AIMC Topic: Proteins

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Machine-learning-guided directed evolution for protein engineering.

Nature methods Jul 15, 2019
Protein engineering through machine-learning-guided directed evolution enables the optimization of protein functions. Machine-learning approaches predict how sequence maps to function in a data-driven manner without requiring a detailed model of the ...
Protein Engineering Humans Machine Learning Models, Biological Algorithms Directed Molecular Evolution Proteins
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Protein Function Prediction: From Traditional Classifier to Deep Learning.

Proteomics Jul 11, 2019
Deep learning demonstrates greater competence over traditional machine learning techniques for many tasks. In last several years, deep learning has been applied to protein function prediction and a series of good achievements has been obtained. These...
Computational Biology Proteins Amino Acid Sequence Machine Learning Deep Learning
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AMC-Net: Asymmetric and multi-scale convolutional neural network for multi-label HPA classification.

Computer methods and programs in biomedicine Jul 9, 2019
BACKGROUND AND OBJECTIVES: The multi-label Human Protein Atlas (HPA) classification can yield a better understanding of human diseases and help doctors to enhance the automatic analysis of biomedical images. The existing automatic protein recognition...
Databases, Protein Microscopy, Fluorescence Microscopy Probability Microtubules Pattern Recognition, Automated Reproducibility of Results Image Processing, Computer-Assisted Proteins Algorithms Phenotype Humans Neural Networks, Computer Cell Nucleus False Positive Reactions
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DM-RPIs: Predicting ncRNA-protein interactions using stacked ensembling strategy.

Computational biology and chemistry Jul 6, 2019
ncRNA-protein interactions (ncRPIs) play an important role in a number of cellular processes, such as post-transcriptional modification, transcriptional regulation, disease progression and development. Since experimental methods are expensive and tim...
Proteins RNA, Untranslated Neural Networks, Computer Machine Learning Support Vector Machine
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Automated feature engineering improves prediction of protein-protein interactions.

Amino acids Jul 5, 2019
Over the last decade, various machine learning (ML) and statistical approaches for protein-protein interaction (PPI) predictions have been developed to help annotating functional interactions among proteins, essential for our system-level understandi...
Protein Interaction Mapping Software Algorithms Computational Biology Support Vector Machine Proteins Humans Machine Learning
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Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models.

BMC bioinformatics Jul 2, 2019
BACKGROUND: In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance bet...
Machine Learning Phenotype Databases, Factual Humans Fanconi Anemia Genomics Signal Transduction Proteins
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Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins.

Neural computation Jul 1, 2019
A restricted Boltzmann machine (RBM) is an unsupervised machine learning bipartite graphical model that jointly learns a probability distribution over data and extracts their relevant statistical features. RBMs were recently proposed for characterizi...
Computer Simulation Models, Statistical Probability Static Electricity Stochastic Processes Sequence Alignment Principal Component Analysis Models, Molecular Amino Acid Sequence Proteins Unsupervised Machine Learning
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AGL-Score: Algebraic Graph Learning Score for Protein-Ligand Binding Scoring, Ranking, Docking, and Screening.

Journal of chemical information and modeling Jul 1, 2019
Although algebraic graph theory-based models have been widely applied in physical modeling and molecular studies, they are typically incompetent in the analysis and prediction of biomolecular properties, confirming the common belief that "one cannot ...
Computational Biology Proteins Databases, Protein Molecular Docking Simulation Drug Design Molecular Structure Machine Learning Algorithms
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Trader as a new optimization algorithm predicts drug-target interactions efficiently.

Scientific reports Jun 27, 2019
Several machine learning approaches have been proposed for predicting new benefits of the existing drugs. Although these methods have introduced new usage(s) of some medications, efficient methods can lead to more accurate predictions. To this end, w...
Computational Biology Proteins Humans Drug Development Software Data Mining Machine Learning Algorithms Datasets as Topic ROC Curve
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A Self-Consistent Sonification Method to Translate Amino Acid Sequences into Musical Compositions and Application in Protein Design Using Artificial Intelligence.

ACS nano Jun 26, 2019
We report a self-consistent method to translate amino acid sequences into audible sound, use the representation in the musical space to train a neural network, and then apply it to generate protein designs using artificial intelligence (AI). The soni...
Proteins Models, Molecular Mutation Humans Artificial Intelligence Protein Folding Amino Acid Sequence Sound Protein Conformation
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