AIMC Topic: Proteins

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Hierarchical deep learning for predicting GO annotations by integrating protein knowledge.

Bioinformatics (Oxford, England)
MOTIVATION: Experimental testing and manual curation are the most precise ways for assigning Gene Ontology (GO) terms describing protein functions. However, they are expensive, time-consuming and cannot cope with the exponential growth of data genera...

Distributed nonsynchronous event-triggered state estimation of genetic regulatory networks with hidden Markovian jumping parameters.

Mathematical biosciences and engineering : MBE
In this paper, the distributed state estimation problem of genetic regulatory networks (GRNs) with hidden Markovian jumping parameters (HMJPs) is explored. Furthermore, in order to improve the communication efficiency among state estimation sensors, ...

Predicting ncRNA-protein interactions based on dual graph convolutional network and pairwise learning.

Briefings in bioinformatics
Noncoding RNAs (ncRNAs) have recently attracted considerable attention due to their key roles in biology. The ncRNA-proteins interaction (NPI) is often explored to reveal some biological activities that ncRNA may affect, such as biological traits, di...

SADeepcry: a deep learning framework for protein crystallization propensity prediction using self-attention and auto-encoder networks.

Briefings in bioinformatics
The X-ray diffraction (XRD) technique based on crystallography is the main experimental method to analyze the three-dimensional structure of proteins. The production process of protein crystals on which the XRD technique relies has undergone multiple...

DLF-Sul: a multi-module deep learning framework for prediction of S-sulfinylation sites in proteins.

Briefings in bioinformatics
Protein S-sulfinylation is an important posttranslational modification that regulates a variety of cell and protein functions. This modification has been linked to signal transduction, redox homeostasis and neuronal transmission in studies. Therefore...

PLP_FS: prediction of lysine phosphoglycerylation sites in protein using support vector machine and fusion of multiple F_Score feature selection.

Briefings in bioinformatics
A newly invented post-translational modification (PTM), phosphoglycerylation, has shown its essential role in the construction and functional properties of proteins and dangerous human diseases. Hence, it is very urgent to know about the molecular me...

PScL-DDCFPred: an ensemble deep learning-based approach for characterizing multiclass subcellular localization of human proteins from bioimage data.

Bioinformatics (Oxford, England)
MOTIVATION: Characterization of protein subcellular localization has become an important and long-standing task in bioinformatics and computational biology, which provides valuable information for elucidating various cellular functions of proteins an...

RAPPPID: towards generalizable protein interaction prediction with AWD-LSTM twin networks.

Bioinformatics (Oxford, England)
MOTIVATION: Computational methods for the prediction of protein-protein interactions (PPIs), while important tools for researchers, are plagued by challenges in generalizing to unseen proteins. Datasets used for modelling protein-protein predictions ...

Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling.

Bioinformatics (Oxford, England)
MOTIVATION: Recently, AlphaFold2 achieved high experimental accuracy for the majority of proteins in Critical Assessment of Structure Prediction (CASP 14). This raises the hope that one day, we may achieve the same feat for RNA structure prediction f...