AIMC Topic: Neural Networks, Computer

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Fluorescent Microneedle Sensors Based on Tb@Hydrogen-Bonded Organic Frameworks for Real-Time Food Freshness Monitoring.

Inorganic chemistry
As crucial biomarkers of food spoilage, portable and real-time monitoring of the biogenic amines (BAs) is essential to ensuring food safety. In light of this, a ratiometric fluorescent probe (Tb@HOF-BPTC) is developed, which exhibits distinct fluores...

Dual-Branch Contrastive Network with Deep Separable Convolution for Enhanced 6mA Site Identification.

Journal of chemical information and modeling
DNA N6-methyladenine (6mA) is a pivotal DNA modification integral to various biological processes, yet its exact regulatory role in eukaryotes is still unclear and controversial due to its sparsity, limitations in detection technologies, and complex ...

Characterizing DNA Origami Nanostructures in TEM Images Using Convolutional Neural Networks.

Journal of chemical information and modeling
Artificial intelligence (AI) models remain an emerging strategy to accelerate materials design and development. We demonstrate that CNN models can characterize DNA origami nanostructures employed in programmable self-assembly, which is important in m...

RTK_RAG: Leveraging Retrieval Augmented Generation with Multi-Window Convolutional Neural Networks for Superior ATP Binding Site Prediction in Receptor Tyrosine Kinases.

Journal of chemical information and modeling
Receptor tyrosine kinases (RTKs) are key regulators of cellular signaling and are frequently involved in cancer development. As their activation depends on ATP binding to the kinase domain, precisely identifying ATP binding sites is critical for mech...

Predicting Biological Activity from Biosynthetic Gene Clusters Using Neural Networks.

Journal of chemical information and modeling
Microorganisms such as bacteria and fungi have been used for natural products that translate to drugs. However, assessing the bioactivity of extract from culture to identify novel natural molecules remains a strenuous process due to the cumbersome or...

Data-Driven Optimization of Industrial Impact Polypropylene Characterization: Machine Learning Insights.

Journal of chemical information and modeling
The experimental determination of impact polypropylene (ICP) physical properties, such as tensile modulus, flexural modulus, and impact strength, is a time-sensitive process that can delay real-time decision making during industrial production. This ...

Enhancing Monte Carlo Tree Search for Retrosynthesis.

Journal of chemical information and modeling
Computer-Assisted Synthesis Programs are increasingly employed by organic chemists. Often, these tools combine neural networks for policy prediction with heuristic search algorithms. We propose two novel enhancements, which we call eUCT and dUCT, to ...

A hybrid predictor-corrector network and spatiotemporal classifier method for noisy plant PET image classification.

Physics in medicine and biology
. Plant Positron Emission Tomography (PET) is a new and efficient imaging technique which aims at providing a quantitative analysis of plant stress, enabling personalized crop management and maximizing productivity. However, a highly performant class...

Accelerating mechanistic model calibration in protein chromatography using artificial neural networks.

Journal of chromatography. A
In the manufacturing of therapeutic monoclonal antibodies (mAbs), mechanistic models can aid the evaluation and selection of suitable chromatography operating conditions during process development. However, model calibration remains a common bottlene...

Rapid diagnosis of lung cancer by multi-modal spectral data combined with deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Lung cancer is a malignant tumor that poses a serious threat to human health. Existing lung cancer diagnostic techniques face the challenges of high cost and slow diagnosis. Early and rapid diagnosis and treatment are essential to improve the outcome...