AIMC Topic: Deep Learning

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The Application of Anisotropically Collapsing Gels, Deep Learning, and Optical Microscopy for Chemical Characterization of Nanoparticles and Nanoplastics.

Langmuir : the ACS journal of surfaces and colloids
The surface chemistry of nanomaterials, particularly the density of functional groups, governs their behavior in applications such as bioanalysis, bioimaging, and environmental impact studies. Here, we report a precise method to quantify carboxyl gro...

Predicting Protein Function in the AI and Big Data Era.

Biochemistry
It is an exciting time for researchers working to link proteins to their functions. Most techniques for extracting functional information from genomic sequences were developed several years ago, with major progress driven by the availability of big d...

Multiplexing and Sensing with Fluorescence Lifetime Imaging Microscopy Empowered by Phasor U-Net.

Analytical chemistry
Fluorescence lifetime imaging microscopy (FLIM) has been widely used as an essential multiplexing and sensing tool in frontier fields such as materials science and life sciences. However, the accuracy of lifetime estimation is compromised by limited ...

NMR Pure Shift Spectroscopy and Its Potential Applications in the Pharmaceutical Industry.

Chembiochem : a European journal of chemical biology
H nuclear magnetic resonance (NMR) spectroscopy plays an important role in the pharmaceutical industry, but for complex substances, spectral analysis is challenging due to the narrow chemical shift range and signal splitting caused by scalar coupling...

Deep learning reveals pathology-confirmed neuroimaging signatures in Alzheimer's, vascular and Lewy body dementias.

Brain : a journal of neurology
Concurrent neurodegenerative and vascular pathologies pose a diagnostic challenge in the clinical setting, with histopathology remaining the definitive modality for dementia-type diagnosis. To address this clinical challenge, we introduce a neuropath...

Robust Uncertainty-Informed Glaucoma Classification Under Data Shift.

Translational vision science & technology
PURPOSE: Standard deep learning (DL) models often suffer significant performance degradation on out-of-distribution (OOD) data, where test data differs from training data, a common challenge in medical imaging due to real-world variations.

A deep learning-based method for predicting the frequency classes of drug side effects based on multi-source similarity fusion.

Bioinformatics (Oxford, England)
MOTIVATION: Drug side effects refer to harmful or adverse reactions that occur during drug use, unrelated to the therapeutic purpose. A core issue in drug side effect prediction is determining the frequency of these drug side effects in the populatio...

CrossAttOmics: multiomics data integration with cross-attention.

Bioinformatics (Oxford, England)
MOTIVATION: Advances in high throughput technologies enabled large access to various types of omics. Each omics provides a partial view of the underlying biological process. Integrating multiple omics layers would help have a more accurate diagnosis....

Near-term prediction of sustained ventricular arrhythmias applying artificial intelligence to single-lead ambulatory electrocardiogram.

European heart journal
BACKGROUND AND AIMS: Accurate near-term prediction of life-threatening ventricular arrhythmias would enable pre-emptive actions to prevent sudden cardiac arrest/death. A deep learning-enabled single-lead ambulatory electrocardiogram (ECG) may identif...

Multimodal CustOmics: A unified and interpretable multi-task deep learning framework for multimodal integrative data analysis in oncology.

PLoS computational biology
Characterizing cancer presents a delicate challenge as it involves deciphering complex biological interactions within the tumor's microenvironment. Clinical trials often provide histology images and molecular profiling of tumors, which can help under...