AIMC Topic: Animals

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Characterization of Machine Learning-Based Surrogate Models of Neural Activation Under Electrical Stimulation.

Bioelectromagnetics
Electrical stimulation of peripheral nerves via implanted electrodes has been shown to be a promising approach to restore sensation, movement, and autonomic functions across a wide range of illnesses and injuries. While in principle computational mod...

Unveiling Long Non-coding RNA Networks from Single-Cell Omics Data Through Artificial Intelligence.

Methods in molecular biology (Clifton, N.J.)
Single-cell omics technologies have revolutionized the study of long non-coding RNAs (lncRNAs), offering unprecedented resolution in elucidating their expression dynamics, cell-type specificity, and associated gene regulatory networks (GRNs). Concurr...

Deep learning can detect elbow disease in dogs screened for elbow dysplasia.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Medical image analysis based on deep learning is a rapidly advancing field in veterinary diagnostics. The aim of this retrospective diagnostic accuracy study was to develop and assess a convolutional neural network (CNN, EfficientNet) to evaluate elb...

TIRESIA and TISBE: Explainable Artificial Intelligence Based Web Platforms for the Transparent Assessment of the Developmental Toxicity of Chemicals and Drugs.

Methods in molecular biology (Clifton, N.J.)
Developmental toxicity is key human health endpoint, especially relevant for safeguarding maternal and child well-being. It is an object of increasing attention from international regulatory bodies such as the US EPA (US Environmental Protection Agen...

Machine Learning in Early Prediction of Metabolism of Drugs.

Methods in molecular biology (Clifton, N.J.)
Machine learning (ML) has increasingly been applied to predict properties of drugs. Particularly, metabolism can be predicted with ML methods, which can be exploited during drug discovery and development. The prediction of metabolism is a crucial bot...

Development of machine learning-based quantitative structure-activity relationship models for predicting plasma half-lives of drugs in six common food animal species.

Toxicological sciences : an official journal of the Society of Toxicology
Plasma half-life is a crucial pharmacokinetic parameter for estimating extralabel withdrawal intervals of drugs to ensure the safety of food products derived from animals. This study focuses on developing a quantitative structure-activity relationshi...

Exploring the Mechanisms of Sanguinarine in the Treatment of Osteoporosis by Integrating Network Pharmacology Analysis and Deep Learning Technology.

Current computer-aided drug design
BACKGROUND: Sanguinarine (SAN) has been reported to have antioxidant, antiinflammatory, and antimicrobial activities with potential for the treatment of osteoporosis (OP).

Integrating single-cell multimodal epigenomic data using 1D convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Recent experimental developments enable single-cell multimodal epigenomic profiling, which measures multiple histone modifications and chromatin accessibility within the same cell. Such parallel measurements provide exciting new opportuni...

[Design and research of a pneumatic soft intestine robot imitating the inchworm].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
In order to seek a patient friendly and low-cost intestinal examination method, a structurally simple pneumatic soft intestinal robot inspired by inchworms is designed and manufactured. The intestinal robot was consisted of two radially expanding cyl...

[Prediction of areas of snail spread in Anhui Province based on five machine learning models].

Zhongguo xue xi chong bing fang zhi za zhi = Chinese journal of schistosomiasis control
OBJECTIVE: To predict the areas of snail spread in Anhui Province from 1977 to 2023 using machine learning models, and to compare the effectiveness of different machine learning models for prediction of areas of snail spread, so as to provide insig...