Computational and Mathematical Methods in Medicine

Predictive Modelling Based on Statistical Learning in Biomedicine


Status
Published

Lead Editor

1Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

2Paul-Ehrlich-Institut, Langen, Germany

3Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany


Predictive Modelling Based on Statistical Learning in Biomedicine

Description

During recent years, considerable research has been devoted to exploring the combination of state-of-the-art statistical methodology with machine-learning techniques. This framework, often referred to as statistical learning, provides advantages for biomedical research, particularly regarding data situations frequently encountered in modern biomedical research characterized by large numbers of potential features or variables. In such situations, it is often the primary aim to obtain sparse and explanatory models, which can be generalized effectively. When techniques of statistical learning are applied to these problems, interpretable prediction rules leading to accurate forecasts for future or unseen observations can be deduced from potentially high-dimensional data.

This special issue aims to collect cutting-edge research and novel approaches emerging from statistical computing for applications in biomedicine. The purpose of the issue is to cover a broad range of methodological contributions regarding different types of algorithms and fields of biomedical application. The focus lies in providing deeper insights into the potentials of combining statistical modelling with machine learning to ultimately find interpretable prediction rules for biomedical applications.

Therefore, we invite researchers to contribute original research articles related to new methodologies and state-of-the-art applications in the field of predictive modelling based on statistical learning. We encourage authors to support reproducible research by submitting manuscripts accompanied by corresponding computer code that supports their conclusions. Purely theoretical articles without proper applied motivation and/or lacking biomedical applications are discouraged.

Potential topics include but are not limited to the following:

  • Variable or feature selection techniques for potentially high-dimensional data and their impact on prediction accuracy
  • Innovative model classes providing deeper insights into complex data structures
  • Techniques to improve run-time efficiency, decrease memory demand, or enhance tuning of statistical learning algorithms
  • New methodology to evaluate prediction accuracy or the performance of statistical learning algorithms
  • Innovative worked-out applications of predictive modelling based on statistical learning in biomedical research

Articles

  • Special Issue
  • - Volume 2017
  • - Article ID 4041736
  • - Editorial

Predictive Modelling Based on Statistical Learning in Biomedicine

Olaf Gefeller | Benjamin Hofner | ... | Elisabeth Waldmann
  • Special Issue
  • - Volume 2017
  • - Article ID 7847531
  • - Research Article

Correcting Classifiers for Sample Selection Bias in Two-Phase Case-Control Studies

Norbert Krautenbacher | Fabian J. Theis | Christiane Fuchs
  • Special Issue
  • - Volume 2017
  • - Article ID 6083072
  • - Review Article

An Update on Statistical Boosting in Biomedicine

Andreas Mayr | Benjamin Hofner | ... | Olaf Gefeller
  • Special Issue
  • - Volume 2017
  • - Article ID 7907163
  • - Research Article

A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data

Andrea Bommert | Jörg Rahnenführer | Michel Lang
  • Special Issue
  • - Volume 2017
  • - Article ID 1421409
  • - Research Article

Probing for Sparse and Fast Variable Selection with Model-Based Boosting

Janek Thomas | Tobias Hepp | ... | Bernd Bischl
  • Special Issue
  • - Volume 2017
  • - Article ID 7340565
  • - Research Article

Integration of Multiple Genomic Data Sources in a Bayesian Cox Model for Variable Selection and Prediction

Tabea Treppmann | Katja Ickstadt | Manuela Zucknick
  • Special Issue
  • - Volume 2017
  • - Article ID 6742763
  • - Research Article

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies

Stefanie Friedrichs | Juliane Manitz | ... | Benjamin Hofner
  • Special Issue
  • - Volume 2017
  • - Article ID 5271091
  • - Research Article

Nonparametric Subgroup Identification by PRIM and CART: A Simulation and Application Study

Armin Ott | Alexander Hapfelmeier
  • Special Issue
  • - Volume 2017
  • - Article ID 7691937
  • - Research Article

IPF-LASSO: Integrative -Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data

Anne-Laure Boulesteix | Riccardo De Bin | ... | Mathias Fuchs
  • Special Issue
  • - Volume 2017
  • - Article ID 4201984
  • - Research Article

Dysphonic Voice Pattern Analysis of Patients in Parkinson’s Disease Using Minimum Interclass Probability Risk Feature Selection and Bagging Ensemble Learning Methods

Yunfeng Wu | Pinnan Chen | ... | Jian Chen
Computational and Mathematical Methods in Medicine
 Journal metrics
Acceptance rate28%
Submission to final decision103 days
Acceptance to publication41 days
CiteScore1.840
Impact Factor1.563
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