Results 71 to 80 of about 57,481 (265)
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Exosomes are emerging as powerful biomarkers for disease diagnosis and monitoring. This review highlights the integration of surface‐enhanced Raman spectroscopy with artificial intelligence to enhance molecular fingerprinting of exosomes. Machine learning and deep learning techniques improve spectral interpretation, enabling accurate classification of ...
Munevver Akdeniz +2 more
wiley +1 more source
Advanced Experiment Design Strategies for Drug Development
Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang +3 more
wiley +1 more source
Recommendations for clinical and molecular identification of LS, surgical and endoscopic management of LS‐associated colorectal cancer and preventive measures for cancer were produced. The emphasis was on surgical and gastroenterological aspects of the cancer spectrum.
T. T. Seppälä +18 more
wiley +1 more source
Majority‐Voting Overlapping Method for Error Correction in DNA Data Storage
We propose an overlapping‐based majority‐voting method for DNA data storage error correction. By aligning multiple reads and choosing the most frequent base per position, it suppresses substitution errors without prior models. Validated on synthetic and real sequencing data, it achieves high‐fidelity, scalable, and cost‐effective reconstruction ...
Thi Bich Ngoc Nguyen +5 more
wiley +1 more source
Quantitative phase maps of single cells recorded in flow cytometry modality feed a hierarchical architecture of machine learning models for the label‐free identification of subtypes of ovarian cancer. The employment of a priori clinical information improves the classification performance, thus emulating the clinical application of liquid biopsy during ...
Daniele Pirone +11 more
wiley +1 more source
A 3D holotomography system coupled with a deep learning model distinguishes how cells die—apoptosis, necroptosis or necrosis—without any fluorescent labels. Training on refractive index maps of HeLa cells yields 97% accuracy and flags necroptosis hours before chemical dyes.
Minwook Kim +8 more
wiley +1 more source
This study introduces a biomarker‐agnostic diagnostic strategy for ovarian cancer, utilizing a machine learning‐enhanced electronic nose to analyze volatile organic compound signatures from blood plasma. By overcoming the dependence on specific biomarkers, this approach enables accurate detection, staging, and cancer type differentiation, offering a ...
Ivan Shtepliuk +4 more
wiley +1 more source
Motivated by the need for more flexible decision-making mechanisms in the European Union, the paper proposes a simple but novel voting scheme for binary decisions taken by committees that meet regularly over time. At each meeting, committee members are allowed to store their vote for future use; the decision is then taken according to the majority of ...
openaire +4 more sources
Context‐Aware Semiautonomous Control for Upper‐Limb Prostheses
A semiautonomous prosthetic control strategy integrates electromyographic‐based intention with computer vision‐driven grasp adaptation and wrist orientation. Comparative experiments with functional tasks evaluate performance, usability, and cognitive workload.
Gianmarco Cirelli +7 more
wiley +1 more source

