AIMC Topic: Protein Interaction Maps

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The signed two-space proximity model for learning representations in protein-protein interaction networks.

Bioinformatics (Oxford, England)
MOTIVATION: Accurately predicting complex protein-protein interactions (PPIs) is crucial for decoding biological processes, from cellular functioning to disease mechanisms. However, experimental methods for determining PPIs are computationally expens...

Autophagy-related gene SQSTM1 predicts the prognosis of hepatocellular carcinoma.

Computers in biology and medicine
BACKGROUND: The relationship between autophagy and the progression of hepatocellular carcinoma (HCC) is notably substantial, yet the underlying mechanisms remain incompletely elucidated. Our objective is to construct a predictive model, thereby provi...

Integrative approaches for predicting protein network perturbations through machine learning and structural characterization.

Journal of proteomics
Chromatin remodeling complexes, such as the Saccharomyces cerevisiae INO80 complex, exemplify how dynamic protein interaction networks govern cellular function through a balance of conserved structural modules and context-dependent functional partner...

Similarity of immune-associated markers in COVID-19 and Kawasaki disease: analyses from bioinformatics and machine learning.

BMC pediatrics
BACKGROUND: Infection by the SARS-CoV-2 virus can cause coronavirus disease 2019 (COVID-19) and can also exacerbate the symptoms of Kawasaki disease (KD), an acute vasculitis that mostly affects children. This study used bioinformatics and machine le...

A multi-objective evolutionary algorithm for detecting protein complexes in PPI networks using gene ontology.

Scientific reports
Detecting protein complexes is crucial in computational biology for understanding cellular mechanisms and facilitating drug discovery. Evolutionary algorithms (EAs) have proven effective in uncovering protein complexes within networks of protein-prot...

Identification of potential metabolic biomarkers and immune cell infiltration for metabolic associated steatohepatitis by bioinformatics analysis and machine learning.

Scientific reports
BACKGROUND: Metabolic associated steatohepatitis (MASH) represents a severe subtype of metabolic associated fatty liver disease (MASLD), with an increased risk of progression to cirrhosis and hepatocellular carcinoma. The nomenclature shift from nona...

Exploring the potential biomarkers and potential causality of Ménière disease based on bioinformatics and machine learning.

Medicine
Meniere disease (MD) is a common inner ear disorder closely related to immune abnormalities, but research on the characteristic genes between MD and immune responses is still insufficient. We employ bioinformatics and machine learning to predict pote...