AIMC Topic: Proteins

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Protein-Protein Interactions Prediction via Multimodal Deep Polynomial Network and Regularized Extreme Learning Machine.

IEEE journal of biomedical and health informatics Jun 12, 2018
Predicting the protein-protein interactions (PPIs) has played an important role in many applications. Hence, a novel computational method for PPIs prediction is highly desirable. PPIs endow with protein amino acid mutation rate and two physicochemica...
Humans Reproducibility of Results Deep Learning Proteins Databases, Protein Helicobacter pylori Protein Interaction Maps Computational Biology Protein Interaction Mapping Supervised Machine Learning Yeasts
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Predicting human protein function with multi-task deep neural networks.

PloS one Jun 11, 2018
Machine learning methods for protein function prediction are urgently needed, especially now that a substantial fraction of known sequences remains unannotated despite the extensive use of functional assignments based on sequence similarity. One majo...
Databases, Protein Machine Learning Humans Proteins Neural Networks, Computer
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Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes.

Biophysical chemistry Jun 7, 2018
The possibility of using the atomic coordinates of protein-ligand complexes to assess binding affinity has a beneficial impact in the early stages of drug development and design. From the computational view, the creation of reliable scoring functions...
Protein Binding Proteins Ligands Databases, Protein Machine Learning Thermodynamics Molecular Docking Simulation
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DeepText2GO: Improving large-scale protein function prediction with deep semantic text representation.

Methods (San Diego, Calif.) Jun 6, 2018
As of April 2018, UniProtKB has collected more than 115 million protein sequences. Less than 0.15% of these proteins, however, have been associated with experimental GO annotations. As such, the use of automatic protein function prediction (AFP) to r...
Animals Computational Biology Proteins Humans Machine Learning Gene Ontology Eukaryota Sequence Analysis, Protein Data Mining Semantics
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Consistent prediction of GO protein localization.

Scientific reports May 17, 2018
The GO-Cellular Component (GO-CC) ontology provides a controlled vocabulary for the consistent description of the subcellular compartments or macromolecular complexes where proteins may act. Current machine learning-based methods used for the automat...
Software Gene Ontology Animals Drosophila melanogaster Databases, Protein Arabidopsis Computational Biology Molecular Sequence Annotation Proteomics Solanum lycopersicum Proteins Saccharomyces cerevisiae
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Sequentially distant but structurally similar proteins exhibit fold specific patterns based on their biophysical properties.

Computational biology and chemistry May 16, 2018
The Three-dimensional structure of a protein depends on the interaction between their amino acid residues. These interactions are in turn influenced by various biophysical properties of the amino acids. There are several examples of proteins that sha...
Protein Conformation Protein Folding Support Vector Machine Models, Molecular Bayes Theorem Proteins
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Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques.

Environmental science and pollution research international May 16, 2018
Computational self-adapting methods (Support Vector Machines, SVM) are compared with an analytical method in effluent composition prediction of a two-stage anaerobic digestion (AD) process. Experimental data for the AD of poultry manure were used. Th...
Machine Learning Animals Waste Disposal, Fluid Proteins Poultry Manure Neural Networks, Computer Anaerobiosis Ammonia Nitrogen Biological Oxygen Demand Analysis
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CNNH_PSS: protein 8-class secondary structure prediction by convolutional neural network with highway.

BMC bioinformatics May 8, 2018
BACKGROUND: Protein secondary structure is the three dimensional form of local segments of proteins and its prediction is an important problem in protein tertiary structure prediction. Developing computational approaches for protein secondary structu...
Neural Networks, Computer Deep Learning Databases, Protein Protein Structure, Secondary Proteins
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RaptorX-Angle: real-value prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning.

BMC bioinformatics May 8, 2018
BACKGROUND: Protein dihedral angles provide a detailed description of protein local conformation. Predicted dihedral angles can be used to narrow down the conformational space of the whole polypeptide chain significantly, thus aiding protein tertiary...
Databases, Protein Amino Acids Entropy Protein Structure, Tertiary Costs and Cost Analysis Cluster Analysis Proteins Software Deep Learning
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ProLego: tool for extracting and visualizing topological modules in protein structures.

BMC bioinformatics May 4, 2018
BACKGROUND: In protein design, correct use of topology is among the initial and most critical feature. Meticulous selection of backbone topology aids in drastically reducing the structure search space. With ProLego, we present a server application to...
Neural Networks, Computer Proteins
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