AIMC Topic: Amino Acid Sequence

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DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Automated function prediction (AFP) of proteins is a large-scale multi-label classification problem. Two limitations of most network-based methods for AFP are (i) a single model must be trained for each species and (ii) protein sequence i...

Prediction of prokaryotic transposases from protein features with machine learning approaches.

Microbial genomics
Identification of prokaryotic transposases (Tnps) not only gives insight into the spread of antibiotic resistance and virulence but the process of DNA movement. This study aimed to develop a classifier for predicting Tnps in bacteria and archaea usin...

iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization.

Nucleic acids research
Sequence-based analysis and prediction are fundamental bioinformatic tasks that facilitate understanding of the sequence(-structure)-function paradigm for DNAs, RNAs and proteins. Rapid accumulation of sequences requires equally pervasive development...

CATH functional families predict functional sites in proteins.

Bioinformatics (Oxford, England)
MOTIVATION: Identification of functional sites in proteins is essential for functional characterization, variant interpretation and drug design. Several methods are available for predicting either a generic functional site, or specific types of funct...

Learning the molecular grammar of protein condensates from sequence determinants and embeddings.

Proceedings of the National Academy of Sciences of the United States of America
Intracellular phase separation of proteins into biomolecular condensates is increasingly recognized as a process with a key role in cellular compartmentalization and regulation. Different hypotheses about the parameters that determine the tendency of...

Sequence representation approaches for sequence-based protein prediction tasks that use deep learning.

Briefings in functional genomics
Deep learning has been increasingly used in bioinformatics, especially in sequence-based protein prediction tasks, as large amounts of biological data are available and deep learning techniques have been developed rapidly in recent years. For sequenc...

Towards peptide-based tunable multistate memristive materials.

Physical chemistry chemical physics : PCCP
Development of new memristive hardware is a technological requirement towards widespread neuromorphic computing. Molecular spintronics seems to be a fertile field for the design and preparation of this hardware. Within molecular spintronics, recent r...

Machine and Deep Learning for Prediction of Subcellular Localization.

Methods in molecular biology (Clifton, N.J.)
Protein subcellular localization prediction (PSLP), which plays an important role in the field of computational biology, identifies the position and function of proteins in cells without expensive cost and laborious effort. In the past few decades, v...

Deep Learning in Drug Target Interaction Prediction: Current and Future Perspectives.

Current medicinal chemistry
Drug-target Interactions (DTIs) prediction plays a central role in drug discovery. Computational methods in DTIs prediction have gained more attention because carrying out in vitro and in vivo experiments on a large scale is costly and time-consuming...

Improved Prediction of Protein-Protein Interaction Mapping on by Using Amino Acid Sequence Features in a Supervised Learning Framework.

Protein and peptide letters
BACKGROUND: Protein-Protein Interaction (PPI) has emerged as a key role in the control of many biological processes including protein function, disease incidence, and therapy design. However, the identification of PPI by wet lab experiment is a chall...