AIMC Topic: Animals

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Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape.

Nature communications
New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which ar...

The application of artificial intelligence to accelerate G protein-coupled receptor drug discovery.

British journal of pharmacology
The application of artificial intelligence (AI) approaches to drug discovery for G protein-coupled receptors (GPCRs) is a rapidly expanding area. Artificial intelligence can be used at multiple stages during the drug discovery process, from aiding ou...

TSML: A New Pig Behavior Recognition Method Based on Two-Stream Mutual Learning Network.

Sensors (Basel, Switzerland)
Changes in pig behavior are crucial information in the livestock breeding process, and automatic pig behavior recognition is a vital method for improving pig welfare. However, most methods for pig behavior recognition rely on human observation and de...

Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii.

Nature chemical biology
Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug resistance. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches. Fortunately, machine learning ...

Advancing Computational Toxicology by Interpretable Machine Learning.

Environmental science & technology
Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants ...

Deep Learning-Based Image Analysis of Liver Steatosis in Mouse Models.

The American journal of pathology
The incidence of nonalcoholic fatty liver disease is a continuously growing health problem worldwide, along with obesity. Therefore, novel methods to both efficiently study the manifestation of nonalcoholic fatty liver disease and to analyze drug eff...

Stimulation-mediated reverse engineering of silent neural networks.

Journal of neurophysiology
Reconstructing connectivity of neuronal networks from single-cell activity is essential to understanding brain function, but the challenge of deciphering connections from populations of silent neurons has been largely unmet. We demonstrate a protocol...

Echo2Pheno: a deep-learning application to uncover echocardiographic phenotypes in conscious mice.

Mammalian genome : official journal of the International Mammalian Genome Society
Echocardiography, a rapid and cost-effective imaging technique, assesses cardiac function and structure. Despite its popularity in cardiovascular medicine and clinical research, image-derived phenotypic measurements are manually performed, requiring ...

Prospective Validation of Machine Learning Algorithms for Absorption, Distribution, Metabolism, and Excretion Prediction: An Industrial Perspective.

Journal of chemical information and modeling
Absorption, distribution, metabolism, and excretion (ADME), which collectively define the concentration profile of a drug at the site of action, are of critical importance to the success of a drug candidate. Recent advances in machine learning algori...

Classification of breast tumors by using a novel approach based on deep learning methods and feature selection.

Breast cancer research and treatment
PURPOSE: Cancer is one of the most insidious diseases that the most important factor in overcoming the cancer is early diagnosis and detection. The histo-pathological images are used to determine whether the tissue is cancerous and the type of cancer...