AIMC Topic: Proteins

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Statistical and machine learning approaches to predicting protein-ligand interactions.

Current opinion in structural biology Feb 20, 2018
Data driven computational approaches to predicting protein-ligand binding are currently achieving unprecedented levels of accuracy on held-out test datasets. Up until now, however, this has not led to corresponding breakthroughs in our ability to des...
Humans Proteins Ligands Quantitative Structure-Activity Relationship Protein Binding Computational Biology Models, Statistical Machine Learning Algorithms
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Exploring autophagy with Gene Ontology.

Autophagy Feb 17, 2018
Autophagy is a fundamental cellular process that is well conserved among eukaryotes. It is one of the strategies that cells use to catabolize substances in a controlled way. Autophagy is used for recycling cellular components, responding to cellular ...
Autophagy Databases, Genetic Humans Gene Ontology Molecular Sequence Annotation Parkinson Disease Animals Proteins
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ATPbind: Accurate Protein-ATP Binding Site Prediction by Combining Sequence-Profiling and Structure-Based Comparisons.

Journal of chemical information and modeling Feb 8, 2018
Protein-ATP interactions are ubiquitous in a wide variety of biological processes. Correctly locating ATP binding sites from protein information is an important but challenging task for protein function annotation and drug discovery. However, there i...
Support Vector Machine Solvents Binding Sites Protein Structure, Secondary Proteins Databases, Protein Adenosine Triphosphate
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Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction.

BMC bioinformatics Feb 6, 2018
BACKGROUND: The correct determination of protein-protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been ...
Binding Sites Protein Interaction Domains and Motifs Area Under Curve Amino Acids ROC Curve Support Vector Machine Proteins
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Boosted neural networks scoring functions for accurate ligand docking and ranking.

Journal of bioinformatics and computational biology Feb 4, 2018
Predicting the native poses of ligands correctly is one of the most important steps towards successful structure-based drug design. Binding affinities (BAs) estimated by traditional scoring functions (SFs) are typically used to score and rank-order p...
tRNA Methyltransferases Ligands Escherichia coli Proteins Drug Design Protein Binding Proteins Databases, Protein Structure-Activity Relationship Machine Learning Algorithms Molecular Docking Simulation Neural Networks, Computer
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Efficient computational model for classification of protein localization images using Extended Threshold Adjacency Statistics and Support Vector Machines.

Computer methods and programs in biomedicine Feb 2, 2018
BACKGROUND AND OBJECTIVE: Discriminative and informative feature extraction is the core requirement for accurate and efficient classification of protein subcellular localization images so that drug development could be more effective. The objective o...
Computer Simulation Reproducibility of Results Microscopy, Fluorescence Subcellular Fractions Computational Biology Support Vector Machine Proteins
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Feature expansion by a continuous restricted Boltzmann machine for near-infrared spectrometric calibration.

Analytica chimica acta Jan 31, 2018
A modified algorithm for training a restricted Boltzmann machine (RBM) has been devised and demonstrated for improving the results for partial least squares (PLS) calibration of wheat and meat by near-infrared (NIR) spectroscopy. In all cases, the PL...
Least-Squares Analysis Meat Calibration Machine Learning Spectroscopy, Near-Infrared Algorithms Proteins Fats
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K: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks.

Journal of chemical information and modeling Jan 29, 2018
Accurately predicting protein-ligand binding affinities is an important problem in computational chemistry since it can substantially accelerate drug discovery for virtual screening and lead optimization. We propose here a fast machine-learning appro...
Drug Discovery Deep Learning Protein Binding Computational Biology Proteins Databases, Protein Structure-Activity Relationship Ligands Models, Chemical
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Predicting the Effect of Single and Multiple Mutations on Protein Structural Stability.

Molecules (Basel, Switzerland) Jan 27, 2018
Predicting how a point mutation alters a protein's stability can guide pharmaceutical drug design initiatives which aim to counter the effects of serious diseases. Conducting mutagenesis studies in physical proteins can give insights about the effect...
Protein Conformation Amino Acid Substitution Thermodynamics Support Vector Machine Mutation Decision Trees Neural Networks, Computer Protein Stability Proteins Computer Simulation
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Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition.

Journal of chemical information and modeling Jan 10, 2018
Inspired by natural language processing techniques, we here introduce Mol2vec, which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Like the Word2vec models, where vectors of closely related w...
Proteins Protein Conformation Unsupervised Machine Learning Natural Language Processing Algorithms Molecular Structure Reproducibility of Results Models, Chemical Datasets as Topic
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