AIMC Topic: Machine Learning

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TEtrimmer: a tool to automate the manual curation of transposable elements.

Nature communications
Transposable elements (TEs) are repetitive DNA sequences that move within genomes and play important roles in gene regulation and genome evolution. Accurate TE annotation in genomesĀ is crucial for downstream analyses but challenging due to their sequ...

Predicting rhizosphere-competence-related catabolic gene clusters in plant-associated bacteria with rhizoSMASH.

Nature communications
Plants release a substantial fraction of their photosynthesized carbon into the rhizosphere as root exudates that drive microbiome assembly. Deciphering how plants modulate the composition and activities of rhizosphere microbiota through root exudate...

From reactive to proactive: Continuous protein monitoring for preventive health care.

Science (New York, N.Y.)
Continuous biomarker monitoring is revolutionizing chronic disease management, with glucose monitoring for diabetes as the primary example. Given the success of this approach, a transition to continuous protein monitoring (CPM, a real-time, implantab...

Predicting postoperative fever in culture-negative patients undergoing mini-PCNL using MAP score-augmented machine learning: a retrospective cohort study.

World journal of urology
PURPOSE: Postoperative fever is a common complication following percutaneous nephrolithotomy (PCNL) that occurs even in patients with sterile urine cultures. Traditional risk-assessment tools are insufficient in this subset of patients. This study ai...

Machine Learning-Based Classification of White Matter Functional Changes in Stroke Patients Using Resting-State fMRI.

Brain topography
Neuroimaging studies of brain function are important research methods widely applied to stroke patients. Currently, a large number of studies have focused on functional imaging of the gray matter cortex. Relevant research indicates that certain areas...

Machine learning-powered plasmonic pattern recognition: etch-suppressed gold nanorods for multiplex urinary analysis of catecholamine neurotransmitters.

Analytical methods : advancing methods and applications
Simultaneous monitoring of catecholamine neurotransmitters (CNTs)-including epinephrine (Epi), norepinephrine (NE), levodopa (L-DOPA), and dopamine (DA)-is essential for the accurate diagnosis and effective management of various neurological disorder...

Introduction of sub-band augmentation with machine learning to develop an insomnia classification model using single-channel EEG signals.

Physiological measurement
. Biological signals can be used to record sleep activities and can be used to identify sleep disorders. Insomnia is a sleep disorder that can be detected using supervised learning models developed using biological signal analysis. The baseline insom...

Predicting stock returns using machine learning combined with data envelopment analysis and automatic feature engineering: A case study on the Vietnamese stock market.

PloS one
In financial markets, predicting stock returns is an essential task for investors. This paper is one of the first studies using business efficiency scores calculated from data envelopment analysis to predict stock returns. In the meantime, this is al...

Equitable AI: Exploring the role of gender in poverty estimation models using geospatial data.

PloS one
Household surveys have been the foundation for poverty measurement in developing countries for the past half-century, but the spatial and temporal gaps in these survey data often limit how well anti-poverty programs can be targeted, monitored, or eva...

Latent representation of H&E images retains biological information in a breast cancer cohort.

PloS one
Imaging technologies and staining based pathology are important components of common practice cancer care. Specifically, H&E imaging is standard for almost all cancer patients. Traditionally, H&E images can serve, when used by experienced trained pat...