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DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations
(Royal Soc Chemistry, 2020)
The identification of physical interactions between drug candidate compounds and target biomolecules is an important process in drug discovery. Since conventional screening procedures are expensive and time consuming, ...
DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks
(Nature Publishing Group, 2019)
Automated protein function prediction is critical for the annotation of uncharacterized protein sequences, where accurate prediction methods are still required. Recently, deep learning based methods have outperformed ...
ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature
(BMC, 2018)
Background: The automated prediction of the enzymatic functions of uncharacterized proteins is a crucial topic in bioinformatics. Although several methods and tools have been proposed to classify enzymes, most of these ...
Large-scale automated function prediction of protein sequences and an experimental case study validation on PTEN transcript variants
(Wiley, 2018)
Recent advances in computing power and machine learning empower functional annotation of protein sequences and their transcript variations. Here, we present an automated prediction system UniGOPred, for GO annotations and ...
CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations
(Oxford Academic, 2021)
Systemic analysis of available large-scale biological/biomedical data is critical for studying biological mechanisms, and developing novel and effective treatment approaches against diseases. However, different layers of ...