AIMC Topic: Protein Interaction Maps

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How did we get there? AI applications to biological networks and sequences.

Computers in biology and medicine
The rapidly advancing field of artificial intelligence (AI) has transformed numerous scientific domains, including biology, where a vast and complex volume of data is available for analysis. This paper provides a comprehensive overview of the current...

Identification of biomarkers associated with M1 macrophages in the ST-segment elevation myocardial infarction through bioinformatics and machine learning approaches.

Scientific reports
ST-segment elevation myocardial infarction (STEMI) is considered a critical cardiac condition with a poor prognosis. Shortly after STEMI occurs, the increased number of circulating leukocytes including macrophages can lead to the accumulation of more...

Association of the characteristics of brain magnetic resonance imaging with genes related to disease onset in schizophrenia patients.

SLAS technology
BACKGROUND: Schizophrenia (SCH) is a complex neurodevelopmental disorder, whose pathogenesis is not fully elucidated. This article aims to reveal disease-specific brain structural and functional changes and their potential genetic basis by analyzing ...

PlantPathoPPI: An Ensemble-based Machine Learning Architecture for Prediction of Protein-Protein Interactions between Plants and Pathogens.

Journal of molecular biology
This study aimed to develop a machine learning-based tool for predicting protein-protein interactions (PPIs) between plant-pathogen systems, addressing the challenges of experimental PPI identification. Identifying PPIs in plant-pathogen interactions...

Proteomic Learning of Gamma-Aminobutyric Acid (GABA) Receptor-Mediated Anesthesia.

Journal of chemical information and modeling
Anesthetics are crucial in surgical procedures and therapeutic interventions, but they come with side effects and varying levels of effectiveness, calling for novel anesthetic agents that offer more precise and controllable effects. Targeting Gamma-a...

GALR1 and PENK serve as potential biomarkers in invasive non-functional pituitary neuroendocrine tumours.

Gene
BACKGROUND: Some nonfunctioning pituitary neuroendocrine tumor (NFPitNET) can show invasive growth, which increases the difficulty of surgery and indicates a poor prognosis. However, the molecular mechanism related to invasiveness remains to be furth...

Air pollution and prostate cancer: Unraveling the connection through network toxicology and machine learning.

Ecotoxicology and environmental safety
BACKGROUND: In recent years, air pollution has been demonstrated to be associated with the occurrence of various diseases. This study aims to explore the potential association between air pollutants and prostate cancer (PCa) and to identify key genes...

Identification of potential biomarkers for lung cancer using integrated bioinformatics and machine learning approaches.

PloS one
Lung cancer is one of the most common cancer and the leading cause of cancer-related death worldwide. Early detection of lung cancer can help reduce the death rate; therefore, the identification of potential biomarkers is crucial. Thus, this study ai...

Identification of Biomarkers for Response to Interferon in Chronic Hepatitis B Based on Bioinformatics Analysis and Machine Learning.

Viral immunology
Interferon (IFN) is a pivotal agent against hepatitis B virus (HBV) in clinic, but there is a lack of accurate biomarkers to predict the response to IFN therapy in patients with chronic hepatitis B (CHB). Our study aimed to investigate potential targ...

NPI-HGNN: A Heterogeneous Graph Neural Network-Based Approach for Predicting ncRNA-Protein Interactions.

Interdisciplinary sciences, computational life sciences
Accurate identification of ncRNA-protein interactions (NPIs) is critical for understanding various cellular activities and biological functions of ncRNAs and proteins. Many sequence- and/or structure- and graph-based computational approaches have bee...