease. Insilico Medicine expects to initiate a Phase 2 proof-of-concept study in active ulcerative colitis patients.
A new method called Annotatability helps identify mismatches in cell annotations and better characterizes biological data structures. This approach enables more precise downstream analysis of biological signals, capturing cellular communities associated with target signals.
A team of researchers at the Indian Institute of Science (IISc) has developed a machine learning-based approach to predict material properties using limited data. By leveraging transfer learning and multi-property pre-training, they were able to improve model performance and extend its applicability to new materials.
A team of researchers developed a machine learning framework to streamline the discovery of high-performance ionic thermoelectric materials. The approach predicted Seebeck coefficients with high accuracy and identified critical molecular descriptors influencing material performance.
A new study uses machine learning to reduce time needed for calculating screening parameters in Koopmans functionals, enabling faster predictions of material spectral properties. Researchers trained a simple model using modest data and achieved accurate results, paving the way for studying temperature-dependent spectral properties.
Researchers developed a novel AI method using Disentangled Variational Autoencoder (D-VAE) for inverse materials design, making the process data-efficient and interpretable. The method was tested on high-entropy alloys, producing clear results that highlight influencing material features.
Current energy-hungry transformer-based systems contrast with Turing's idea of machines that develop intelligence naturally, like human children. AI systems can now perform tasks exclusive to human intellect, such as generating coherent text and discussing abstract ideas, but with limitations on sustainability and societal impact