AIMC Topic: Animals

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Identification of hub biomarkers in coronary artery disease patients using machine learning and bioinformatic analyses.

Scientific reports
Understanding the molecular underpinnings of CAD is essential for developing effective therapeutic strategies. This study aims to identify and analyze differentially expressed hub biomarkers in the peripheral blood of CAD patients. Based on RNA-seq d...

Deep transfer learning approach for automated cell death classification reveals novel ferroptosis-inducing agents in subsets of B-ALL.

Cell death & disease
Ferroptosis is a recently described type of regulated necrotic cell death whose induction has anti-cancer therapeutic potential, especially in hematological malignancies. However, efforts to uncover novel ferroptosis-inducing therapeutics have been l...

Poisson random measure noise-induced coherence in epidemiological priors informed deep neural networks to identify the intensity of virus dynamics.

Scientific reports
Differential equations-based epidemiological compartmental systems and deep neural networks-based artificial intelligence can effectively analyze and combat monkeypox (MPV) transmission with Poisson random measure noise into a stochastic SEIQR (susce...

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents.

Journal of visualized experiments : JoVE
Predatory pursuit behavior involves a series of important physiological processes, such as locomotion, learning, and decision-making that are critical to the success of an animal in capturing prey. However, there are few methods and systems for study...

Quantifying Area Back Scatter of Marine Organisms in the Arctic Ocean by Machine Learning-Based Post-Processing of Volume Back Scatter.

Sensors (Basel, Switzerland)
As the sea ice reduces in both extent and thickness and the Arctic Ocean opens, there is substantial interest in mapping the marine ecosystem in this remote and until now largely inaccessible ocean. We used the during surveys in the Central Arctic O...

Integrating bioinformatics and machine learning to identify glomerular injury genes and predict drug targets in diabetic nephropathy.

Scientific reports
Diabetes mellitus (DM) is a chronic metabolic disorder that poses significant challenges to public health. Among its various complications, diabetic nephropathy (DN) emerges as a critical microvascular complication associated with high mortality rate...

AI-Assisted Hypothesis Generation to Address Challenges in Cardiotoxicity Research: Simulation Study Using ChatGPT With GPT-4o.

Journal of medical Internet research
BACKGROUND: Cardiotoxicity is a major concern in heart disease research because it can lead to severe cardiac damage, including heart failure and arrhythmias.

Prediction of plasma concentration-time profiles in mice using deep neural network integrated with pharmacokinetic models.

International journal of pharmaceutics
Quantitative structure-activity relationship (QSAR) methods have emerged as powerful tools to streamline non-clinical pharmacokinetic (PK) studies, with extensive evidence demonstrating their potential to predict key in vivo PK parameters such as cle...

Using explainable machine learning to predict the irritation and corrosivity of chemicals on eyes and skin.

Toxicology letters
Contact with specific chemicals often results in corrosive and irritative responses in the eyes and skin, playing a pivotal role in assessing the potential hazards of personal care products, cosmetics, and industrial chemicals to human health. While ...

Quality prediction of seabream Sparus aurata by deep learning algorithms and explainable artificial intelligence.

Food chemistry
In this study, Convolutional Neural Network (CNN), DenseNet121, Inception V3 and ResNet50 machine learning algorithms were used to determine the quality changes in sea bream stored in refrigerator conditions using eye and gill images. The sea bream w...