AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Protein Interaction Mapping

Showing 91 to 100 of 223 articles

Clear Filters

JCDB: a comprehensive knowledge base for Jatropha curcas, an emerging model for woody energy plants.

BMC genomics
BACKGROUND: Jatropha curcas is an oil-bearing plant, and has seeds with high oil content (~ 40%). Several advantages, such as easy genetic transformation and short generation duration, have led to the emergence of J. curcas as a model for woody energ...

Identification of aberrantly methylated‑differentially expressed genes and gene ontology in prostate cancer.

Molecular medicine reports
Prostate cancer (PCa) is the most frequent urological malignancy in men worldwide. DNA methylation has an essential role in the etiology and pathogenesis of PCa. The purpose of the present study was to identify the aberrantly methylated‑differentiall...

Integrated structural modeling and super-resolution imaging resolve GPCR oligomers.

Progress in molecular biology and translational science
Formation of G protein-coupled receptors (GPCRs) dimers and higher order oligomers represents a key mechanism in pleiotropic signaling, yet how individual protomers function within oligomers remains poorly understood. For the Class A/rhodopsin subfam...

Multimodal deep representation learning for protein interaction identification and protein family classification.

BMC bioinformatics
BACKGROUND: Protein-protein interactions(PPIs) engage in dynamic pathological and biological procedures constantly in our life. Thus, it is crucial to comprehend the PPIs thoroughly such that we are able to illuminate the disease occurrence, achieve ...

Improving neural protein-protein interaction extraction with knowledge selection.

Computational biology and chemistry
Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. Meanwhile, knowledge bases (KBs) contain huge amounts of structured information of protein entities and thei...

Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks.

PloS one
Protein-protein interaction network data provides valuable information that infers direct links between genes and their biological roles. This information brings a fundamental hypothesis for protein function prediction that interacting proteins tend ...

An Ensemble Classifier to Predict Protein-Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model.

International journal of molecular sciences
Protein plays a critical role in the regulation of biological cell functions. Among them, whether proteins interact with each other has become a fundamental problem, because proteins usually perform their functions by interacting with other proteins....

Automated feature engineering improves prediction of protein-protein interactions.

Amino acids
Over the last decade, various machine learning (ML) and statistical approaches for protein-protein interaction (PPI) predictions have been developed to help annotating functional interactions among proteins, essential for our system-level understandi...

Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks.

Genomics
BACKGROUND: Glioma is the most lethal nervous system cancer. Recent studies have made great efforts to study the occurrence and development of glioma, but the molecular mechanisms are still unclear. This study was designed to reveal the molecular mec...

A novel conjoint triad auto covariance (CTAC) coding method for predicting protein-protein interaction based on amino acid sequence.

Mathematical biosciences
Protein-protein interactions (PPIs) play a crucial role in the life-sustaining activities of organisms. Although various methods for the prediction of PPIs have been developed in the past decades, their robustness and prediction accuracy need to be i...