AIMC Topic: Machine Learning

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MRI radiomics for the prediction of recurrence in patients with clinically non-functioning pituitary macroadenomas.

Computers in biology and medicine
Twelve to 66% of patients with clinically non-functioning pituitary adenoma (NFPA) experience tumor recurrence 1-5 years after the first surgery. Nevertheless, there is still no recurrence prediction factor concisely established and reproduced in the...

Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.

International journal of environmental research and public health
Suicide is a leading cause of death that defies prediction and challenges prevention efforts worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means of investigating large datasets to enhance risk detection. A system...

Detecting asthma exacerbations using daily home monitoring and machine learning.

The Journal of asthma : official journal of the Association for the Care of Asthma
OBJECTIVE: Acute exacerbations contribute significantly to the morbidity of asthma. Recent studies have shown that early detection and treatment of asthma exacerbations leads to improved outcomes. We aimed to develop a machine learning algorithm to d...

Reconfiguration of αmplitude driven dominant coupling modes (DoCM) mediated by α-band in adolescents with schizophrenia spectrum disorders.

Progress in neuro-psychopharmacology & biological psychiatry
Electroencephalography (EEG) based biomarkers have been shown to correlate with the presence of psychotic disorders. Increased delta and decreased alpha power in psychosis indicate an abnormal arousal state. We investigated brain activity across the ...

Machine Learning in Laryngoscopy Analysis: A Proof of Concept Observational Study for the Identification of Post-Extubation Ulcerations and Granulomas.

The Annals of otology, rhinology, and laryngology
OBJECTIVE: Computer-aided analysis of laryngoscopy images has potential to add objectivity to subjective evaluations. Automated classification of biomedical images is extremely challenging due to the precision required and the limited amount of annot...

m7GPredictor: An improved machine learning-based model for predicting internal m7G modifications using sequence properties.

Analytical biochemistry
As one of the most important post-transcriptional modifications, the N7-methylguanosine (m7G) plays a key role in many RNA processing events. The accurate identification of m7G is crucial for elucidating its biological significance and future applica...

Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China.

Environmental pollution (Barking, Essex : 1987)
In arid and semi-arid regions, water-quality problems are crucial to local social demand and human well-being. However, the conventional remote sensing-based direct detection of water quality parameters, especially using spectral reflectance of water...

Predicting the progression of mild cognitive impairment to Alzheimer's disease by longitudinal magnetic resonance imaging-based dictionary learning.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Efficient prediction of the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is important for the early intervention and management of AD. The aim of our study was to develop a longitudinal structural magnetic res...

Prediction of Neuropeptides from Sequence Information Using Ensemble Classifier and Hybrid Features.

Journal of proteome research
As hormones in the endocrine system and neurotransmitters in the immune system, neuropeptides (NPs) provide many opportunities for the discovery of new drugs and targets for nervous system disorders. In spite of their importance in the hormonal regul...

Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies.

The journal of physical chemistry letters
The current pandemic demands a search for therapeutic agents against the novel coronavirus SARS-CoV-2. Here, we present an efficient computational strategy that combines machine learning (ML)-based models and high-fidelity ensemble docking studies to...