AIMC Topic: Animals

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Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage.

Cell genomics
Deep learning models have advanced our ability to predict cell-type-specific chromatin patterns from transcription factor (TF) binding motifs, but their application to perturbed contexts remains limited. We applied transfer learning to predict how co...

Structural Similarity, Activity, and Toxicity of Mycotoxins: Combining Insights from Unsupervised and Supervised Machine Learning Algorithms.

Journal of agricultural and food chemistry
A large number of mycotoxins and related fungal metabolites have not been assessed in terms of their toxicological impacts. Current methodologies often prioritize specific target families, neglecting the complexity and presence of co-occurring compou...

Design and implementation of an independent-drive bionic dragonfly robot.

Bioinspiration & biomimetics
Bionic flapping wing robots achieve flight by imitating animal flapping wings, which are safe, flexible, and efficient. Their practicality and human-machine symbiosis in narrow and complex environments are better than traditional fixed-wing or multir...

Prediction and validation of anoikis-related genes in neuropathic pain using machine learning.

PloS one
BACKGROUND: Neuropathic pain (NP) can be induced by a variety of clinical conditions, such as spinal cord injury, lumbar disc herniation (LDH), lumbar spinal stenosis, diabetes, herpes zoster, and spinal cord tumors, and inflammatory stimuli. The pat...

Enhancing beef tallow flavor through enzymatic hydrolysis: Unveiling key aroma precursors and volatile compounds using machine learning.

Food chemistry
Lipids are critical precursors of aroma compounds in beef tallow. This study investigated how enzymatic hydrolysis treatment affected the aroma precursors and flavor of beef tallow during the manufacturing process. Using gas chromatography-mass spect...

AI/ML modeling to enhance the capability of in vitro and in vivo tests in predicting human carcinogenicity.

Mutation research. Genetic toxicology and environmental mutagenesis
This study aimed to develop an in silico model for predicting human carcinogenicity using advanced deep learning techniques, specifically Graph Neural Networks (GNN), through a multitask learning (MTL) approach. The MTL framework leveraged auxiliary ...

Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics.

Communications biology
In eukaryotic cells, DNA replication is organised both spatially and temporally, as evidenced by the stage-specific spatial distribution of replication foci in the nucleus. Despite the genetic association of aberrant DNA replication with numerous hum...

Early detection of feline chronic kidney disease via 3-hydroxykynurenine and machine learning.

Scientific reports
Feline chronic kidney disease (CKD) is one of the most frequently encountered diseases in veterinary practice, and the leading cause of mortality in cats over five years of age. While diagnosing advanced CKD is straightforward, current routine tests ...

A springtail-inspired multimodal walking-jumping microrobot.

Science robotics
Although legged robots have demonstrated effective mobility in some natural settings, as robot size decreases, obstacles in their environment become challenging to overcome. Small arthropods scale obstacles many times their size through jumps powered...

Multi-omics analyses and machine learning prediction of oviductal responses in the presence of gametes and embryos.

eLife
The oviduct is the site of fertilization and preimplantation embryo development in mammals. Evidence suggests that gametes alter oviductal gene expression. To delineate the adaptive interactions between the oviduct and gamete/embryo, we performed a m...