Defining Data Science by a Data-Driven Quantification of the Community

Emmert‐Streib, Frank; Dehmer, Matthias

doi:10.3390/make1010015

Cited by 30 publications

(26 citation statements)

References 30 publications

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“…In contrast to classification methods, the output variables for regression models assume real values. Due to the fact that many application problems come in this form, regression models find widespread applications across many fields, e.g., [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

Emmert‐Streib

Dehmer

2019

MAKE

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110

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Regression models are a form of supervised learning methods that are important for machine learning, statistics, and general data science. Despite the fact that classical ordinary least squares (OLS) regression models have been known for a long time, in recent years there are many new developments that extend this model significantly. Above all, the least absolute shrinkage and selection operator (LASSO) model gained considerable interest. In this paper, we review general regression models with a focus on the LASSO and extensions thereof, including the adaptive LASSO, elastic net, and group LASSO. We discuss the regularization terms responsible for inducing coefficient shrinkage and variable selection leading to improved performance metrics of these regression models. This makes these modern, computational regression models valuable tools for analyzing high-dimensional problems.

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Section: Introductionmentioning

confidence: 99%

High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

Emmert‐Streib

Dehmer

2019

MAKE

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110

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“…This ensures the highest possible standards of the quality control service that is needed in a clinical context. This is necessary because the fast paced developments in cancer genomics and its data analysis processes require constantly novel solutions that are not available off-theshelf (Dunn and Bourne, 2017;Emmert-Streib and Dehmer, 2019). This is also in contrast to most biostatistics and computational biology departments at medical or clinical institutions, which serve frequently merely as facilities to provide support for other departments at the same institution.…”

Section: Demonstrated Expertise Of the Members Constituting The Extermentioning

confidence: 99%

Ensuring Quality Standards and Reproducible Research for Data Analysis Services in Oncology: A Cooperative Service Model

Emmert‐Streib¹,

Dehmer²,

Yli‐Harja³

2019

Front. Cell Dev. Biol.

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Modern molecular high-throughput devices, e.g., next-generation sequencing, have transformed medical research. Resulting data sets are usually high-dimensional on a genomic-scale providing multi-factorial information from intertwined molecular and cellular activities of genes and their products. This genomics-revolution installed precision medicine offering breathtaking opportunities for patient's diagnosis and treatment. However, due to the speed of these developments the quality standards of the involved data analyses are lacking behind, as exemplified by the infamous Duke Saga. In this paper, we argue in favor of a two-stage cooperative serve model that couples data generation and data analysis in the most beneficial way from the perspective of a patient to ensure data analysis quality standards including reproducible research.

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“…The second is from Cox who introduced what is nowadays called the Cox Proportional Hazard Model (CPHM), which is a regression model [11]. Interestingly, both models are heavily used to date and belong to the toolbox of every data scientist [12].…”

Section: Introductionmentioning

confidence: 99%

Introduction to Survival Analysis in Practice

Emmert‐Streib

Dehmer

2019

MAKE

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The modeling of time to event data is an important topic with many applications in diverse areas. The collective of methods to analyze such data are called survival analysis, event history analysis or duration analysis. Survival analysis is widely applicable because the definition of an ’event’ can be manifold and examples include death, graduation, purchase or bankruptcy. Hence, application areas range from medicine and sociology to marketing and economics. In this paper, we review the theoretical basics of survival analysis including estimators for survival and hazard functions. We discuss the Cox Proportional Hazard Model in detail and also approaches for testing the proportional hazard (PH) assumption. Furthermore, we discuss stratified Cox models for cases when the PH assumption does not hold. Our discussion is complemented with a worked example using the statistical programming language R to enable the practical application of the methodology.

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Defining Data Science by a Data-Driven Quantification of the Community

Cited by 30 publications

References 30 publications

High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

Ensuring Quality Standards and Reproducible Research for Data Analysis Services in Oncology: A Cooperative Service Model

Introduction to Survival Analysis in Practice

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