AI Medical Compendium Topic:
Protein Binding

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[Immunochromatographic Test System for the Detection of T-2 Toxin].

Prikladnaia biokhimiia i mikrobiologiia Jan 1, 2015
An immunochromatographic test system was developed for the detection of T-2 toxin (T2T), which is one of priority contaminants of cereals. The detection is based on the competition between T2T in the sample and the T2T-protein conjugate immobilized o...
Mice Reagent Strips Methanol Food Contamination Protein Binding Animals Edible Grain Gold Antibodies, Monoclonal Chromatography, Affinity Fusarium Immunoconjugates Metal Nanoparticles Limit of Detection Binding, Competitive Zea mays T-2 Toxin
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The quantitative prediction of HLA-B*2705 peptide binding affinities using Support Vector Regression to gain insights into its role for the Spondyloarthropathies.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jan 1, 2015
Computational methods are increasingly utilised in many immunoinformatics problems such as the prediction of binding affinity of peptides. The peptides could provide valuable insight into the drug design and development such as vaccines. Moreover, th...
Humans Spondylarthropathies Peptides Support Vector Machine Protein Binding Alleles Computational Biology HLA-B27 Antigen
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Predicting Protein-Protein Interaction Sites with a Novel Membership Based Fuzzy SVM Classifier.

IEEE/ACM transactions on computational biology and bioinformatics Jan 1, 2015
Predicting residues that participate in protein-protein interactions (PPI) helps to identify, which amino acids are located at the interface. In this paper, we show that the performance of the classical support vector machine (SVM) algorithm can furt...
Models, Chemical Amino Acid Sequence Pattern Recognition, Automated Fuzzy Logic Humans Molecular Sequence Data Computer Simulation Binding Sites Models, Statistical Algorithms Protein Binding Protein Interaction Mapping Sequence Alignment Sequence Analysis, Protein
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Analysis of Drug Design for a Selection of G Protein-Coupled Neuro- Receptors Using Neural Network Techniques.

Current computer-aided drug design Jan 1, 2015
A study is presented on how well possible drug-molecules can be predicted with respect to their function and binding to a selection of neuro-receptors by the use of artificial neural networks. The ligands investigated in this study are chosen to be c...
Humans Drug Design Protein Binding Neural Networks, Computer Receptor, Serotonin, 5-HT2B Receptors, Opioid, mu Ligands Receptor, Metabotropic Glutamate 5
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Prediction of protein-protein interaction sites from weakly homologous template structures using meta-threading and machine learning.

Journal of molecular recognition : JMR Jan 1, 2015
The identification of protein-protein interactions is vital for understanding protein function, elucidating interaction mechanisms, and for practical applications in drug discovery. With the exponentially growing protein sequence data, fully automate...
Binding Sites Models, Molecular Hydrogen Bonding Software Algorithms Protein Binding Evolution, Molecular Machine Learning Proteins
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Constructing query-driven dynamic machine learning model with application to protein-ligand binding sites prediction.

IEEE transactions on nanobioscience Jan 1, 2015
We are facing an era with annotated biological data rapidly and continuously generated. How to effectively incorporate new annotated data into the learning step is crucial for enhancing the performance of a bioinformatics prediction model. Although m...
Binding Sites Proteins Databases, Protein Machine Learning Nucleotides Ligands Protein Binding Models, Biological
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Some remarks on prediction of protein-protein interaction with machine learning.

Medicinal chemistry (Shariqah (United Arab Emirates)) Jan 1, 2015
Protein-protein interactions (PPIs) play a key role in many cellular processes. Uncovering the PPIs and their function within the cell is a challenge of post-genomic biology and will improve our understanding of disease and help in the development of...
Algorithms Protein Binding Proteins Machine Learning High-Throughput Screening Assays
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Ligand biological activity predictions using fingerprint-based artificial neural networks (FANN-QSAR).

Methods in molecular biology (Clifton, N.J.) Jan 1, 2015
This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to t...
Algorithms Protein Binding Computational Biology Neural Networks, Computer Receptors, Cannabinoid Databases, Chemical Quantitative Structure-Activity Relationship Cannabinoids Models, Molecular Ligands
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