AIMC Topic: Machine Learning

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Machine learning prediction of stalk lignin content using Fourier transform infrared spectroscopy in large scale maize germplasm.

International journal of biological macromolecules
Lignin has been recognized as a major factor contributing to lignocellulosic recalcitrance in biofuel production and attracted attentions as a high-value product in the biorefinery field. As the traditional wet chemical methods for detecting lignin c...

A machine learning approach to map the potential agroecological complexity in an indigenous community of Colombia.

Journal of environmental management
Agroecological systems are potential solutions to the environmental challenges of intensive agriculture. Indigenous communities, such as the Kamëntšá Biyá and Kamëntšá Inga from the Sibundoy Valley (SV) in Colombia, have their own ancient agroecologi...

COVID-19 from symptoms to prediction: A statistical and machine learning approach.

Computers in biology and medicine
During the COVID-19 pandemic, the analysis of patient data has become a cornerstone for developing effective public health strategies. This study leverages a dataset comprising over 10,000 anonymized patient records from various leading medical insti...

Polynomial-SHAP analysis of liver disease markers for capturing of complex feature interactions in machine learning models.

Computers in biology and medicine
Liver disease diagnosis is pivotal for effective patient management, and machine learning techniques have shown promise in this domain. In this study, we investigate the impact of Polynomial-SHapley Additive exPlanations analysis on enhancing the per...

Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signaling in a Model of Niemann-Pick Disease Type C.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle su...

Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights.

BMC neurology
Connectomics is a neuroscience paradigm focused on noninvasively mapping highly intricate and organized networks of neurons. The advent of neuroimaging has led to extensive mapping of the brain functional and structural connectome on a macroscale lev...

Visualization obesity risk prediction system based on machine learning.

Scientific reports
Obesity is closely associated with various chronic diseases.Therefore, accurate, reliable and cost-effective methods for preventing its occurrence and progression are required. In this study, we developed a visualized obesity risk prediction system b...

Capture of RNA-binding proteins across mouse tissues using HARD-AP.

Nature communications
RNA-binding proteins (RBPs) modulate all aspects of RNA metabolism, but a comprehensive picture of RBP expression across tissues is lacking. Here, we describe our development of the method we call HARD-AP that robustly retrieves RBPs and tightly asso...

CNVoyant a machine learning framework for accurate and explainable copy number variant classification.

Scientific reports
The precise classification of copy number variants (CNVs) presents a significant challenge in genomic medicine, primarily due to the complex nature of CNVs and their diverse impact on rare genetic diseases (RGDs). This complexity is compounded by the...

A machine learning-based Coagulation Risk Index predicts acute traumatic coagulopathy in bleeding trauma patients.

The journal of trauma and acute care surgery
BACKGROUND: Acute traumatic coagulopathy (ATC) is a well-described phenomenon known to begin shortly after injury. This has profound implications for resuscitation from hemorrhagic shock, as ATC is associated with increased risk for massive transfusi...