AIMC Topic: Peptides

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Exploring novel ANGICon-EIPs through ameliorated peptidomics techniques: Can deep learning strategies as a core breakthrough in peptide structure and function prediction?

Food research international (Ottawa, Ont.) Oct 27, 2023
Dairy-derived angiotensin-I-converting enzyme inhibitory peptides (ANGICon-EIPs) have been regarded as a relatively safe supplementary diet-therapy strategy for individuals with hypertension, and short-chain peptides may have more relevant antihypert...
Amino Acid Sequence Deep Learning Proteins Humans Neural Networks, Computer Peptides
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Identification of Water-Soluble Polymers through Machine Learning of Fluorescence Signals from Multiple Peptide Sensors.

ACS applied bio materials Oct 27, 2023
Recently, there has been growing concern about the discharge of water-soluble polymers (especially synthetic polymers) into the environment. Therefore, the identification of water-soluble polymers in water samples is becoming increasingly crucial. In...
Machine Learning Water Polymers Peptides Fluorescence
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Deep Learning Empowers the Discovery of Self-Assembling Peptides with Over 10 Trillion Sequences.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Sep 25, 2023
Self-assembling of peptides is essential for a variety of biological and medical applications. However, it is challenging to investigate the self-assembling properties of peptides within the complete sequence space due to the enormous sequence quanti...
Oligopeptides Peptides Deep Learning
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Empowering peptidomics: utilizing computational tools and approaches.

Bioanalysis Sep 22, 2023
Bioinformatics plays a critical role in the advancement of peptidomics by providing powerful tools for data analysis, interpretation and integration. Peptidomics is concerned with the study of peptides, short chains of amino acids with diverse biolog...
Artificial Intelligence Power, Psychological Amino Acid Sequence Peptides Computational Biology
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Prediction of Cholecystokinin-Secretory Peptides Using Bidirectional Long Short-term Memory Model Based on Transfer Learning and Hierarchical Attention Network Mechanism.

Biomolecules Sep 11, 2023
Cholecystokinin (CCK) can make the human body feel full and has neurotrophic and anti-inflammatory effects. It is beneficial in treating obesity, Parkinson's disease, pancreatic cancer, and cholangiocarcinoma. Traditional biological experiments are c...
Cholecystokinin Receptors, Cholecystokinin Machine Learning Peptides Humans
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DeepTPpred: A Deep Learning Approach With Matrix Factorization for Predicting Therapeutic Peptides by Integrating Length Information.

IEEE journal of biomedical and health informatics Sep 6, 2023
The abuse of traditional antibiotics has led to increased resistance of bacteria and viruses. Efficient therapeutic peptide prediction is critical for peptide drug discovery. However, most of the existing methods only make effective predictions for o...
Humans Deep Learning Peptides Drug Discovery Neural Networks, Computer
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An Effective Plant Small Secretory Peptide Recognition Model Based on Feature Correction Strategy.

Journal of chemical information and modeling Aug 29, 2023
Plant small secretory peptides (SSPs) play an important role in the regulation of biological processes in plants. Accurately predicting SSPs enables efficient exploration of their functions. Traditional experimental verification methods are very reli...
Biological Transport Machine Learning Research Design Electric Power Supplies Peptides
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Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides.

Molecular diversity Aug 26, 2023
Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this p...
Deep Learning Antiviral Agents Computational Biology Viruses Humans Animals Machine Learning Antimicrobial Peptides Peptides
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Improved prediction of protein-protein interactions by a modified strategy using three conventional docking software in combination.

International journal of biological macromolecules Aug 24, 2023
Proteins play a crucial role in many biological processes, where their interaction with other proteins are integral. Abnormal protein-protein interactions (PPIs) have been linked to various diseases including cancer, and thus targeting PPIs holds pro...
Neoplasms Humans Peptides Software Proteins Artificial Intelligence Molecular Docking Simulation Protein Binding
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AggBERT: Best in Class Prediction of Hexapeptide Amyloidogenesis with a Semi-Supervised ProtBERT Model.

Journal of chemical information and modeling Aug 8, 2023
The prediction of peptide amyloidogenesis is a challenging problem in the field of protein folding. Large language models, such as the ProtBERT model, have recently emerged as powerful tools in analyzing protein sequences for applications, such as pr...
Supervised Machine Learning Amino Acid Sequence Heuristics Amyloid Machine Learning Peptides
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