What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
His primary areas of investigation include Data mining, Statistics, Metabolomics, Principal component analysis and Multivariate statistics.
His study in the fields of Data analysis under the domain of Data mining overlaps with other disciplines such as Set, Batch processing and Class differences.
His work on Resampling, Statistic and Latent variable model as part of general Statistics research is frequently linked to Control limits, bridging the gap between disciplines.
The concepts of his Metabolomics study are interwoven with issues in Gut flora, Data science and Human study.
His study with Principal component analysis involves better knowledge in Artificial intelligence.
His Multivariate statistics research includes elements of Interpretability, Partial least squares regression, Multilevel model and Multivariate analysis.
His most cited work include:
- Centering, scaling, and transformations: improving the biological information content of metabolomics data (1374 citations)
- Assessment of PLSDA cross validation (888 citations)
- Analysis of multiblock and hierarchical PCA and PLS models (537 citations)
What are the main themes of his work throughout his whole career to date?
Johan A. Westerhuis mainly focuses on Data mining, Metabolomics, Principal component analysis, Statistics and Artificial intelligence.
His study focuses on the intersection of Data mining and fields such as Data set with connections in the field of Model selection and Cross-validation.
The study incorporates disciplines such as Metabolite, Gut flora, Food science and Computational biology in addition to Metabolomics.
His Principal component analysis research also works with subjects such as
- Component analysis that intertwine with fields like Subspace topology and Data analysis,
- Algorithm and related Dynamic time warping.
He interconnects Feature selection and Scaling in the investigation of issues within Statistics.
His research integrates issues of Regression analysis, Machine learning and Pattern recognition in his study of Artificial intelligence.
He most often published in these fields:
- Data mining (26.22%)
- Metabolomics (30.49%)
- Principal component analysis (18.29%)
What were the highlights of his more recent work (between 2018-2021)?
- Data mining (26.22%)
- Principal component analysis (18.29%)
- Computational biology (8.54%)
In recent papers he was focusing on the following fields of study:
Johan A. Westerhuis mainly investigates Data mining, Principal component analysis, Computational biology, Sensor fusion and Multi omics.
His Data integration study, which is part of a larger body of work in Data mining, is frequently linked to Data type, bridging the gap between disciplines.
His Data integration research includes themes of Experimental data and Data analysis.
Sparse PCA is the focus of his Principal component analysis research.
His work carried out in the field of Computational biology brings together such families of science as RNA-Seq and Metabolomics.
His Sensor fusion research incorporates themes from Interval, Systems biology and Measure.
Between 2018 and 2021, his most popular works were:
- Variable selection and validation in multivariate modelling. (40 citations)
- Variable selection and validation in multivariate modelling. (40 citations)
- Harmonization of quality metrics and power calculation in multi-omic studies. (20 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Johan A. Westerhuis focuses on Data type, Data mining, Multivariate statistics, Feature selection and RNA-Seq.
You can notice a mix of various disciplines of study, such as Data integration, Statistical classification, Set, Field and Sample size determination, in his Data type studies.
His Data mining study combines topics in areas such as Design of experiments, Exponential family, Cross-validation, Model selection and Categorical variable.
His work deals with themes such as Multivariate analysis, Partial least squares regression, Variable elimination and Overfitting, which intersect with Multivariate statistics.
His Feature selection study is concerned with the larger field of Artificial intelligence.
His RNA-Seq study incorporates themes from Proteomics, Multi omics, Cellular differentiation and Metabolomics.
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