New algorithm identifies disease-linked changes in cells without prior training

A new algorithm could drive breakthroughs in understanding cancer, Alzheimer's disease and other potentially fatal conditions ...
C&EN

Metal Oxide–Metal Organic Framework Layers for Discrimination of Multiple Gases Employing Machine Learning Algorithms

Nanotechnology Research Laboratory, Research School of Chemistry, College of Science, The Australian National University, Canberra, ACT 2601, Australia School of Engineering and Technology, The ...
Frontiers

Reproducible supervised learning-assisted classification of spontaneous synaptic waveforms with Eventer

Detection and analysis of spontaneous synaptic events is an extremely common task in many neuroscience research labs. Various algorithms and tools have been developed over the years to improve the ...
Nature

Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots

The design framework of our program is illustrated in Fig. 2a. An appropriate scheme was designed to pre-process the RHEED video data. Then, a ML model was selected based on the pre-processing results ...
Frontiers

A Machine Learning Approach for Detecting Vicarious Trial and Error Behaviors

Vicarious trial and error behaviors (VTEs) indicate periods of indecision during decision-making, and have been proposed as a behavioral marker of deliberation. In ...
Nature

Single Molecule Fluorescence Microscopy and Machine Learning for Rhesus D Antigen Classification

In transfusion medicine, the identification of the Rhesus D type is important to prevent anti-D immunisation in Rhesus D negative recipients. In particular, the detection of the very low expressed DEL ...