AIMC Topic: Algorithms

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Applicability Domain for Trustable Predictions.

Methods in molecular biology (Clifton, N.J.)
In the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML), understanding and correctly applying the concept of the applicability domain (AD) has emerged as an essential part. This chapter begins with an introduction ...

Generative Modeling of RNA Sequence Families with Restricted Boltzmann Machines.

Methods in molecular biology (Clifton, N.J.)
In this chapter, we discuss the potential application of Restricted Boltzmann machines (RBM) to model sequence families of structured RNA molecules. RBMs are a simple two-layer machine learning model able to capture intricate sequence dependencies in...

Machine Learning for RNA Design: LEARNA.

Methods in molecular biology (Clifton, N.J.)
Machine learning algorithms, and in particular deep learning approaches, have recently garnered attention in the field of molecular biology due to remarkable results. In this chapter, we describe machine learning approaches specifically developed for...

Development of machine learning-based quantitative structure-activity relationship models for predicting plasma half-lives of drugs in six common food animal species.

Toxicological sciences : an official journal of the Society of Toxicology
Plasma half-life is a crucial pharmacokinetic parameter for estimating extralabel withdrawal intervals of drugs to ensure the safety of food products derived from animals. This study focuses on developing a quantitative structure-activity relationshi...

Continuous glucose monitoring using machine learning models and IoT device data: A meta-analysis.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Machine learning offers diverse options for effectively managing blood glucose levels in diabetes patients. Selecting the right ML algorithm is critical given the array of available choices. Integrating data from IoT devices presents prom...

Revolutionizing health monitoring: Integrating transformer models with multi-head attention for precise human activity recognition using wearable devices.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: A daily activity routine is vital for overall health and well-being, supporting physical and mental fitness. Consistent physical activity is linked to a multitude of benefits for the body, mind, and emotions, playing a key role in raising...

Enhancing liver tumor segmentation with UNet-ResNet: Leveraging ResNet's power.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Liver cancer poses a significant health challenge due to its high incidence rates and complexities in detection and treatment. Accurate segmentation of liver tumors using medical imaging plays a crucial role in early diagnosis and treatme...

A Systematic Review on the Effectiveness of Machine Learning in the Detection of Atrial Fibrillation.

Current cardiology reviews
Recent endeavors have led to the exploration of Machine Learning (ML) to enhance the detection and accurate diagnosis of heart pathologies. This is due to the growing need to improve efficiency in diagnostics and hasten the process of delivering trea...

Democratizing Artificial Intelligence in Anatomic Pathology.

Archives of pathology & laboratory medicine
CONTEXT.—: Artificial intelligence is a transforming technology for anatomic pathology. Involvement within the workforce will foster support for algorithm development and implementation.

Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives.

Current medicinal chemistry
Gastric cancer (GC) represents a significant global health burden, ranking as the fifth most common malignancy and the fourth leading cause of cancer-related death worldwide. Despite recent advancements in GC treatment, the five-year survival rate fo...