AIMC Topic: Sequence Analysis, Protein

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Protein threading using residue co-variation and deep learning.

Bioinformatics (Oxford, England) Jul 1, 2018
MOTIVATION: Template-based modeling, including homology modeling and protein threading, is a popular method for protein 3D structure prediction. However, alignment generation and template selection for protein sequences without close templates remain...
Protein Structure, Secondary Sequence Alignment Sequence Analysis, Protein Proteins Models, Molecular Software Deep Learning
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DeepFam: deep learning based alignment-free method for protein family modeling and prediction.

Bioinformatics (Oxford, England) Jul 1, 2018
MOTIVATION: A large number of newly sequenced proteins are generated by the next-generation sequencing technologies and the biochemical function assignment of the proteins is an important task. However, biological experiments are too expensive to cha...
Proteins Receptors, G-Protein-Coupled Sequence Analysis, Protein Software Deep Learning
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DeepSig: deep learning improves signal peptide detection in proteins.

Bioinformatics (Oxford, England) May 15, 2018
MOTIVATION: The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization.
Protein Transport Eukaryota Sequence Analysis, Protein Bacteria Internet Protein Sorting Signals Software Deep Learning
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Protein classification using modified n-grams and skip-grams.

Bioinformatics (Oxford, England) May 1, 2018
MOTIVATION: Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used t...
Protein Conformation Molecular Sequence Annotation Sequence Analysis, Protein Natural Language Processing Models, Molecular Supervised Machine Learning Proteins
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DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.

Bioinformatics (Oxford, England) May 1, 2018
MOTIVATION: Significant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the ...
Machine Learning Computational Biology Caspases Neural Networks, Computer Protein Conformation Sequence Analysis, Protein
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pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information.

Bioinformatics (Oxford, England) May 1, 2018
MOTIVATION: For in-depth understanding the functions of proteins in a cell, the knowledge of their subcellular localization is indispensable. The current study is focused on human protein subcellular location prediction based on the sequence informat...
Protein Transport Gene Ontology Sequence Analysis, Protein Humans Software Computational Biology
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DeepSF: deep convolutional neural network for mapping protein sequences to folds.

Bioinformatics (Oxford, England) Apr 15, 2018
MOTIVATION: Protein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a target protein based on the fold of a te...
Sequence Analysis, Protein Animals Computational Biology Protein Folding Humans Sequence Alignment Models, Molecular Neural Networks, Computer Software
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DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.

Bioinformatics (Oxford, England) Feb 15, 2018
MOTIVATION: A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often ...
Sequence Analysis, Protein Gene Ontology Eukaryota Humans Software Animals Computational Biology Protein Interaction Maps Proteins Bacteria Supervised Machine Learning
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Structure-based prediction of protein- peptide binding regions using Random Forest.

Bioinformatics (Oxford, England) Feb 1, 2018
MOTIVATION: Protein-peptide interactions are one of the most important biological interactions and play crucial role in many diseases including cancer. Therefore, knowledge of these interactions provides invaluable insights into all cellular processe...
Peptides Protein Binding Proteins Protein Domains Computational Biology Humans Machine Learning Sequence Analysis, Protein Protein Tyrosine Phosphatase, Non-Receptor Type 4
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Survey of Computational Approaches for Prediction of DNA-Binding Residues on Protein Surfaces.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2018
The increasing number of protein structures with uncharacterized function necessitates the development of in silico prediction methods for functional annotations on proteins. In this chapter, different kinds of computational approaches are briefly in...
Binding Sites Databases, Protein Sequence Analysis, Protein DNA Protein Binding Amino Acid Sequence Computational Biology Machine Learning DNA-Binding Proteins Protein Conformation Molecular Docking Simulation
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