AIMC Topic: Amino Acid Sequence

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[Prediction of protein subcellular localization based on multilayer sparse coding].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology Apr 25, 2019
In order to provide a theoretical basis for better understanding the function and properties of proteins, we proposed a simple and effective feature extraction method for protein sequences to determine the subcellular localization of proteins. First,...
Algorithms Computational Biology Amino Acid Sequence Protein Transport Subcellular Fractions Support Vector Machine Proteins
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Identifying short disorder-to-order binding regions in disordered proteins with a deep convolutional neural network method.

Journal of bioinformatics and computational biology Feb 1, 2019
Molecular recognition features (MoRFs) are key functional regions of intrinsically disordered proteins (IDPs), which play important roles in the molecular interaction network of cells and are implicated in many serious human diseases. Identifying MoR...
Humans Neural Networks, Computer Deep Learning Membrane Proteins Databases, Protein Amino Acid Sequence Protein Binding Computational Biology Intrinsically Disordered Proteins Binding Sites
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Using two-dimensional convolutional neural networks for identifying GTP binding sites in Rab proteins.

Journal of bioinformatics and computational biology Feb 1, 2019
Deep learning has been increasingly and widely used to solve numerous problems in various fields with state-of-the-art performance. It can also be applied in bioinformatics to reduce the requirement for feature extraction and reach high performance. ...
Guanosine Triphosphate Binding Sites rab GTP-Binding Proteins Deep Learning Humans Computational Biology Amino Acids Databases, Protein Neural Networks, Computer Amino Acid Sequence
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DeepDom: Predicting protein domain boundary from sequence alone using stacked bidirectional LSTM.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
Protein domain boundary prediction is usually an early step to understand protein function and structure. Most of the current computational domain boundary prediction methods suffer from low accuracy and limitation in handling multi-domain types, or ...
Databases, Protein Protein Domains Amino Acid Sequence Algorithms Deep Learning Neural Networks, Computer Computational Biology
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Recent Advances in Machine Learning Methods for Predicting Heat Shock Proteins.

Current drug metabolism Jan 1, 2019
BACKGROUND: As molecular chaperones, Heat Shock Proteins (HSPs) not only play key roles in protein folding and maintaining protein stabilities, but are also linked with multiple kinds of diseases. Therefore, HSPs have been regarded as the focus of dr...
Heat-Shock Proteins Machine Learning Databases, Protein Computational Biology Amino Acid Sequence
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A Novel Amino Acid Sequence-based Computational Approach to Predicting Cell-penetrating Peptides.

Current computer-aided drug design Jan 1, 2019
INTRODUCTION: Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates.
Cell Membrane Permeability Cell-Penetrating Peptides Machine Learning Computer Simulation Structure-Activity Relationship Amino Acid Sequence Computational Biology Humans
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Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks.

Bioinformatics (Oxford, England) Dec 1, 2018
MOTIVATION: Accurate prediction of a protein contact map depends greatly on capturing as much contextual information as possible from surrounding residues for a target residue pair. Recently, ultra-deep residual convolutional networks were found to b...
Neural Networks, Computer Proteins Amino Acid Sequence Computational Biology Protein Interaction Mapping
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High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features.

Bioinformatics (Oxford, England) Oct 1, 2018
MOTIVATION: In addition to substitution frequency data from protein sequence alignments, many state-of-the-art methods for contact prediction rely on additional sources of information, or features, of protein sequences in order to predict residue-res...
Protein Interaction Domains and Motifs Amino Acid Sequence Sequence Alignment Computational Biology Proteins Neural Networks, Computer Algorithms
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Learned protein embeddings for machine learning.

Bioinformatics (Oxford, England) Aug 1, 2018
MOTIVATION: Machine-learning models trained on protein sequences and their measured functions can infer biological properties of unseen sequences without requiring an understanding of the underlying physical or biological mechanisms. Such models enab...
Sequence Analysis, Protein Software Computational Biology Bacteria Humans Models, Biological Proteins Amino Acid Sequence Machine Learning Eukaryota
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DeepSol: a deep learning framework for sequence-based protein solubility prediction.

Bioinformatics (Oxford, England) Aug 1, 2018
MOTIVATION: Protein solubility plays a vital role in pharmaceutical research and production yield. For a given protein, the extent of its solubility can represent the quality of its function, and is ultimately defined by its sequence. Thus, it is imp...
Deep Learning Computational Biology Solubility Proteins Computer Simulation Amino Acid Sequence
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