AIMC Topic: Machine Learning

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Predicting electrical storms by remote monitoring of implantable cardioverter-defibrillator patients using machine learning.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
AIMS: Electrical storm (ES) is a serious arrhythmic syndrome that is characterized by recurrent episodes of ventricular arrhythmias. Electrical storm is associated with increased mortality and morbidity despite the use of implantable cardioverter-def...

Localizing and Classifying Adaptive Targets with Trend Filtered Regression.

Molecular biology and evolution
Identifying genomic locations of natural selection from sequence data is an ongoing challenge in population genetics. Current methods utilizing information combined from several summary statistics typically assume no correlation of summary statistics...

Causal models adjusting for time-varying confounding-a systematic review of the literature.

International journal of epidemiology
BACKGROUND: Obtaining unbiased causal estimates from longitudinal observational data can be difficult due to exposure-affected time-varying confounding. The past decade has seen considerable development in methods for analysing such complex longitudi...

BIPSPI: a method for the prediction of partner-specific protein-protein interfaces.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-Protein Interactions (PPI) are essentials for most cellular processes and thus, unveiling how proteins interact is a crucial question that can be better understood by identifying which residues are responsible for the interaction....

Automatic recognition of ligands in electron density by machine learning.

Bioinformatics (Oxford, England)
MOTIVATION: The correct identification of ligands in crystal structures of protein complexes is the cornerstone of structure-guided drug design. However, cognitive bias can sometimes mislead investigators into modeling fictitious compounds without so...

An account of in silico identification tools of secreted effector proteins in bacteria and future challenges.

Briefings in bioinformatics
Bacterial pathogens secrete numerous effector proteins via six secretion systems, type I to type VI secretion systems, to adapt to new environments or to promote virulence by bacterium-host interactions. Many computational approaches have been used i...

Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models.

Briefings in bioinformatics
Timely identification of adverse drug reactions (ADRs) is highly important in the domains of public health and pharmacology. Early discovery of potential ADRs can limit their effect on patient lives and also make drug development pipelines more robus...

Predictive analysis across spatial scales links zoonotic malaria to deforestation.

Proceedings. Biological sciences
The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria ( Plasmodium knowlesi) is associated with deforestation although mechani...