AIMC Topic: Neural Networks, Computer

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Leveraging readily available clinical data with machine learning to predict first-line immunotherapy outcomes in non-small cell lung cancer.

International immunopharmacology
BACKGROUND: Immune checkpoint inhibitors (ICIs) are essential first-line treatments for recurrent or metastatic non-small cell lung cancer (NSCLC). However, predicting their effectiveness and the occurrence of immunotherapy-related adverse events (ir...

Augmenting MACCS Keys with Persistent Homology Fingerprints for Protein-Ligand Binding Classification.

Journal of chemical information and modeling
Machine learning has become an essential tool in computational drug design, enabling models to uncover patterns in molecular data and predict protein-ligand interactions. This study introduces a novel approach by integrating persistence images with M...

MMF-MCP: A Deep Transfer Learning Model Based on Multimodal Information Fusion for Molecular Feature Extraction and Carcinogenicity Prediction.

Journal of chemical information and modeling
Molecular carcinogenicity is a crucial factor in the development of cancer, and accurate prediction of it is vital for cancer prevention, treatment, and drug development. In recent years, deep learning has been applied to predict molecular carcinogen...

Transfer learning enables robust prediction of cellular toxicity from environmental micro- and nanoplastics.

Journal of hazardous materials
Micro- and nanoplastics (MNPs) are emerging pollutants that accumulate in ecosystems, food chains, and the human body, raising concerns about human health risks. However, understanding their toxicity remains challenging due to limited experimental da...

DEEP Q-NAS: A new algorithm based on neural architecture search and reinforcement learning for brain tumor identification from MRI.

Computers in biology and medicine
A significant obstacle in brain tumor treatment planning is determining the tumor's actual size. Magnetic resonance imaging (MRI) is one of the first-line brain tumor diagnosis. It takes a lot of effort and mostly depends on the operator's experience...

Explainable deep learning for stratified medicine in inflammatory bowel disease.

Genome biology
Moving from a one-size-fits-all to an individual approach in precision medicine requires a deeper understanding of disease molecular mechanisms. Especially in heterogeneous complex diseases such as inflammatory bowel disease (IBD), better molecular s...

Wave Propagation Phenomena in Nonlinear Hierarchical Neural Networks with Predictive Coding Feedback Dynamics.

Bulletin of mathematical biology
We propose a mathematical framework to systematically explore the propagation properties of a class of continuous in time nonlinear neural network models comprising a hierarchy of processing areas, mutually connected according to the principles of pr...

Optimizing potato yield predictions in Uttar Pradesh, India: a comparative analysis of machine learning models.

Scientific reports
Potato as a staple food, plays a crucial role in ensuring a sustainable food supply and mitigating poverty and malnutrition in various regions across the globe. India, specifically holding the second position in global potato production, plays a sign...

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging.

Nature communications
The process of protein aggregation, central to neurodegenerative diseases like Huntington's, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in d...

Automatic porcine diarrhea viruses classification using pathological images and hybrid semantic neural network.

Computers in biology and medicine
Porcine epidemic diarrhea is a highly contagious intestinal disease in pigs, caused by various strains of porcine epidemic diarrhea virus (PEDV). The infection rate in suckling piglets can reach 100%. Manual analysis of pathological images is the pri...