In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.
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PREDICTION AND PROGNOSIS OF BREAST CANCER BY MACHINE LEARNING Mohammad Sufian Badar1, Shazmeen Shamsi 2, Md Maksuf ul Haque3, Alain Abran4 1 Senior Teaching Faculty, Department of Bioengineering, University of California, Riverside, CA, USA (Corresponding author) [email protected] 2 M.Sc. Bioinformatics, Department of Computer Science, Jamia Millia Islamia, Jamia Nagar, New Delhi 10025 ...
Nov 09, 2020 · Using neighborhood and local data in combination with existing information sources creates a more accurate prediction on a patient's recovery prospects after an out-of-hospital cardiac arrest ...
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PURPOSE For patients with early-stage breast cancer, predicting the risk of metastatic relapse is of crucial importance. Existing predictive models rely on agnostic survival analysis statistical tools (eg, Cox regression). Here we define and evaluate the predictive ability of a mechanistic model for time to distant metastatic relapse. METHODS The data we used for our model consisted of 642 ...
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Feb 10, 2020 · Machine Learning Crash Course ... In this lesson, you'll debug a real-world ML problem* related to cancer prediction. Estimated Time: 5 minutes Learning Objectives.