AIMC Topic: Reproducibility of Results

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DeepScaffold: A Comprehensive Tool for Scaffold-Based De Novo Drug Discovery Using Deep Learning.

Journal of chemical information and modeling
The ultimate goal of drug design is to find novel compounds with desirable pharmacological properties. Designing molecules retaining particular scaffolds as their core structures is an efficient way to obtain potential drug candidates. We propose a s...

A deep learning approach for converting prompt gamma images to proton dose distributions: A Monte Carlo simulation study.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: In proton therapy, imaging prompt gamma (PG) rays has the potential to verify proton dose (PD) distribution. Despite the fact that there is a strong correlation between the gamma-ray emission and PD, they are still different in terms of the ...

Gait Biomarkers Classification by Combining Assembled Algorithms and Deep Learning: Results of a Local Study.

Computational and mathematical methods in medicine
Machine learning, one of the core disciplines of artificial intelligence, is an approach whose main emphasis is analytical model building. In other words, machine learning enables an automaton to make its own decisions based on a previous training pr...

Absolute quantitation of high abundant Fc-glycopeptides from human serum IgG-1.

Analytica chimica acta
Absolute quantitation of IgG-1 Fc-glycosylation, which is crucial for the clinical practice of glyco-biomarkers and quality control of biopharmaceuticals, has been hindered by the lack of glycopeptide standards. In this study, eleven high abundant Ig...

Original signal amplification assay for N-Terminal pro-brain natriuretic peptide detection based on BiMoO photosensitive matrix.

Analytica chimica acta
An original dual signal amplification immunoassay for N-Terminal pro-brain natriuretic peptide (NT-proBNP) detection is developed based on Au NPs and Zn doped CdS nanoparticles (ZnCdS) co-sensitized BiMoO nanosheet photoelectrochemical (PEC) platform...

Multiple Compounds Recognition from The Tandem Mass Spectral Data Using Convolutional Neural Network.

Molecules (Basel, Switzerland)
Mixtures analysis can provide more information than individual components. It is important to detect the different compounds in the real complex samples. However, mixtures are often disturbed by impurities and noise to influence the accuracy. Purific...

On Modelling Label Uncertainty in Deep Neural Networks: Automatic Estimation of Intra- Observer Variability in 2D Echocardiography Quality Assessment.

IEEE transactions on medical imaging
Uncertainty of labels in clinical data resulting from intra-observer variability can have direct impact on the reliability of assessments made by deep neural networks. In this paper, we propose a method for modelling such uncertainty in the context o...

Machine learning based quantification of ejection and filling parameters by fully automated dynamic measurement of left ventricular volumes from cardiac magnetic resonance images.

Magnetic resonance imaging
BACKGROUND: Although analysis of cardiac magnetic resonance (CMR) images provides accurate and reproducible measurements of left ventricular (LV) volumes, these measurements are usually not performed throughout the cardiac cycle because of lack of to...

SVR-EEMD: An Improved EEMD Method Based on Support Vector Regression Extension in PPG Signal Denoising.

Computational and mathematical methods in medicine
Photoplethysmography (PPG) has been widely used in noninvasive blood volume and blood flow detection since its first appearance. However, its noninvasiveness also makes the PPG signals vulnerable to noise interference and thus exhibits nonlinear and ...