AIMC Topic: Peptide Fragments
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AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CAC) predicts heart failure and outperforms NT-proBNP: The multi-ethnic study of Atherosclerosis.
Journal of cardiovascular computed tomography
Apr 24, 2024
INTRODUCTION: Coronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC™...
United States
Predictive Value of Tests
Coronary Angiography
Peptide Fragments
Risk Assessment
Incidence
Time Factors
Multidetector Computed Tomography
Vascular Calcification
Asymptomatic Diseases
Computed Tomography Angiography
Radiographic Image Interpretation, Computer-Assisted
Coronary Artery Disease
Heart Failure
Natriuretic Peptide, Brain
Female
Reproducibility of Results
Risk Factors
Aged
Aged, 80 and over
Male
Middle Aged
Humans
Artificial Intelligence
Biomarkers
Prognosis
Artificial intelligence universal biomarker prediction tool.
Journal of thrombosis and thrombolysis
Dec 20, 2023
Through experiencing cardiopulmonary arrest, an artificial intelligence universal biomarker prediction tool was developed to help patients understand improvement in the trends of their disease. PyPI tool handles two biomarkers, hbA1c for diabetes and...
Deep learning-based NT-proBNP prediction from the ECG for risk assessment in the community.
Clinical chemistry and laboratory medicine
Nov 20, 2023
OBJECTIVES: The biomarker N-terminal pro B-type natriuretic peptide (NT-proBNP) has predictive value for identifying individuals at risk for cardiovascular disease (CVD). However, it is not widely used for screening in the general population, potenti...
Aggregation-induced electrochemiluminescence enhancement of Ag-MOG for amyloid β 42 sensing.
Analytica chimica acta
Oct 10, 2023
This study aimed to introduce an immunosensor for measuring amyloid β 42 (Aβ) levels by aggregation-induced enhanced electrochemiluminescence (ECL). Metal-organic gels (MOGs) are novel soft materials with advantages such as high gel stability, good l...
Interpretable Machine Learning of Amino Acid Patterns in Proteins: A Statistical Ensemble Approach.
Journal of chemical theory and computation
Aug 8, 2023
Explainable and interpretable unsupervised machine learning helps one to understand the underlying structure of data. We introduce an ensemble analysis of machine learning models to consolidate their interpretation. Its application shows that restric...
A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer's dementia: China Aging and Neurodegenerative Initiative (CANDI) study.
Alzheimer's & dementia : the journal of the Alzheimer's Association
Jun 6, 2022
INTRODUCTION: To test the utility of the "A/T/N" system in the Chinese population, we study core Alzheimer's disease (AD) biomarkers in a newly established Chinese cohort.
Deep-Learning-Assisted Stratification of Amyloid Beta Mutants Using Drying Droplet Patterns.
Advanced materials (Deerfield Beach, Fla.)
May 4, 2022
The development of simple and accurate methods to predict mutations in proteins remains an unsolved challenge in modern biochemistry. It is discovered that critical information about primary and secondary peptide structures can be inferred from the s...
Single-molecule fluorescence imaging and deep learning reveal highly heterogeneous aggregation of amyloid-β 42.
Proceedings of the National Academy of Sciences of the United States of America
Mar 15, 2022
Polymorphism in the structure of amyloid fibrils suggests the existence of many different assembly pathways. Characterization of this heterogeneity is the key to understanding the aggregation mechanism and toxicity, but in practice it is extremely di...
DeepLC can predict retention times for peptides that carry as-yet unseen modifications.
Nature methods
Oct 28, 2021
The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex liquid chromatography-mass spectrometry identification workflows. However, due to the way peptides are encoded in current prediction mod...
iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features.
International journal of molecular sciences
Aug 19, 2021
Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identify...
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