AIMC Topic: Protein Interaction Maps

Clear Filters Showing 341 to 350 of 453 articles

IAS: Interaction Specific GO Term Associations for Predicting Protein-Protein Interaction Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Proteins carry out their function in a cell through interactions with other proteins. A large scale protein-protein interaction (PPI) network of an organism provides static yet an essential structure of interactions, which is valuable clue for unders...

PathwaysWeb: a gene pathways API with directional interactions, expanded gene ontology, and versioning.

Bioinformatics (Oxford, England)
UNLABELLED: PathwaysWeb is a resource-based, well-documented web system that provides publicly available information on genes, biological pathways, Gene Ontology (GO) terms, gene-gene interaction networks (importantly, with the directionality of inte...

Genome-wide prediction of prokaryotic two-component system networks using a sequence-based meta-predictor.

BMC bioinformatics
BACKGROUND: Two component systems (TCS) are signalling complexes manifested by a histidine kinase (receptor) and a response regulator (effector). They are the most abundant signalling pathways in prokaryotes and control a wide range of biological pro...

Dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks.

BMC bioinformatics
BACKGROUND: The identification of protein functional modules would be a great aid in furthering our knowledge of the principles of cellular organization. Most existing algorithms for identifying protein functional modules have a common defect -- once...

Protein complex detection in PPI networks based on data integration and supervised learning method.

BMC bioinformatics
BACKGROUND: Revealing protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to predict...

Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder.

Computational biology and chemistry
Protein-protein interactions (PPIs) play essential roles in many biological processes. In protein-protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (I...

Functional annotation and biological interpretation of proteomics data.

Biochimica et biophysica acta
Proteomics experiments often generate a vast amount of data. However, the simple identification and quantification of proteins from a cell proteome or subproteome is not sufficient for the full understanding of complex mechanisms occurring in the bio...

Identification of mitochondria metabolism-related biomarkers associated with the development of rheumatoid arthritis using bioinformatics: An observational study.

Medicine
Rheumatoid arthritis (RA) is a systemic inflammatory autoimmune disorder that has serious physical and mental health implications. It is evident that disruptions to mitochondrial function have a considerable impact on the survival, activation, and di...

Unraveling risk factors and transcriptomic signatures in liver cancer progression and mortality through machine learning and bioinformatics.

Briefings in functional genomics
Liver cancer (LC) is the second leading cause of cancer-related deaths globally, yet the molecular mechanisms linking its progression with associated risk factors (RFs) remain poorly understood. To address this, we developed an integrative multi-stag...

Integrative machine learning and bioinformatics analysis unveil key genes for precise glioma classification and prognosis evaluation.

Computational biology and chemistry
Gliomas exhibit significant heterogeneity and diverse molecular subtypes, and there are marked differences in treatment strategies and prognoses for gliomas of different grades and molecular types. However, current glioma molecular subtyping systems ...