AIMC Topic: Machine Learning

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A statistical framework for non-negative matrix factorization based on generalized dual divergence.

Neural networks : the official journal of the International Neural Network Society
A statistical framework for non-negative matrix factorization based on generalized dual Kullback-Leibler divergence, which includes members of the exponential family of models, is proposed. A family of algorithms is developed using this framework, in...

Early identification of epilepsy surgery candidates: A multicenter, machine learning study.

Acta neurologica Scandinavica
OBJECTIVES: Epilepsy surgery is underutilized. Automating the identification of potential surgical candidates may facilitate earlier intervention. Our objective was to develop site-specific machine learning (ML) algorithms to identify candidates befo...

Epigenetic Target Fishing with Accurate Machine Learning Models.

Journal of medicinal chemistry
Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represe...

Reperfusion Therapy in Acute Ischemic Stroke with Active Cancer: A Meta-Analysis Aided by Machine Learning.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: While the prevalence of active cancer patients experiencing acute stroke is increasing, the effects of active cancer on reperfusion therapy outcomes are inconclusive. Thus, we aimed to compare the safety and outcomes of reperfusion therap...

A random forest method with feature selection for developing medical prediction models with clustered and longitudinal data.

Journal of biomedical informatics
BACKGROUND: Machine learning methodologies are gaining popularity for developing medical prediction models for datasets with a large number of predictors, particularly in the setting of clustered and longitudinal data. Binary Mixed Model (BiMM) fores...

Estimation of drug exposure by machine learning based on simulations from published pharmacokinetic models: The example of tacrolimus.

Pharmacological research
We previously demonstrated that Machine learning (ML) algorithms can accurately estimate drug area under the curve (AUC) of tacrolimus or mycophenolate mofetil (MMF) based on limited information, as well as or even better than maximum a posteriori Ba...

Artificial neural networks for multi-omics classifications of hepato-pancreato-biliary cancers: towards the clinical application of genetic data.

European journal of cancer (Oxford, England : 1990)
PURPOSE: Several multi-omics classifications have been proposed for hepato-pancreato-biliary (HPB) cancers, but these classifications have not proven their role in the clinical practice and been validated in external cohorts.

Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival.

Scientific reports
Cox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical met...

Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA.

Scientific reports
Type 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across spatial and temporal scales, attributable to variations of socioeconomic and lifestyle risk factors. Understanding these variations in risk factors contributio...

Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome.

Nature communications
The stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of drugs such as proton pump inhibitors (PPIs), or bacterial infection such as Helicobacter pylori, cause significant microbial alterations. Ye...