AIMC Topic: Machine Learning

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A novel way to use cross-validation to measure connectivity by machine learning allows epilepsy surgery outcome prediction.

NeuroImage
The rate of success of epilepsy surgery, ensuring seizure-freedom, is limited by the lack of epileptogenicity biomarkers. Previous evidence supports the critical role of functional connectivity during seizure generation to characterize the epileptoge...

Machine learning techniques for the prediction of indoor gamma-ray dose rates - Strengths, weaknesses and implications for epidemiology.

Journal of environmental radioactivity
We investigate methods that improve the estimation of indoor gamma ray dose rates at locations where measurements had not been made. These new predictions use a greater range of modelling techniques and larger variety of explanatory variables than ou...

Do circular innovations and carrying capacity of natural environment enhance circular economy in the European Union? Evidence from simulation and machine learning methods.

Journal of environmental management
The longevity of the natural environment is fundamental for sustainable production and consumption. The circular economy conserves natural resources, ensures sustainable production and consumption, and protects the natural environment. The role of ci...

Machine learning prediction model of the treatment response in schizophrenia reveals the importance of metabolic and subjective characteristics.

Schizophrenia research
Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique, we aimed to formulate a model predicting antipsychotic treatment outcomes. Data were obtained from...

Development of a Predictive Model of Occult Cancer After a Venous Thromboembolism Event Using Machine Learning: The CLOVER Study.

Medicina (Kaunas, Lithuania)
: Venous thromboembolism (VTE) can be the first manifestation of an underlying cancer. This study aimed to develop a predictive model to assess the risk of occult cancer between 30 days and 24 months after a venous thrombotic event using machine lear...

Prediction of Composite Clinical Outcomes for Childhood Neuroblastoma Using Multi-Omics Data and Machine Learning.

International journal of molecular sciences
Neuroblastoma is a common malignant tumor in childhood that seriously endangers the health and lives of children, making it essential to find effective prognostic markers to accurately predict their clinical outcomes. The development of high-throughp...

Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.

BMC cancer
Glioblastoma Multiforme (GBM), classified as a grade IV glioma by the World Health Organization (WHO), is a prevalent and notably aggressive form of brain tumor derived from glial cells. It stands as one of the most severe forms of primary brain canc...

Phenotype prediction in plants is improved by integrating large-scale transcriptomic datasets.

NAR genomics and bioinformatics
Research on the dynamic expression of genes in plants is important for understanding different biological processes. We used the large amounts of transcriptomic data from various plant sample sources that are publicly available to investigate whether...

BiGM-lncLoc: Bi-level Multi-Graph Meta-Learning for Predicting Cell-Specific Long Noncoding RNAs Subcellular Localization.

Interdisciplinary sciences, computational life sciences
The precise spatiotemporal expression of long noncoding RNAs (lncRNAs) plays a pivotal role in biological regulation, and aberrant expression of lncRNAs in different subcellular localizations has been intricately linked to the onset and progression o...

Predicting lack of clinical improvement following varicose vein ablation using machine learning.

Journal of vascular surgery. Venous and lymphatic disorders
OBJECTIVE: Varicose vein ablation is generally indicated in patients with active/healed venous ulcers. However, patient selection for intervention in individuals without venous ulcers is less clear. Tools that predict lack of clinical improvement (LC...