AIMC Topic: Deep Learning

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Iterative improvement of deep learning models using synthetic regulatory genomics.

Genome research
Deep learning models can accurately reconstruct genome-wide epigenetic tracks from the reference genome sequence alone. But it is unclear what predictive power they have on sequence diverging from the reference, such as disease- and trait-associated ...

Deep Learning for Automated Measures of SUV and Molecular Tumor Volume in [Ga]PSMA-11 or [F]DCFPyL, [F]FDG, and [Lu]Lu-PSMA-617 Imaging with Global Threshold Regional Consensus Network.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Metastatic castration-resistant prostate cancer has a high rate of mortality with a limited number of effective treatments after hormone therapy. Radiopharmaceutical therapy with [Lu]Lu-prostate-specific membrane antigen-617 (LuPSMA) is one treatment...

Multiview state-of-health estimation for lithium-ion batteries using time-frequency image fusion and attention-based deep learning.

PloS one
Lithium-ion batteries are high-performance energy storage devices that have been widely used in a variety of applications. Accurate early-stage prediction of their remaining useful life is essential for preventing failures and mitigating safety risks...

Diagnosis of colorectal cancer using residual transformer with mixed attention and explainable AI.

PloS one
Colorectal cancer (CRC) is the leading cause of cancer disease and poses a significant threat to global health. Although deep learning models have been utilized to accurately diagnose CRC, they still face challenges in capturing the global correlatio...

Food defect detection technologies based on deep learning and prospects in detection of unsound wheat kernels.

Food chemistry
With rising concerns over global food security and quality pressures and the rapid advancement of agricultural intelligence, wheat quality detection demands higher efficiency, accuracy, and automation. Unsound wheat kernels, which adversely affect fl...

Patient-specific functional liver segments based on centerline classification of the hepatic and portal veins.

Computer assisted surgery (Abingdon, England)
PURPOSE: Couinaud's liver segment classification has been widely adopted for liver surgery planning, yet its rigid anatomical boundaries often fail to align precisely with individual patient anatomy. This study proposes a novel patient-specific liver...

Geometry-Driven Attention Model with 3D Molecular Features for Multi-Property Prediction of OLED Materials.

Journal of chemical information and modeling
As Organic Light-Emitting Diode (OLED) technology advances in applications such as high-end displays, medical devices, and VR/AR systems, the development of high-performance materials that improve energy efficiency and support environmental sustainab...

Learning Binding Affinities via Fine-Tuning of Protein and Ligand Language Models.

Journal of chemical information and modeling
Accurate in silico prediction of protein-ligand binding affinity is essential for efficient hit identification in large molecular libraries. Commonly used structure-based methods such as docking often fail to rank compounds effectively, and free ener...

AFPDeepPred: A Deep Learning Framework for Accurate Identification of Antifreeze Proteins.

Journal of chemical information and modeling
Antifreeze proteins (AFPs) are essential for the survival of organisms in subzero environments and have significant potential in biomedical and agricultural applications. However, their high sequence diversity poses a significant challenge for accura...

A robust deep learning framework for RNA 5-methyluridine modification prediction using integrated features.

BMC biology
BACKGROUND: The discovery of RNA 5-methyluridine (m5U) modifications is vital in computational biology due to their essential significance in different biological processes. This study presents a powerful predictor named 5-meth-Uri, which improves th...