AIMC Topic: Machine Learning

Clear Filters Showing 27971 to 27980 of 34417 articles

Novel natural vector with asymmetric covariance for classifying biological sequences.

Gene
The genome sequences of organisms form a large and complex landscape, presenting a significant challenge in bioinformatics: how to utilize mathematical tools to describe and analyze this space effectively. The ability to compare relationships between...

Temporal evolution stages classification and aging time prediction of gel-pen ink using GC-IMS combined with machine learning for forensic science applications.

Journal of chromatography. A
Determining the temporal evolution of inks remains a critical challenge in forensic document analysis. The temporal evolution stages classification and aging time prediction of gel-pen ink were investigated by integrating gas chromatography-ion mobil...

Integration of multi-omics data and machine learning to identify antioxidant biomarkers in type 1 diabetes.

Free radical biology & medicine
The identification of biomarkers for early diagnosis and monitoring the progression of Type 1 Diabetes (T1DM) is essential for improving disease management. This study integrates multi-omics data with machine learning to identify antioxidant stress p...

The melanoma MEGA-study: Integrating proteogenomics, digital pathology, and AI-analytics for precision oncology.

Journal of proteomics
Melanoma remains the most aggressive form of skin cancer, characterized by high metastatic potential, genetic heterogeneity, and resistance to conventional therapies. The Melanoma MEGA-Study is a multi-center initiative designed to address these clin...

Prediction of trihalomethane occurrence and cancer risk using interpretable machine learning and virtual data augmentation.

Journal of hazardous materials
Trihalomethanes (THMs) in drinking water are regulated for carcinogenic health risks. However, frequent water quality monitoring imposes significant resource burdens. This study proposes a framework integrating interpretable machine learning (ML) wit...

Managing waste for production of low-carbon concrete mix using uncertainty-aware machine learning model.

Environmental research
This study introduces an uncertainty-aware AI-driven optimization framework for designing sustainable concrete mixtures that incorporate waste-derived materials. The primary objectives are to reduce global warming potential (GWP) and promote a circul...

A novel hybrid machine learning approach for accurate retrieval of ocean surface chlorophyll-a across oligotrophic to eutrophic waters.

Environmental research
Accurate assessment of chlorophyll a (Chla) concentration distribution and variations is significant for environmental monitoring and ecological research. However, the inversion of Chla in different optical types of water bodies can only be achieved ...

Prediction of airborne bacterial concentrations and identification of critical factors in contaminated waste facilities: Insights into interpretable machine learning models.

Journal of hazardous materials
The efficient prediction of airborne bacterial concentrations is crucial for better understanding and management of environmental sanitation risks in waste facilities. Traditional linear models have proven inadequate in capturing the complex relation...

Machine learning based prediction by PlantCdMiner and experimental validation of cadmium-responsive genes in plants.

Journal of hazardous materials
Plants have evolved diverse adaptive mechanisms to sense and respond to environmental stimuli such as cadmium stress. The regulation of gene expression plays a critical role in plant responses to abiotic stress. However, homologous genes from differe...

Predicting estrogen receptor agonists from plastic additives across various aquatic-related species using machine learning and AlphaFold2.

Journal of hazardous materials
The absence of effective public databases greatly limits high-throughput prediction of hormonal effects mediated by nuclear receptors in aquatic organisms. In this study, we developed novel strategies for multi-species screening of estrogen receptor ...