AIMC Topic: Machine Learning

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Development of a web-based tool for estimating individualized survival curves in glioblastoma using clinical, mRNA, and tumor microenvironment features with fusion techniques.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
OBJECTIVE: Glioblastoma (GBM), one of the most common brain tumors, is known for its low survival rates and poor treatment responses. This study aims to create a robust predictive model that integrates multiple feature types, including clinical data,...

Research hotspots and frontiers of machine learning in renal medicine: a bibliometric and visual analysis from 2013 to 2024.

International urology and nephrology
BACKGROUND: The kidney, an essential organ of the human body, can suffer pathological damage that can potentially have serious adverse consequences on the human body and even affect life. Furthermore, the majority of kidney-induced illnesses are freq...

Machine learning assisted dual-modal SERS detection for circulating tumor cells.

Biosensors & bioelectronics
Detecting circulating tumor cells (CTCs) from blood has become a promising approach for cancer diagnosis. Surface-enhanced Raman Spectroscopy (SERS) has rapidly developed as a significant detection technology for CTCs, offering high sensitivity and s...

Development and validation of a machine learning-based diagnostic model for Parkinson's disease in community-dwelling populations: Evidence from the China health and retirement longitudinal study (CHARLS).

Parkinsonism & related disorders
BACKGROUND: Parkinson's disease (PD) is a major neurodegenerative disorder in Middle-aged and elderly people.There is a pressing need for effective predictive models, particularly in chinese population.

Reducing cadmium and arsenic accumulation in rice grains: The coupled effect of sulfur's biomass dilution and soil immobilization analyzed using meta-analysis and machine learning.

The Science of the total environment
The biogeochemical cycling of sulfur (S) in paddy soil influences cadmium (Cd) and arsenic (As) migration. However, the impact of S application on Cd and As within the soil-rice system has not been fully explored. This study aimed to examine the effe...

Significant spatiotemporal changes in atmospheric particulate mercury pollution in China: Insights from meta-analysis and machine-learning.

The Science of the total environment
PM bound mercury (PBM) in the atmosphere is a major component of total mercury, which is a pollutant of global concern and a potent neurotoxicant when converted to methylmercury. Despite its importance, comprehensive macroanalyses of PBM on large sca...

Predicting burn probability: Dimensionality reduction strategies enable accurate and computationally efficient metamodeling.

Journal of environmental management
Predicting the probability that a given location will be burnt by a wildfire is an important part of understanding the risk that wildfires pose and how our management actions (e.g., prescribed burning) can reduce this risk. Existing methods to quanti...

Topic-sentiment analysis of citizen environmental complaints in China: Using a Stacking-BERT model.

Journal of environmental management
Environmental complaints serve as a crucial means for citizens to participate in environmental regulation, providing precise insights for real-time identification of pollution issues and understanding public environmental concerns. This study analyze...

Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in Crohn's disease by integrating bioinformatics and machine learning.

Autoimmunity
Crohn's disease (CD) presents significant diagnostic and therapeutic challenges due to its unclear etiology, frequent relapses, and limited treatment options. Traditional monitoring often relies on invasive and costly gastrointestinal procedures. Thi...