AIMC Topic: Machine Learning

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High-throughput screening, next generation sequencing and machine learning: advanced methods in enzyme engineering.

Chemical communications (Cambridge, England)
Enzyme engineering is an important biotechnological process capable of generating tailored biocatalysts for applications in industrial chemical conversion and biopharma. Typical enhancements sought in enzyme engineering and evolution campaigns inclu...

Machine Learning Identifies Pan-Cancer Landscape of Nrf2 Oxidative Stress Response Pathway-Related Genes.

Oxidative medicine and cellular longevity
BACKGROUND: Oxidative stress produced a large amount of reactive oxygen species (ROS), which played a pivotal role in balanced ability and determining cell fate. The activated Nrf2 signaling pathway that responds to the excessive ROS regulated the ex...

Machine learning-based prediction of critical illness in children visiting the emergency department.

PloS one
OBJECTIVES: Triage is an essential emergency department (ED) process designed to provide timely management depending on acuity and severity; however, the process may be inconsistent with clinical and hospitalization outcomes. Therefore, studies have ...

Associations Between Different Dietary Vitamins and the Risk of Obesity in Children and Adolescents: A Machine Learning Approach.

Frontiers in endocrinology
BACKGROUNDS: Simultaneous dietary intake of vitamins is considered as a common and real scenario in daily life. However, limited prospective studies have evaluated the association between multivitamins intake and obesity in children and adolescents.

Evaluating the robustness of targeted maximum likelihood estimators via realistic simulations in nutrition intervention trials.

Statistics in medicine
Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targe...

Receptor tyrosine kinase MET ligand-interaction classified via machine learning from single-particle tracking data.

Molecular biology of the cell
Internalin B-mediated activation of the membrane-bound receptor tyrosine kinase MET is accompanied by a change in receptor mobility. Conversely, it should be possible to infer from receptor mobility whether a cell has been treated with internalin B. ...

Applications of artificial intelligence and machine learning for the hip and knee surgeon: current state and implications for the future.

International orthopaedics
BACKGROUND: Artificial Intelligence (AI)/Machine Learning (ML) applications have been proven efficient to improve diagnosis, to stratify risk, and to predict outcomes in many respective medical specialties, including in orthopaedics.

Residue-Frustration-Based Prediction of Protein-Protein Interactions Using Machine Learning.

The journal of physical chemistry. B
The study of protein-protein interactions (PPIs) is important in understanding the function of proteins. However, it is still a challenge to investigate the transient protein-protein interaction by experiments. Hence, the computational prediction for...

Improving Radar Human Activity Classification Using Synthetic Data with Image Transformation.

Sensors (Basel, Switzerland)
Machine Learning (ML) methods have become state of the art in radar signal processing, particularly for classification tasks (e.g., of different human activities). Radar classification can be tedious to implement, though, due to the limited size and ...