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 ...
A Comparison of Logistic Regression Against Machine Learning Algorithms for Gastric Cancer Risk Prediction Within Real-World Clinical Data Streams We performed a six-phase process including: training ...
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 ...
• Repurposed COVID-19 RATs provide an ideal platform for observing differences in blood coagulability. • Random Forest image classification algorithms can facilitate rapid coagulation status ...
The Francis College of Engineering, Department of Electrical and Computer Engineering, invites you to attend a Doctoral Dissertation Proposal defense by Masoumeh Farhadi Nia on: "Machine Learning for ...
Two studies published in 2022 demonstrate machine learning techniques employed to reduce uncertainty in atomic force microscopy (AFM). Making AFM more accurate with artificial intelligence (AI) could ...
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