AI Medical Compendium Topic:
Proteins

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prPred-DRLF: Plant R protein predictor using deep representation learning features.

Proteomics
Plant resistance (R) proteins play a significant role in the detection of pathogen invasion. Accurately predicting plant R proteins is a key task in phytopathology. Most plant R protein predictors are dependent on traditional feature extraction metho...

FoldHSphere: deep hyperspherical embeddings for protein fold recognition.

BMC bioinformatics
BACKGROUND: Current state-of-the-art deep learning approaches for protein fold recognition learn protein embeddings that improve prediction performance at the fold level. However, there still exists aperformance gap at the fold level and the (relativ...

Genetic variant effect prediction by supervised nonnegative matrix tri-factorization.

Molecular omics
Discriminating between deleterious and neutral mutations among numerous non-synonymous single nucleotide variants (nsSNVs) that may be observed through whole exome sequencing (WES) is considered a great challenge. In this regard, many machine learnin...

AoP-LSE: Antioxidant Proteins Classification Using Deep Latent Space Encoding of Sequence Features.

Current issues in molecular biology
It is of utmost importance to develop a computational method for accurate prediction of antioxidants, as they play a vital role in the prevention of several diseases caused by oxidative stress. In this correspondence, we present an effective computat...

Protein-protein interaction prediction based on ordinal regression and recurrent convolutional neural networks.

BMC bioinformatics
BACKGROUND: Protein protein interactions (PPIs) are essential to most of the biological processes. The prediction of PPIs is beneficial to the understanding of protein functions and thus is helpful to pathological analysis, disease diagnosis and drug...

Protein Family Classification from Scratch: A CNN Based Deep Learning Approach.

IEEE/ACM transactions on computational biology and bioinformatics
Next-generation sequencing techniques provide us with an opportunity for generating sequenced proteins and identifying the biological families and functions of these proteins. However, compared with identified proteins, uncharacterized proteins consi...

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

IEEE/ACM transactions on computational biology and bioinformatics
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...

Protein Crystallization Identification via Fuzzy Model on Linear Neighborhood Representation.

IEEE/ACM transactions on computational biology and bioinformatics
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...

MANORAA: A machine learning platform to guide protein-ligand design by anchors and influential distances.

Structure (London, England : 1993)
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...

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
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 ...