AIMC Topic: Machine Learning

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Multi-component metabolite electrochemical detection and analysis based on machine learning.

Analytical methods : advancing methods and applications
Metabolic molecules are highly correlated with various physiological indicators and diseases, so it is particularly important to monitor the levels of multiple metabolites in the body. Due to the similar electrochemical properties of uric acid (UA), ...

Extensive novel diversity and phenotypic associations in the dromedary camel microbiome are revealed through deep metagenomics and machine learning.

PloS one
The dromedary camel, also known as one-humped camel or Arabian camel, is iconic and economically important to Arabian society. Its contemporary importance in commerce and transportation, along with the historical and modern use of its milk and meat p...

Use of hybrid quantum-classical algorithms for enhancing biomarker classification.

PloS one
Quantum machine learning (QML) combines quantum computing with machine learning, offering potential for solving intricate problems. Our research delves into QML's application in identifying gene expression biomarkers for clear cell renal cell carcino...

Discriminative graph regularized representation learning for recognition.

PloS one
Feature extraction has been extensively studied in the machine learning field as it plays a critical role in the success of various practical applications. To uncover compact low-dimensional feature representations with strong generalization and disc...

Rapid classification of bacteria by a portable Raman spectrometer and machine learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Efficient, accurate, and early identification of plant pathogens is crucial for reducing disease spread and ensuring food security. The development of rapid diagnostic methods based on Raman spectroscopy (RS) coupled with machine learning (ML) holds ...

Predicting treatment-seeking status for alcohol use disorder using polygenic scores and machine learning in a deeply-phenotyped sample.

Drug and alcohol dependence
BACKGROUND: Few individuals with alcohol use disorder (AUD) receive treatment. Previous studies have shown drinking behavior, psychological problems, and substance dependence to predict treatment seeking. However, to date, no studies have incorporate...

Machine learning identification of tinnitus-related features in auditory peripheral spontaneous activity in a guinea pig noise-induced tinnitus model.

Hearing research
OBJECTIVES: Tinnitus affects millions globally, yet its clinical assessment relies on subjective reports, limiting diagnostic accuracy and treatment development. This study aimed to identify objective, tinnitus-related features within ensemble sponta...

Leveraging unsupervised machine learning techniques for detecting outliers in the daily milk yield data of dairy cows.

Journal of dairy science
The lactation curve is essential for developing effective feeding plans, optimizing breeding, and strategizing milk production for dairy farms. However, health disorders, as well as external factors such as heat stress, dietary changes, and certain m...

Comparison of synthetic LGE with optimal inversion time vs. conventional LGE via representation learning: Quantification of Bias in Population Analysis.

Computers in biology and medicine
PURPOSE: Late Gadolinium Enhancement (LGE) images are crucial elements of CMR protocols for evaluating myocardial infarct (MI) severity and size. However, these images rely on signal intensity changes and manual inversion time (TI) settings, leading ...

Beyond traditional models: Jaya-optimized ensembles for accurate heart disease prediction.

Computers in biology and medicine
INTRODUCTION: Heart Disease (HD) stands as the foremost reason for mortality all over the world for both men and women. Millions of people are affected worldwide every year, resulting in numerous fatalities. Timely and precise detection is essential ...