AIMC Topic: Machine Learning

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Prediction of acute methanol poisoning prognosis using machine learning techniques.

Toxicology
Methanol poisoning is a global public health concern, especially prevalent in developing nations. This study focuses on predicting the severity of methanol intoxication using machine learning techniques, aiming to improve early identification and pro...

A novel method for multi-pollutant monitoring in water supply systems using chemical machine vision.

Environmental science and pollution research international
Drinking water is vital for human health and life, but detecting multiple contaminants in it is challenging. Traditional testing methods are both time-consuming and labor-intensive, lacking the ability to capture abrupt changes in water quality over ...

Benchmarking Active Learning Protocols for Ligand-Binding Affinity Prediction.

Journal of chemical information and modeling
Active learning (AL) has become a powerful tool in computational drug discovery, enabling the identification of top binders from vast molecular libraries. To design a robust AL protocol, it is important to understand the influence of AL parameters, a...

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States.

Environmental science & technology
Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dyna...

Simplex Lattice Design and Machine Learning Methods for the Optimization of Novel Microemulsion Systems to Enhance p-Coumaric Acid Oral Bioavailability: In Vitro and In Vivo Studies.

AAPS PharmSciTech
Novel p-coumaric acid microemulsion systems were developed to circumvent its absorption and bioavailability challenges. Simplex-lattice mixture design and machine learning methods were employed for optimization. Two optimized formulations were charac...

Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality.

NPJ systems biology and applications
Genome-scale metabolic models are powerful tools for understanding cellular physiology. Flux balance analysis (FBA), in particular, is an optimization-based approach widely employed for predicting metabolic phenotypes. In model microbes such as Esche...

An integrative machine learning model for the identification of tumor T-cell antigens.

Bio Systems
The escalating global incidence of cancer poses significant health challenges, underscoring the need for innovative and more efficacious treatments. Cancer immunotherapy, a promising approach leveraging the body's immune system against cancer, emerge...

A scoping review of fair machine learning techniques when using real-world data.

Journal of biomedical informatics
OBJECTIVE: The integration of artificial intelligence (AI) and machine learning (ML) in health care to aid clinical decisions is widespread. However, as AI and ML take important roles in health care, there are concerns about AI and ML associated fair...

Predicting the long-term collective behaviour of fish pairs with deep learning.

Journal of the Royal Society, Interface
Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess soc...

EHR-HGCN: An Enhanced Hybrid Approach for Text Classification Using Heterogeneous Graph Convolutional Networks in Electronic Health Records.

IEEE journal of biomedical and health informatics
Text classification is a central part of natural language processing, with important applications in understanding the knowledge behind biomedical texts including electronic health records (EHR). In this article, we propose a novel heterogeneous grap...