Machine learning model improves accuracy of liquid biopsy results

A machine learning model developed by researchers at the Johns Hopkins Kimmel Cancer Center filters out the biological noise ...
News-Medical.Net

Machine learning model filters out the biological noise in liquid biopsy samples

A machine learning model developed by researchers at the Johns Hopkins Kimmel Cancer Center filters out the biological noise in liquid biopsy samples, helping clinicians better match therapies to ...
The Lancet

Effective sample size for individual risk predictions: quantifying uncertainty in machine learning models

Copyright: © 2025 The Author(s). Published by Elsevier Ltd. Individual prediction uncertainty is a key aspect of clinical prediction model performance; however ...
ahajournals.org

Abstract 4361575: Phenotyping Cardiogenic Shock Using Unsupervised Machine Learning: A Nationwide Cluster Analysis from National Inpatient Sample

Introduction: Cardiogenic shock (CS) is a heterogeneous clinical syndrome, with varied clinical outcomes driven by hemodynamic states, and initial presentation. However, unsupervised machine learning ...
TheServerSide

Sample Questions for AWS' Machine Learning Associate Certification

Community driven content discussing all aspects of software development from DevOps to design patterns. The AWS Machine Learning Associate certification validates your ability to configure, build, and ...
TheServerSide

Google Machine Learning Certification Sample Questions

Community driven content discussing all aspects of software development from DevOps to design patterns. The Google Cloud Professional Machine Learning Engineer certification validates your ability to ...
Frontiers

Machine learning-based single-sample molecular classifier for cancer grading

Tumor subtyping based on morphological grade is used in cancer treatment and management decision-making and to determine a patient’s prognosis. While low- and high-grade tumors are predictive of ...
Scientific Research Publishing

Huang, A. A., & Huang, S. Y. (2023). Increasing Transparency in Machine Learning through Bootstrap Simulation and Shapely Additive Explanations. PLOS ONE, 18, e0281922.

ABSTRACT: Machine learning (ML) has revolutionized risk management by enabling organizations to make data-driven decisions with higher accuracy and speed. However, as machine learning models grow more ...
C&EN

Integrating Machine Learning-Based Pose Sampling with Established Scoring Functions for Virtual Screening

Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria Christian Doppler Laboratory ...
C&EN

Efficient Sampling for Machine Learning Electron Density and Its Response in Real Space

Hefei National Research Center for Physical Sciences at the Microscale, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China Key Laboratory of ...
GitHub

Machine-Learning Assisted Canonical Sampling (MLACS)

This package provides a set of tools to perform various simulations bringing together machine-learning methods with quantum simulations of atomic systems. Here are some features implemented in the ...