AIMC Topic: Animals

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Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts, and clinical tumors using deep learning.

Science advances
Cell lines and patient-derived xenografts are essential to cancer research; however, the results derived from such models often lack clinical translatability, as they do not fully recapitulate the complex cancer biology. Identifying preclinical model...

Improving quantification accuracy of a nuclear Overhauser enhancement signal at -1.6 ppm at 4.7 T using a machine learning approach.

Physics in medicine and biology
A new nuclear Overhauser enhancement (NOE)-mediated saturation transfer MRI signal at -1.6 ppm, potentially from choline phospholipids and termed NOE(-1.6), has been reported in biological tissues at high magnetic fields. This signal shows promise fo...

Artificial intelligence and machine learning in veterinary medicine: a regulatory perspective on current initiatives and future prospects.

American journal of veterinary research
The US FDA's Center for Veterinary Medicine (CVM) is advancing its leadership in veterinary science by integrating AI and machine learning (ML) into its regulatory framework and scientific initiatives. This paper explores the CVM's strategic approach...

Exploring a new paradigm for serum-accessible component rules of natural medicines using machine learning and development and validation of a direct predictive model.

International journal of pharmaceutics
In the field of pharmaceutical research, Lipinski's Rule of Five (RO5) was once widely regarded as the prevailing standard for the development of novel drugs. Despite the fact that an increasing number of recently approved drugs no longer adhere to t...

Random noise promotes slow heterogeneous synaptic dynamics important for robust working memory computation.

Proceedings of the National Academy of Sciences of the United States of America
Recurrent neural networks (RNNs) based on model neurons that communicate via continuous signals have been widely used to study how cortical neural circuits perform cognitive tasks. Training such networks to perform tasks that require information main...

AI-designed miniproteins neutralize snake toxins.

Science (New York, N.Y.)
Scientists are turning to AI to make antivenoms cheaper, faster, and more effective.

Deep learning and generative artificial intelligence in aging research and healthy longevity medicine.

Aging
With the global population aging at an unprecedented rate, there is a need to extend healthy productive life span. This review examines how Deep Learning (DL) and Generative Artificial Intelligence (GenAI) are used in biomarker discovery, deep aging ...

Mitochondrial segmentation and function prediction in live-cell images with deep learning.

Nature communications
Mitochondrial morphology and function are intrinsically linked, indicating the opportunity to predict functions by analyzing morphological features in live-cell imaging. Herein, we introduce MoDL, a deep learning algorithm for mitochondrial image seg...

Simulating fish autonomous swimming behaviours using deep reinforcement learning based on Kolmogorov-Arnold Networks.

Bioinspiration & biomimetics
The study of fish swimming behaviours and locomotion mechanisms holds significant scientific and engineering value. With the rapid advancements in artificial intelligence, a new method combining deep reinforcement learning (DRL) with computational fl...

Robust estimation of skin physiological parameters from hyperspectral images using Bayesian neural networks.

Journal of biomedical optics
SIGNIFICANCE: Machine learning models for the direct extraction of tissue parameters from hyperspectral images have been extensively researched recently, as they represent a faster alternative to the well-known iterative methods such as inverse Monte...