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Published in 2022 at "Journal of Chemical Information and Modeling"
DOI: 10.1021/acs.jcim.2c00522
Abstract: Thirty-eight percent of protein structures in the Protein Data Bank contain at least one metal ion. However, not all these metal sites are biologically relevant. Cations present as impurities during sample preparation or in the…
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Keywords:
network;
physiological adventitious;
learning identify;
metal ... See more keywords
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Published in 2023 at "Genome Biology and Evolution"
DOI: 10.1093/gbe/evad084
Abstract: Abstract Interpreting protein function from sequence data is a fundamental goal of bioinformatics. However, our current understanding of protein diversity is bottlenecked by the fact that most proteins have only been functionally validated in model…
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Keywords:
unsupervised deep;
learning identify;
learning;
deep learning ... See more keywords
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Published in 2020 at "Circulation"
DOI: 10.1161/circ.142.suppl_3.13102
Abstract: Background: Atrial fibrillation (AF) is associated with stroke, especially when AF goes undetected. Deep neural networks (DNN) can predict incident AF from a 12-lead resting ECG. We hypothesize tha...
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Keywords:
13102 prediction;
abstract 13102;
learning identify;
incident deep ... See more keywords
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Published in 2022 at "Frontiers in Aging Neuroscience"
DOI: 10.3389/fnagi.2022.962319
Abstract: Objective Progressive conditions characterized by cognitive decline, including mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are clinical conditions representing a major risk factor to develop dementia, however, the diagnosis of these pre-dementia conditions…
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Keywords:
use machine;
learning identify;
machine learning;
neuropsychological tests ... See more keywords