AIMC Topic: Proteins

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Automatic Gene Function Prediction in the 2020's.

Genes Oct 27, 2020
The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active...
Algorithms Computational Biology Models, Biological Amino Acid Sequence Proteins Electronic Data Processing Molecular Sequence Annotation Gene Ontology Machine Learning
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Self-evoluting framework of deep convolutional neural network for multilocus protein subcellular localization.

Medical & biological engineering & computing Oct 20, 2020
In the present paper, deep convolutional neural network (DCNN) is applied to multilocus protein subcellular localization as it is more suitable for multi-class classification. There are two main problems with this application. First, the appropriate ...
Proteins Databases, Factual Support Vector Machine Neural Networks, Computer Algorithms Humans
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Using Deep Learning to Extrapolate Protein Expression Measurements.

Proteomics Oct 16, 2020
Mass spectrometry (MS)-based quantitative proteomics experiments typically assay a subset of up to 60% of the ≈20 000 human protein coding genes. Computational methods for imputing the missing values using RNA expression data usually allow only for i...
Animals Deep Learning Mass Spectrometry Proteins Molecular Sequence Annotation Mice Proteomics
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Guiding Conventional Protein-Ligand Docking Software with Convolutional Neural Networks.

Journal of chemical information and modeling Oct 14, 2020
The high-performance computational techniques have brought significant benefits for drug discovery efforts in recent decades. One of the most challenging problems in drug discovery is the protein-ligand binding pose prediction. To predict the most st...
Molecular Docking Simulation Protein Binding Ligands Neural Networks, Computer Proteins Software
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Application of deep learning in genomics.

Science China. Life sciences Oct 10, 2020
In recent years, deep learning has been widely used in diverse fields of research, such as speech recognition, image classification, autonomous driving and natural language processing. Deep learning has showcased dramatically improved performance in ...
Genomics Proteins DNA Animals Deep Learning RNA Machine Learning Humans Algorithms Neural Networks, Computer
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DeepACTION: A deep learning-based method for predicting novel drug-target interactions.

Analytical biochemistry Oct 6, 2020
Drug-target interactions (DTIs) play a key role in drug development and discovery processes. Wet lab prediction of DTIs is time-consuming, expensive, and tedious. Fortunately, computational approaches can identify new interactions (drug-target pairs)...
ROC Curve Proteins Area Under Curve Neural Networks, Computer Pharmaceutical Preparations
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SPOTONE: Hot Spots on Protein Complexes with Extremely Randomized Trees via Sequence-Only Features.

International journal of molecular sciences Oct 1, 2020
Protein Hot-Spots (HS) are experimentally determined amino acids, key to small ligand binding and tend to be structural landmarks on protein-protein interactions. As such, they were extensively approached by structure-based Machine Learning (ML) pred...
Computational Biology Protein Interaction Mapping Proteins Datasets as Topic Machine Learning Humans Amino Acids Databases, Protein Binding Sites Protein Binding Amino Acid Sequence
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DeepAdd: Protein function prediction from k-mer embedding and additional features.

Computational biology and chemistry Sep 23, 2020
With the application of new high throughput sequencing technology, a large number of protein sequences is becoming available. Determination of the functional characteristics of these proteins by experiments is an expensive endeavor that requires a lo...
Databases, Protein Protein Interaction Maps Amino Acid Sequence Algorithms Proteins Deep Learning
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A Two-Layer SVM Ensemble-Classifier to Predict Interface Residue Pairs of Protein Trimers.

Molecules (Basel, Switzerland) Sep 23, 2020
Study of interface residue pairs is important for understanding the interactions between monomers inside a trimer protein-protein complex. We developed a two-layer support vector machine (SVM) ensemble-classifier that considers physicochemical and ge...
Support Vector Machine Protein Multimerization Computational Biology Proteins Protein Structure, Quaternary
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DNSS2: Improved ab initio protein secondary structure prediction using advanced deep learning architectures.

Proteins Sep 16, 2020
Accurate prediction of protein secondary structure (alpha-helix, beta-strand and coil) is a crucial step for protein inter-residue contact prediction and ab initio tertiary structure prediction. In a previous study, we developed a deep belief network...
Protein Conformation, beta-Strand Neural Networks, Computer Sequence Homology, Amino Acid Software Amino Acid Sequence Protein Conformation, alpha-Helical Deep Learning Markov Chains Proteins Sequence Alignment Databases, Protein
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