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Protein Interaction Maps

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A nine-hub-gene signature of metabolic syndrome identified using machine learning algorithms and integrated bioinformatics.

Bioengineered
Early risk assessments and interventions for metabolic syndrome (MetS) are limited because of a lack of effective biomarkers. In the present study, several candidate genes were selected as a blood-based transcriptomic signature for MetS. We collected...

Identification of viral-mediated pathogenic mechanisms in neurodegenerative diseases using network-based approaches.

Briefings in bioinformatics
During the course of a viral infection, virus-host protein-protein interactions (PPIs) play a critical role in allowing viruses to replicate and survive within the host. These interspecies molecular interactions can lead to viral-mediated perturbatio...

SMMPPI: a machine learning-based approach for prediction of modulators of protein-protein interactions and its application for identification of novel inhibitors for RBD:hACE2 interactions in SARS-CoV-2.

Briefings in bioinformatics
Small molecule modulators of protein-protein interactions (PPIs) are being pursued as novel anticancer, antiviral and antimicrobial drug candidates. We have utilized a large data set of experimentally validated PPI modulators and developed machine le...

Prediction and collection of protein-metabolite interactions.

Briefings in bioinformatics
Interactions between proteins and small molecule metabolites play vital roles in regulating protein functions and controlling various cellular processes. The activities of metabolic enzymes, transcription factors, transporters and membrane receptors ...

Why can deep convolutional neural networks improve protein fold recognition? A visual explanation by interpretation.

Briefings in bioinformatics
As an essential task in protein structure and function prediction, protein fold recognition has attracted increasing attention. The majority of the existing machine learning-based protein fold recognition approaches strongly rely on handcrafted featu...

ProtFold-DFG: protein fold recognition by combining Directed Fusion Graph and PageRank algorithm.

Briefings in bioinformatics
As one of the most important tasks in protein structure prediction, protein fold recognition has attracted more and more attention. In this regard, some computational predictors have been proposed with the development of machine learning and artifici...

Application of deep learning methods in biological networks.

Briefings in bioinformatics
The increase in biological data and the formation of various biomolecule interaction databases enable us to obtain diverse biological networks. These biological networks provide a wealth of raw materials for further understanding of biological system...

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...

Study on the differentially expressed genes and signaling pathways in dermatomyositis using integrated bioinformatics method.

Medicine
Dermatomyositis is a common connective tissue disease. The occurrence and development of dermatomyositis is a result of multiple factors, but its exact pathogenesis has not been fully elucidated. Here, we used biological information method to explore...

DeepGOPlus: improved protein function prediction from sequence.

Bioinformatics (Oxford, England)
MOTIVATION: Protein function prediction is one of the major tasks of bioinformatics that can help in wide range of biological problems such as understanding disease mechanisms or finding drug targets. Many methods are available for predicting protein...