AIMC Topic: Machine Learning

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Machine Learning-Powered Optimization of a CHO Cell Cultivation Process.

Biotechnology and bioengineering
Chinese Hamster Ovary (CHO) cells are the most widely used cell lines to produce recombinant therapeutic proteins such as monoclonal antibodies (mAbs). However, the optimization of the CHO cell culture process is very complex and influenced by variou...

Knowledge graph applications and multi-relation learning for drug repurposing: A scoping review.

Computational biology and chemistry
OBJECTIVE: Development of novel drug solutions has always been an expensive endeavour, hence drug repurposing as an approach has gained popularity in recent years. In this review we intend to examine one of the most unique computational methods for d...

Utilising artificial intelligence in developing education of health sciences higher education: An umbrella review of reviews.

Nurse education today
OBJECTIVE: This umbrella review of reviews aims to synthesise current evidence on AI's utilisation in developing education within health sciences disciplines.

Machine learning model for predicting DIBH non-eligibility in left-sided breast cancer radiotherapy: Development, validation and clinical impact analysis.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
OBJECTIVE: Multi-day assessments accurately identify patients with left-sided breast cancer who are ineligible for irradiation in Deep Inspiration Breath Hold (DIBH) and minimise on-couch treatment time in those who are eligible. The challenge of imp...

Integrating neuroscience and artificial intelligence: EEG analysis using ensemble learning for diagnosis Alzheimer's disease and frontotemporal dementia.

Journal of neuroscience methods
BACKGROUND: Alzheimer's disease (AD) and frontotemporal dementia (FTD) are both progressive neurological disorders that affect the elderly. Distinguishing between individuals suffering from these two diseases in the early stages can be quite challeng...

Developing an interpretable machine learning model for diagnosing gout using clinical and ultrasound features.

European journal of radiology
OBJECTIVE: To develop a machine learning (ML) model using clinical data and ultrasound features for gout prediction, and apply SHapley Additive exPlanations (SHAP) for model interpretation.

Investigations into the Efficiency of Computer-Aided Synthesis Planning.

Journal of chemical information and modeling
The efficiency of machine learning (ML) models is crucial to minimize inference times and reduce the carbon footprints of models deployed in production environments. Current models employed in retrosynthesis to generate a synthesis route from a targe...

De Novo exposomic geospatial assembly of chronic disease regions with machine learning & network analysis.

EBioMedicine
BACKGROUND: Determining spatial relationships between diseases and the exposome is limited by available methodologies. aPEER (algorithm for Projection of Exposome and Epidemiological Relationships) uses machine learning (ML) and network analysis to f...

Assessing the impact of rainfall, topography, and human disturbances on nutrient levels using integrated machine learning and GAMs models in the Choctawhatchee River Watershed.

Journal of environmental management
Nutrient pollution caused by excessive total nitrogen (TN) and total phosphorus (TP) is a significant environmental challenge globally, threatening water quality and ecosystem health. This study investigates the interplay between rainfall, topography...

Personalized Cognitive Support via Social Robots.

Sensors (Basel, Switzerland)
This paper explores the use of personalized cognitive support through social robots to assist the elderly in maintaining cognitive health and emotional well-being. As aging populations grow, the demand for innovative solutions to address issues like ...