A review on longitudinal data analysis with random forest
J Hu, S Szymczak - Briefings in Bioinformatics, 2023 - academic.oup.com
In longitudinal studies variables are measured repeatedly over time, leading to clustered
and correlated observations. If the goal of the study is to develop prediction models …
and correlated observations. If the goal of the study is to develop prediction models …
Travelling the world of gene–gene interactions
K Van Steen - Briefings in bioinformatics, 2012 - academic.oup.com
Over the last few years, main effect genetic association analysis has proven to be a
successful tool to unravel genetic risk components to a variety of complex diseases. In the …
successful tool to unravel genetic risk components to a variety of complex diseases. In the …
Why hate carbon taxes? Machine learning evidence on the roles of personal responsibility, trust, revenue recycling, and other factors across 23 European countries
S Levi - Energy Research & Social Science, 2021 - Elsevier
Carbon taxes are considered a key instrument for achieving deep decarbonization but are
often unpopular among voters. While existing studies indicate that public opposition to …
often unpopular among voters. While existing studies indicate that public opposition to …
Random forest for bioinformatics
Y Qi - Ensemble machine learning: Methods and applications, 2012 - Springer
Modern biology has experienced an increased use of machine learning techniques for large
scale and complex biological data analysis. In the area of Bioinformatics, the Random Forest …
scale and complex biological data analysis. In the area of Bioinformatics, the Random Forest …
Integrating abundance and functional traits reveals new global hotspots of fish diversity
Species richness has dominated our view of global biodiversity patterns for centuries,. The
dominance of this paradigm is reflected in the focus by ecologists and conservation …
dominance of this paradigm is reflected in the focus by ecologists and conservation …
Radiomics of brain MRI: utility in prediction of metastatic tumor type
HC Kniep, F Madesta, T Schneider, U Hanning… - Radiology, 2019 - pubs.rsna.org
Purpose To investigate the feasibility of tumor type prediction with MRI radiomic image
features of different brain metastases in a multiclass machine learning approach for patients …
features of different brain metastases in a multiclass machine learning approach for patients …
Statistical solutions for error and bias in global citizen science datasets
Networks of citizen scientists (CS) have the potential to observe biodiversity and species
distributions at global scales. Yet the adoption of such datasets in conservation science may …
distributions at global scales. Yet the adoption of such datasets in conservation science may …
Mixed-effects random forest for clustered data
This paper presents an extension of the random forest (RF) method to the case of clustered
data. The proposed 'mixed-effects random forest'(MERF) is implemented using a standard …
data. The proposed 'mixed-effects random forest'(MERF) is implemented using a standard …
Bacterial profile of dentine caries and the impact of pH on bacterial population diversity
Dental caries is caused by the release of organic acids from fermentative bacteria, which
results in the dissolution of hydroxyapatite matrices of enamel and dentine. While low …
results in the dissolution of hydroxyapatite matrices of enamel and dentine. While low …
Addressing voice recording replications for Parkinson's disease detection
A clinical expert system has been developed for detection of Parkinson's Disease (PD). The
system extracts features from voice recordings and considers an advanced statistical …
system extracts features from voice recordings and considers an advanced statistical …