Results 251 to 260 of about 15,936,409 (281)

Using diverse potentials and scoring functions for the development of improved machine-learned models for protein–ligand affinity and docking pose prediction

Journal of Computer-Aided Molecular Design, 2021
The advent of computational drug discovery holds the promise of significantly reducing the effort of experimentalists, along with monetary cost. More generally, predicting the binding of small organic molecules to biological macromolecules has far-reaching implications for a range of problems, including metabolomics.
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Learning potential score as a predictor of sensitivity to cognitive intervention

Educational and Child Psychology, 2005
The goal of this study is to determine whether the learning potential score of underachieving students may serve as a better predictor of their sensitivity to cognitive intervention than the standard psychometric measures. Primary school immigrant students from Ethiopia were pre-tested at the beginning of the year using the dynamic version of Raven ...
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Modern scores for traditional tests - Review of the diagnostic potential of scores derived from word list learning tests in mild cognitive impairment and early Alzheimer's Disease

Neuropsychologia
Episodic memory impairment is one of the early hallmarks in Alzheimer's Disease. In the clinical diagnosis and research, episodic memory impairment is typically assessed using word lists that are repeatedly presented to and recalled by the participant across several trials.
Simona Schäfer, Johannes Tröger, Jutta Kray   +2 more
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Retrospective single-institution application of a deep learning–based radiomic score in metastatic NSCLC: Potential impact on first-line treatment decisions and outcomes.

Journal of Clinical Oncology
e20608 Background: Immune checkpoint inhibitors (ICIs) targeting the PD-1/PD-L1 axis are standard of care for metastatic non-small cell lung cancer (mNSCLC). Yet a minority of patients achieve durable benefit from ICI monotherapy (ICI MT), underscoring the limitations of current predictive ...
Nicholas Campbell Love, Karishma Sewaramani, Ross McCall, Taly Schmidt, Ryan Beasley, Arpan Patel   +5 more
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Label-Free, Quantum-Mechanically Informed Scoring of Protein–Ligand Complexes with Machine-Learned Interatomic Potentials

Virtual chemical libraries now exceed billions of compounds, placing joint demands on the scoring tools used to prioritize candidates for accuracy and scalability. Supervised affinity models meet scalability demands but remain vulnerable to dataset memorization and train-test leakage.
ilkwon cho, Hatice Gokcan, Olexandr Isayev   +2 more
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Exploring the Potential of Learning in Credit Scoring Models for Alternative Lending Platforms

European Economic Letters, 2023
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Automatic Scoring of Plots in Higher Data Science Education: Exploring the Potential and Challenges of Machine Learning in Scoring Student-generated Plots

This thesis explores the potential and challenges of using machine learning to score student-submitted plots in data science education, an area largely untouched in existing literature. As the number of students in data science courses grows, the demand for teacher assistant hours increases. This thesis investigates how machine learning could alleviate
Larsson, Albin, Wolf, Alex
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A multicentre validation study of the deep learning-based early warning score for predicting in-hospital cardiac arrest in patients admitted to general wards

Resuscitation, 2021
Joon-myoung Kwon, Yeon Joo Lee, Kyeongman Jeon   +2 more
exaly  

Prognostic Value of Machine‐Learning‐Based PRAISE Score for Ischemic and Bleeding Events in Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention

Journal of the American Heart Association, 2023
Boqun Shi, Haoyu Wang, Ke-Fei Dou
exaly  

A deep learning approach for sepsis monitoring via severity score estimation

Computer Methods and Programs in Biomedicine, 2021
Hasan Ogul, Tunc Asuroglu
exaly