A new algorithm could drive breakthroughs in understanding cancer, Alzheimer's disease and other potentially fatal conditions ...
The HeMonitor study evaluated the feasibility and accuracy of non-invasive hemoglobin (Hb) assessment using image-based techniques and machine learning in patients with hematologic malignancies. A ...
Machine learning models narrow down solutions in synthesis, compounding, product design, and more.
Researchers mapped 442,239 single nuclei from nonfailing human hearts to chart how cardiac cells change from fetal ...
BACKGROUND: Hypertension induces structural and functional damage in multiple organs. Evidence of subclinical damage ...
Abstract: Feature selection is a technique commonly used in Data Mining and Machine Learning. Traditional feature selection methods, when applied to large datasets, generate a large number of feature ...
Detection of large vessel occlusions using a deep learning (DL) algorithm for the anterior circulation has shown promising results. However, the role of DL algorithms in detecting posterior ...
Abstract: Anomaly detection has been an important research topic in data mining and machine learning. Many real-world applications such as intrusion or credit card fraud detection require an effective ...
🧬 𝐓𝐇𝐄 𝐀𝐈 𝐄𝐍𝐂𝐘𝐂𝐋𝐎𝐏𝐄𝐃𝐈𝐀 — 𝐃𝐀𝐓𝐀 𝐒𝐂𝐈𝐄𝐍𝐂𝐄 🧭 Data Science is an ...
Los Alamos researchers developed PAS, a real-time tool that helps detect false image claims in machine vision models.
Prion and prion-like proteins are classically associated with protein misfolding, but amyloidogenic sequences can also participate in host defence. Here, using deep learning, we screened 19.3 million ...
Positron emission tomography (PET) brain imaging is a pivotal tool in neuroscience, enabling precise visualization and ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results