Research Keyword: artificial neural networks

XenoBug: machine learning-based tool to predict pollutant-degrading enzymes from environmental metagenomes

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XenoBug is a new artificial intelligence tool that helps scientists find bacteria and their enzymes that can break down harmful pollutants like pesticides, plastics, and petroleum products. The tool analyzes genetic information from environmental samples to predict which enzymes can degrade specific toxic chemicals. This discovery approach could make environmental cleanup faster and cheaper by identifying the right microbes for the job. Researchers can use XenoBug to get starting points for developing new biological cleanup solutions.

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Ultrasound-assisted extraction of neuroprotective antioxidants from Ganoderma lucidum

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This research studied how to best extract healing compounds from the reishi mushroom (Ganoderma lucidum), an important traditional Chinese medicine. Using advanced extraction techniques and computer modeling, scientists identified optimal conditions that doubled the amount of beneficial antioxidants obtained compared to conventional methods. They then tested these extracts on nerve cells in the laboratory, demonstrating that the extracts effectively protected cells from oxidative damage and injury.

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Multilayer perceptron-genetic algorithm as a promising tool for modeling cultivation substrate of Auricularia cornea Native to Iran

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Black ear mushrooms (Auricularia cornea) are nutritious and medicinal fungi that can be grown on waste materials from wood industries. Researchers tested different combinations of sawdust and bran to find the best growing mixture. They used artificial intelligence to predict which combinations would give the best yields, finding that a mix of 70% beech sawdust with 30% wheat bran worked best and could be produced efficiently.

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Artificial Neural Network Prediction of Mechanical Properties in Mycelium-Based Biocomposites

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Scientists developed an artificial intelligence model that can predict how strong and durable mushroom-based composite materials will be. These composites are made by growing mushroom mycelium (fungal threads) through wood particles and other plant materials, creating an eco-friendly alternative to synthetic materials. The AI model learns from physical measurements and can accurately predict mechanical properties, potentially reducing the need for extensive testing and helping design better sustainable materials.

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Artificial intelligence-assisted optimization of extraction enhances the biological activity of Phylloporia ribis

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Scientists optimized how to extract beneficial compounds from Phylloporia ribis mushrooms using artificial intelligence, finding that an AI-assisted method produced extracts with stronger antioxidant power and cancer-fighting properties than traditional statistical approaches. The optimized extracts showed promise in fighting free radicals, potentially supporting brain health against Alzheimer’s disease, and slowing cancer cell growth. This research demonstrates how combining mushroom extraction with modern AI technology could lead to more effective natural medicines.

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