AIMC Topic: Deep Learning

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A preliminary attempt to harmonize using physics-constrained deep neural networks for multisite and multiscanner MRI datasets (PhyCHarm).

NeuroImage
In magnetic resonance imaging (MRI), variations in scan parameters and scanner specifications can result in differences in image appearance. To minimize these differences, harmonization in MRI has been suggested as a crucial image processing techniqu...

Deep learning-based diffusion MRI tractography: Integrating spatial and anatomical information.

NeuroImage
Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain. It plays a crucial role in neuroscience and clinical fields by facilitating the study of brain connectivity and neurological disorders....

A Meta-Learning Approach for Multicenter and Small-Data Single-Cell Image Analysis.

Analytical chemistry
The application of algorithm-based single-cell imaging techniques can visualize and analyze cellular heterogeneity. However, algorithm-based single-cell imaging techniques are severely limited by the high workload required to label single-cell images...

DGMM: A Deep Learning-Genetic Algorithm Framework for Efficient Lead Optimization in Drug Discovery.

Journal of chemical information and modeling
Lead optimization in drug discovery faces the dual challenge of maintaining structural diversity while preserving core molecular features and optimizing the balance between biological activity and drug-like properties. To address these challenges, we...

Representation of Molecules by Sequences of Instructions.

Journal of chemical information and modeling
The processing of chemical information by computational intelligence methods faces the challenge of the structural complexity of molecular graphs. These graphs are not amenable to being represented in a suitable way for such methods. The most popular...

SMILES Token Additivity Model with Interpretability and Generalizability for Fuel Property Predictions.

Journal of chemical information and modeling
Deep learning models for the quantitative structure-property relationship (QSPR) have traditionally encountered challenges related to limited interpretability and generalizability. In this study, we present the simplified molecular input line entry s...

Transfer Learning for Predicting ncRNA-Protein Interactions.

Journal of chemical information and modeling
Noncoding RNAs (ncRNAs) interact with proteins, playing a crucial role in regulating gene expression and cellular functions. Accurate prediction of these interactions is essential for understanding biological processes and developing novel therapeuti...

Dual-mode nanosensor for sensitive detection of methotrexate based on fluorescence technology and deep learning algorithms.

Analytica chimica acta
BACKGROUND: Methotrexate (MTX) in the body can result in severe, potentially life-threatening side effects. As a result, it is imperative to establish a dependable, precise, and specific method for detecting MTX. However, conventional sensors often s...

An Electrocardiogram Multi-Task Benchmark with Comprehensive Evaluations and Insightful Findings.

Studies in health technology and informatics
In the process of patient diagnosis, non-invasive measurements are widely used due to their low risks and quick results. Electrocardiogram (ECG), as a non-invasive method to collect heart activities, is used to diagnose cardiac conditions. Analyzing ...

Deep learning-driven hyperspectral imaging for real-time monitoring and growth modeling of psychrophilic spoilage bacteria in chilled beef.

International journal of food microbiology
Owing to the unsound cold chain system in China, chilled beef's quality would be affected by psychrophilic bacteria, resulting in quality deterioration and corruption, which leads to food safety problems. In this study, the growth of Pseudomonas and ...