Reaching the end-game for GWAS: machine learning approaches for the prioritization of complex disease loci
Genome-wide association studies (GWAS) have revealed thousands of genetic loci that
underpin the complex biology of many human traits. However, the strength of GWAS–the …
underpin the complex biology of many human traits. However, the strength of GWAS–the …
Conditional permutation importance revisited
D Debeer, C Strobl - BMC bioinformatics, 2020 - Springer
Background Random forest based variable importance measures have become popular
tools for assessing the contributions of the predictor variables in a fitted random forest. In this …
tools for assessing the contributions of the predictor variables in a fitted random forest. In this …
Forest fire probability mapping in eastern Serbia: Logistic regression versus random forest method
Forest fire risk has increased globally during the previous decades. The Mediterranean
region is traditionally the most at risk in Europe, but continental countries like Serbia have …
region is traditionally the most at risk in Europe, but continental countries like Serbia have …
[HTML][HTML] An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing
E Izquierdo-Verdiguier, R Zurita-Milla - International Journal of Applied …, 2020 - Elsevier
New Earth observation missions and technologies are delivering large amounts of data.
Processing this data requires developing and evaluating novel dimensionality reduction …
Processing this data requires developing and evaluating novel dimensionality reduction …
Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone
K Kuzmin, AE Adeniyi, AK DaSouza Jr, D Lim… - Biochemical and …, 2020 - Elsevier
Coronaviruses infect many animals, including humans, due to interspecies transmission.
Three of the known human coronaviruses: MERS, SARS-CoV-1, and SARS-CoV-2, the …
Three of the known human coronaviruses: MERS, SARS-CoV-1, and SARS-CoV-2, the …
Lattice thermal conductivity prediction using symbolic regression and machine learning
Prediction models of lattice thermal conductivity (κL) have wide applications in the discovery
of thermoelectrics, thermal barrier coatings, and thermal management of semiconductors …
of thermoelectrics, thermal barrier coatings, and thermal management of semiconductors …
Single-cell classification using mass spectrometry through interpretable machine learning
The brain consists of organized ensembles of cells that exhibit distinct morphologies,
cellular connectivity, and dynamic biochemistries that control the executive functions of an …
cellular connectivity, and dynamic biochemistries that control the executive functions of an …
Development and validation of the gene expression predictor of high-grade serous ovarian carcinoma molecular SubTYPE (PrOTYPE)
Purpose: Gene expression–based molecular subtypes of high-grade serous tubo-ovarian
cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification …
cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification …
Polygenic risk scores outperform machine learning methods in predicting coronary artery disease status
D Gola, J Erdmann, B Müller‐Myhsok… - Genetic …, 2020 - Wiley Online Library
Coronary artery disease (CAD) is the leading global cause of mortality and has substantial
heritability with a polygenic architecture. Recent approaches of risk prediction were based …
heritability with a polygenic architecture. Recent approaches of risk prediction were based …
Statistical learning approaches in the genetic epidemiology of complex diseases
In this paper, we give an overview of methodological issues related to the use of statistical
learning approaches when analyzing high-dimensional genetic data. The focus is set on …
learning approaches when analyzing high-dimensional genetic data. The focus is set on …