AIMC Topic: Proteins

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Protein Fold Recognition by Combining Support Vector Machines and Pairwise Sequence Similarity Scores.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
Protein fold recognition is one of the most essential steps for protein structure prediction, aiming to classify proteins into known protein folds. There are two main computational approaches: one is the template-based method based on the alignment s...
Algorithms Sequence Analysis, Protein Proteins Pattern Recognition, Automated Protein Folding Computational Biology Support Vector Machine
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Protein Crystallization Identification via Fuzzy Model on Linear Neighborhood Representation.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
X-ray crystallography is the most popular approach for analyzing protein 3D structure. However, the success rate of protein crystallization is very low (2-10 percent). To reduce the cost of time and resources, lots of computation-based methods are de...
Proteins Crystallization Support Vector Machine Fuzzy Logic Algorithms Computational Biology
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MANORAA: A machine learning platform to guide protein-ligand design by anchors and influential distances.

Structure (London, England : 1993) Oct 5, 2021
The MANORAA platform uses structure-based approaches to provide information on drug design originally derived from mapping tens of thousands of amino acids on a grid. In-depth analyses of the pockets, frequently occurring atoms, influential distances...
Models, Molecular Protein Domains Ligands Proteins Databases, Protein Tetrahydrofolate Dehydrogenase Crystallography, X-Ray Trimethoprim COVID-19 SARS-CoV-2 Protein Binding Computational Biology Drug Design Humans Machine Learning Pandemics
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WinBinVec: Cancer-Associated Protein-Protein Interaction Extraction and Identification of 20 Various Cancer Types and Metastasis Using Different Deep Learning Models.

IEEE journal of biomedical and health informatics Oct 5, 2021
Biophysical protein-protein interactions perform dominant roles in the initiation and progression of many cancer-related pathways. A protein-protein interaction might play different roles in diverse cancer types. Hence, prioritizing the PPIs in each ...
Humans Neural Networks, Computer Neoplasms Proteins Deep Learning Amino Acid Sequence Computational Biology Protein Interaction Mapping
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Artificial intelligence for microbial biotechnology: beyond the hype.

Microbial biotechnology Oct 4, 2021
It has been a landmark year for artificial intelligence (AI) and biotechnology. Perhaps the most noteworthy of these advances was Google DeepMind's AlphaFold2 algorithm which smashed records in protein structure prediction (Jumper et al., 2021, Natur...
Proteins Biotechnology Algorithms Drug Discovery Artificial Intelligence
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Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms.

Nature methods Oct 4, 2021
Deep learning using neural networks relies on a class of machine-learnable models constructed using 'differentiable programs'. These programs can combine mathematical equations specific to a particular domain of natural science with general-purpose, ...
Neural Networks, Computer Deep Learning Biophysics Computational Chemistry Computational Biology Models, Chemical Pattern Recognition, Automated Proteins Protein Conformation
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XENet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers.

PLoS computational biology Sep 27, 2021
Graph representations are traditionally used to represent protein structures in sequence design protocols in which the protein backbone conformation is known. This infrequently extends to machine learning projects: existing graph convolution algorith...
Algorithms Computers Computer-Aided Design Models, Molecular Neural Networks, Computer Computational Biology Protein Conformation Machine Learning Proteins Computer Graphics
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Deep_CNN_LSTM_GO: Protein function prediction from amino-acid sequences.

Computational biology and chemistry Sep 24, 2021
Protein amino acid sequences can be used to determine the functions of the protein. However, determining the function of a single protein requires many resources and a tremendous amount of time. Computational Intelligence methods such as Deep learnin...
Proteins Neural Networks, Computer Amino Acid Sequence Computational Biology
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When homologous sequences meet structural decoys: Accurate contact prediction by tFold in CASP14-(tFold for CASP14 contact prediction).

Proteins Sep 23, 2021
In this paper, we report our tFold framework's performance on the inter-residue contact prediction task in the 14th Critical Assessment of protein Structure Prediction (CASP14). Our tFold framework seamlessly combines both homologous sequences and st...
Models, Molecular Protein Folding Structural Homology, Protein Sequence Analysis, Protein Computational Biology Proteins Neural Networks, Computer Software Reproducibility of Results
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Testing machine learning techniques for general application by using protein secondary structure prediction. A brief survey with studies of pitfalls and benefits using a simple progressive learning approach.

Computers in biology and medicine Sep 23, 2021
Many researchers have recently used the prediction of protein secondary structure (local conformational states of amino acid residues) to test advances in predictive and machine learning technology such as Neural Net Deep Learning. Protein secondary ...
Machine Learning Databases, Protein Algorithms Proteins Protein Structure, Secondary
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