AIMC Topic: Amino Acid Sequence

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End-to-end learning for compound activity prediction based on binding pocket information.

BMC bioinformatics Oct 29, 2021
BACKGROUND: Recently, machine learning-based ligand activity prediction methods have been greatly improved. However, if known active compounds of a target protein are unavailable, the machine learning-based method cannot be applied. In such cases, do...
Ligands Amino Acid Sequence Protein Binding Neural Networks, Computer Machine Learning Molecular Docking Simulation Proteins
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An Ensemble Deep Learning based Predictor for Simultaneously Identifying Protein Ubiquitylation and SUMOylation Sites.

BMC bioinformatics Oct 24, 2021
BACKGROUND: Several computational tools for predicting protein Ubiquitylation and SUMOylation sites have been proposed to study their regulatory roles in gene location, gene expression, and genome replication. However, existing methods generally rely...
Sumoylation Amino Acid Sequence Deep Learning Lysine Ubiquitination
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Predicting subcellular location of protein with evolution information and sequence-based deep learning.

BMC bioinformatics Oct 22, 2021
BACKGROUND: Protein subcellular localization prediction plays an important role in biology research. Since traditional methods are laborious and time-consuming, many machine learning-based prediction methods have been proposed. However, most of the p...
Amino Acid Sequence Position-Specific Scoring Matrices Proteins Deep Learning Computational Biology Databases, Protein
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Support vector machine-based prediction of pore-forming toxins (PFT) using distributed representation of reduced alphabets.

Journal of bioinformatics and computational biology Oct 22, 2021
Bacterial virulence can be attributed to a wide variety of factors including toxins that harm the host. Pore-forming toxins are one class of toxins that confer virulence to the bacteria and are one of the promising targets for therapeutic interventio...
Support Vector Machine Bacteria Amino Acid Sequence
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CAMAP: Artificial neural networks unveil the role of codon arrangement in modulating MHC-I peptides presentation.

PLoS computational biology Oct 22, 2021
MHC-I associated peptides (MAPs) play a central role in the elimination of virus-infected and neoplastic cells by CD8 T cells. However, accurately predicting the MAP repertoire remains difficult, because only a fraction of the transcriptome generates...
Algorithms Computational Biology Neural Networks, Computer Codon Amino Acid Sequence Histocompatibility Antigens Class I Humans
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Fast activation maximization for molecular sequence design.

BMC bioinformatics Oct 20, 2021
BACKGROUND: Optimization of DNA and protein sequences based on Machine Learning models is becoming a powerful tool for molecular design. Activation maximization offers a simple design strategy for differentiable models: one-hot coded sequences are fi...
Machine Learning Amino Acid Sequence Algorithms
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CL-ACP: a parallel combination of CNN and LSTM anticancer peptide recognition model.

BMC bioinformatics Oct 20, 2021
BACKGROUND: Anticancer peptides are defence substances with innate immune functions that can selectively act on cancer cells without harming normal cells and many studies have been conducted to identify anticancer peptides. In this paper, we introduc...
Amino Acid Sequence Peptides Neural Networks, Computer
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A Deep Learning Approach for Predicting Antigenic Variation of Influenza A H3N2.

Computational and mathematical methods in medicine Oct 16, 2021
Modeling antigenic variation in influenza (flu) virus A H3N2 using amino acid sequences is a promising approach for improving the prediction accuracy of immune efficacy of vaccines and increasing the efficiency of vaccine screening. Antigenic drift a...
Computational Biology Neural Networks, Computer Antigens, Viral Humans Deep Learning Influenza A Virus, H3N2 Subtype Amino Acid Sequence Databases, Protein Influenza, Human Hemagglutinin Glycoproteins, Influenza Virus Antigenic Variation
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Anti-cancer Peptide Recognition Based on Grouped Sequence and Spatial Dimension Integrated Networks.

Interdisciplinary sciences, computational life sciences Oct 12, 2021
The diversification of the characteristic sequences of anti-cancer peptides has imposed difficulties on research. To effectively predict new anti-cancer peptides, this paper proposes a more suitable feature grouping sequence and spatial dimension-int...
Amino Acid Sequence Peptides Algorithms Neural Networks, Computer
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Genetic variant effect prediction by supervised nonnegative matrix tri-factorization.

Molecular omics Oct 11, 2021
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...
Machine Learning Proteins Amino Acid Sequence Phenotype Algorithms
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