AIMC Topic: Machine Learning

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Machine learning for predicting phenotype from genotype and environment.

Current opinion in biotechnology
Predicting phenotype with genomic and environmental information is critically needed and challenging. Machine learning methods have emerged as powerful tools to make accurate predictions from large and complex biological data. Here, we review the pro...

Machine-Learning-Guided Discovery of Electrochemical Reactions.

Journal of the American Chemical Society
The molecular structures synthesizable by organic chemists dictate the molecular functions they can create. The invention and development of chemical reactions are thus critical for chemists to access new and desirable functional molecules in all dis...

Exploring Machine Learning-Based Fault Monitoring for Polymer-Based Additive Manufacturing: Challenges and Opportunities.

Sensors (Basel, Switzerland)
Three-dimensional printing, often known as additive manufacturing (AM), is a groundbreaking technique that enables rapid prototyping. Monitoring AM delivers benefits, as monitoring print quality can prevent waste and excess material costs. Machine le...

Path Generator with Unpaired Samples Employing Generative Adversarial Networks.

Sensors (Basel, Switzerland)
Interactive technologies such as augmented reality have grown in popularity, but specialized sensors and high computer power must be used to perceive and analyze the environment in order to obtain an immersive experience in real time. However, these ...

Machine learning modeling practices to support the principles of AI and ethics in nutrition research.

Nutrition & diabetes
BACKGROUND: Nutrition research is relying more on artificial intelligence and machine learning models to understand, diagnose, predict, and explain data. While artificial intelligence and machine learning models provide powerful modeling tools, failu...

BASIN: A Semi-automatic Workflow, with Machine Learning Segmentation, for Objective Statistical Analysis of Biomedical and Biofilm Image Datasets.

Journal of molecular biology
Micrograph comparison remains useful in bioscience. This technology provides researchers with a quick snapshot of experimental conditions. But sometimes a two- condition comparison relies on researchers' eyes to draw conclusions. Our Bioimage Analysi...

Differentiating dreaming and waking reports with automatic text analysis and Support Vector Machines.

Consciousness and cognition
We analyzed similarities and differences between dream and waking reports in a collection by Kahan and Sullivan (2012). We compared word frequencies in the reports using the automatic text analysis software, Linguistic Inquiry and Word Count (LIWC). ...

DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials.

The journal of physical chemistry. A
Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for different levels of QM methods, such as de...

Exposing the Limitations of Molecular Machine Learning with Activity Cliffs.

Journal of chemical information and modeling
Machine learning has become a crucial tool in drug discovery and chemistry at large, , to predict molecular properties, such as bioactivity, with high accuracy. However, activity cliffs─pairs of molecules that are highly similar in their structure bu...

Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series.

International journal of environmental research and public health
Tree-based machine learning methods have gained traction in the statistical and data science fields. They have been shown to provide better solutions to various research questions than traditional analysis approaches. To encourage the uptake of tree-...