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Dipeptides

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Specific chemiluminescent protocol for dual-site recognition of Streptococcus mutans utilizing strong affinity between teicoplanin and Gram-positive bacteria.

Talanta 29310243
A novel dual-site recognition protocol was developed for chemiluminescent (CL) detection of Streptococcus mutans (S. mutans) based on a designed antibiotic-affinity strategy. Teicoplanin, a broad-spectrum antibiotic against Gram-positive bacteria, wa...
Animals Anti-Bacterial Agents Bacterial Proteins Cell Wall Dipeptides Fresh Water Horseradish Peroxidase Humans Hydrogen Peroxide Immunoglobulin G Iodobenzenes Limit of Detection Luminescent Measurements Luminol Magnetite Nanoparticles Milk Protein Binding Rats Saliva Streptococcus mutans Teicoplanin
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MATEPRED-A-SVM-Based Prediction Method for Multidrug And Toxin Extrusion (MATE) Proteins.

Computational biology and chemistry 26256800
The growth and spread of drug resistance in bacteria have been well established in both mankind and beasts and thus is a serious public health concern. Due to the increasing problem of drug resistance, control of infectious diseases like diarrhea, pn...
Amino Acids Dipeptides Drug Resistance Hydrophobic and Hydrophilic Interactions Organic Cation Transport Proteins Position-Specific Scoring Matrices Reproducibility of Results Support Vector Machine
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Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC.

Computer methods and programs in biomedicine 26233307
Heat Shock Proteins (HSPs) are the substantial ingredients for cell growth and viability, which are found in all living organisms. HSPs manage the process of folding and unfolding of proteins, the quality of newly synthesized proteins and protecting ...
Algorithms Amino Acid Sequence Dipeptides Heat-Shock Proteins HSP70 Heat-Shock Proteins Machine Learning Molecular Sequence Data Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity Sequence Alignment Sequence Analysis, Protein
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RAFP-Pred: Robust Prediction of Antifreeze Proteins Using Localized Analysis of n-Peptide Compositions.

IEEE/ACM transactions on computational biology and bioinformatics 28113406
In extreme cold weather, living organisms produce Antifreeze Proteins (AFPs) to counter the otherwise lethal intracellular formation of ice. Structures and sequences of various AFPs exhibit a high degree of heterogeneity, consequently the prediction ...
Algorithms Antifreeze Proteins Computational Biology Databases, Protein Dipeptides Machine Learning ROC Curve Sequence Analysis, Protein
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In silico identification of milk antihypertensive di- and tripeptides involved in angiotensin I-converting enzyme inhibitory activity.

Nutrition research (New York, N.Y.) 29173648
Identification of bioactive milk peptides could improve food technology through improved selection of food supplements with a focus on antihypertensive properties. We hypothesized that angiotensin I-converting enzyme (ACE) inhibitory activities of mi...
Angiotensin-Converting Enzyme Inhibitors Animals Antihypertensive Agents Binding Sites Catalytic Domain Cattle Computational Biology Dietary Supplements Dipeptides Expert Systems Humans Hydrogen Bonding Hydrophobic and Hydrophilic Interactions Milk Proteins Models, Molecular Molecular Docking Simulation Oligopeptides Peptide Fragments Peptidyl-Dipeptidase A Protein Conformation Proteolysis Quantitative Structure-Activity Relationship
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Accurate prediction of Gram-negative bacterial secreted protein types by fusing multiple statistical features from PSI-BLAST profile.

SAR and QSAR in environmental research 29688029
Gram-negative bacterial secreted proteins play different roles in invaded eukaryotic cells and cause various diseases. Prediction of Gram-negative bacterial secreted protein types is a meaningful and challenging task. In this paper, we develop a mult...
Bacterial Proteins Dipeptides Gram-Negative Bacteria Models, Statistical Structure-Activity Relationship Support Vector Machine
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Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design.

The Journal of chemical physics 30134681
Auto-associative neural networks ("autoencoders") present a powerful nonlinear dimensionality reduction technique to mine data-driven collective variables from molecular simulation trajectories. This technique furnishes explicit and differentiable ex...
Dipeptides Molecular Dynamics Simulation Neural Networks, Computer Peptides
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