AIMC Topic: Protein Interaction Maps

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Identification of ubiquitination-related key biomarkers and immune infiltration in Crohn's disease by bioinformatics analysis and machine learning.

Scientific reports
Crohn's disease (CD) is a chronic inflammatory bowel disease with an unknown etiology. Ubiquitination plays a significant role in the pathogenesis of CD. This study aimed to explore the functional roles of ubiquitination-related genes in CD. Differen...

Identification and validation of key autophagy-related genes in lupus nephritis by bioinformatics and machine learning.

PloS one
INTRODUCTION: Lupus nephritis (LN) is one of the most frequent and serious organic manifestations of systemic lupus erythematosus (SLE). Autophagy, a new form of programmed cell death, has been implicated in a variety of renal diseases, but the relat...

Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.

Frontiers in immunology
INTRODUCTION: Type 2 diabetes (T2D) is a complex metabolic disorder with significant global health implications. Understanding the molecular mechanisms underlying T2D is crucial for developing effective therapeutic strategies. This study employs sing...

Identification of lipid metabolism-related gene markers and construction of a diagnostic model for multiple sclerosis: An integrated analysis by bioinformatics and machine learning.

Analytical biochemistry
BACKGROUND: Multiple sclerosis (MS) is an autoimmune inflammatory disorder that causes neurological disability. Dysregulated lipid metabolism contributes to the pathogenesis of MS. This study aimed to identify lipid metabolism-related gene markers an...

A graph neural network approach for hierarchical mapping of breast cancer protein communities.

BMC bioinformatics
BACKGROUND: Comprehensively mapping the hierarchical structure of breast cancer protein communities and identifying potential biomarkers from them is a promising way for breast cancer research. Existing approaches are subjective and fail to take info...

Establishment of a nomogram model based on immune-related genes using machine learning for aortic dissection diagnosis and immunomodulation assessment.

International journal of medical sciences
The clinical manifestation of aortic dissection (AD) is complex and varied, making early diagnosis crucial for patient survival. This study aimed to identify immune-related markers to establish a nomogram model for AD diagnosis. Three datasets from G...

PPILS: Protein-protein interaction prediction with language of biological coding.

Computers in biology and medicine
Protein-protein interactions within a cell are essential for various fundamental biological processes. Computational techniques have arisen in bioinformatics due to the challenging and resource-intensive nature of experimental protein pair interactio...

Supervised learning approaches for predicting Ebola-Human Protein-Protein interactions.

Gene
The goal of this research work is to predict protein-protein interactions (PPIs) between the Ebola virus and the host who is at risk of infection. Since there are very limited databases available on the Ebola virus; we have prepared a comprehensive d...

MAGPIE: A Machine Learning Approach to Decipher Protein-Protein Interactions in Human Plasma.

Journal of proteome research
Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods are often used to identify protein-protein interactions (PPIs). While these approaches are prone to false positive identifications through contamination and antibody nonspecif...

Deciphering Necroptosis-Associated Molecular Subtypes in Acute Ischemic Stroke Through Bioinformatics and Machine Learning Analysis.

Journal of molecular neuroscience : MN
Acute ischemic stroke (AIS) is a severe disorder characterized by complex pathophysiological processes, which can lead to disability and death. This study aimed to determine necroptosis-associated genes in acute ischemic stroke (AIS) and to investiga...