AIMC Topic: Machine Learning

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Cell-Free Protein Synthesis as a Method to Rapidly Screen Machine Learning-Generated Protease Variants.

ACS synthetic biology
Machine learning (ML) tools have revolutionized protein structure prediction, engineering, and design, but the best ML tool is only as good as the training data it learns from. To obtain high-quality structural or functional data, protein purificatio...

Machine Learning-Enhanced Dual-Band Plasmonic Sensing for Simultaneous Qualitative and Quantitative Detection of Biomolecules in the Mid-Infrared Region.

Sensors (Basel, Switzerland)
Recently, sensing for biomolecules has become increasingly popular in the fields of environmental monitoring, personal health, and food safety. Plasmonic biosensors have been a powerful tool due to their high sensitivity and label-free operation. How...

Quantifying Area Back Scatter of Marine Organisms in the Arctic Ocean by Machine Learning-Based Post-Processing of Volume Back Scatter.

Sensors (Basel, Switzerland)
As the sea ice reduces in both extent and thickness and the Arctic Ocean opens, there is substantial interest in mapping the marine ecosystem in this remote and until now largely inaccessible ocean. We used the during surveys in the Central Arctic O...

Next-Level Prediction of Structural Progression in Knee Osteoarthritis: A Perspective.

International journal of molecular sciences
Osteoarthritis (OA) is a prevalent and disabling chronic disease, with knee OA being the most common form, affecting approximately 73% of individuals over 55 years. Traditional clinical assessments often fail to predict knee structural progression ac...

MixOmics Integration of Biological Datasets Identifies Highly Correlated Variables of COVID-19 Severity.

International journal of molecular sciences
Despite several years passing since the COVID-19 pandemic was declared, challenges remain in understanding the factors that can predict the severity of COVID-19 disease and complications of SARS-CoV-2 infection. While many large-scale multi-omic data...

Towards Skin Cancer Detection Through Low Resolution Images.

Studies in health technology and informatics
Currently, dermatologists need to check numerous image reports (high resolution) for diagnosing skin conditions, and Machine Learning (ML) models can help with this tedious task. However, current ML models usually work best with high-quality images i...

Assessment of Elapsed Time Between Dental Radiographs Using Siamese Network.

Studies in health technology and informatics
Recently, machine learning methods have emerged to predict dental disease progression, often relying on costly annotated datasets and frequently exhibiting low generalization performance. This study evaluates the application of Siamese networks for d...

Machine Learning and Urinary Incontinence in Prostate Cancer: A Generalized Additive Model of Physical Activity and Recovery Patterns.

Studies in health technology and informatics
The ASCAPE project aims to improve the health-related quality of life of prostate cancer patients using artificial intelligence-driven solutions. This study tries to unravel the complex relationships between patient data variables and urinary inconti...

Novel Tinnitus Diagnosis: Biology and Technology for Public Health Management.

Studies in health technology and informatics
Tinnitus, characterized by the perception of ringing or buzzing in the ears, significantly affects millions globally and negatively impacts their quality of life. Current management strategies vary in effectiveness, underscoring the need for precise,...

Towards Multiple Sclerosis Personalised Interventions Based on Real-World Predictive Analytics.

Studies in health technology and informatics
This study investigates the use of machine learning (ML) techniques to predict intervention response in patients with Multiple Sclerosis (PwMS) using real-world data from wearable devices. Data from 27 PwMS, monitored over two months were analyzed wi...