A leakage-controlled multimodal machine learning study of 251 adolescents shows task-evoked fMRI features during emotional response inhibition provide the strongest discriminative signal for ...
A new study shows that randomly splitting EEG windows between training and test sets inflates diagnostic accuracy by roughly ...
A predictive model utilizing serum metabolic profiles was able to distinguish ovarian cancer from control samples with 93% accuracy, according to a new study. Machine learning–based classification ...
Making a personalized T cell therapy for cancer patients currently takes at least six months; scientists at the German Cancer Research Center (DKFZ) and the University Medical Center Mannheim have ...
The onset of psychosis can be predicted before it occurs, using a machine-learning tool which can classify MRI brain scans into those who are healthy and those at risk of a psychotic episode. An ...
Assessing Algorithmic Fairness With a Multimodal Artificial Intelligence Model in Men of African and Non-African Origin on NRG Oncology Prostate Cancer Phase III Trials Recent advances in machine ...
The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...
AI, machine learning, and deep learning. You see these terms repeatedly in introductory materials, yet you might stumble when ...
Making a personalized T cell therapy for cancer patients currently takes at least six months. Scientists have shown that the laborious first step of identifying tumor-reactive T cell receptors for ...
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