The process of develo** predictive models includes many stages. Most resources focus
on the modeling algorithms but neglect other critical aspects of the modeling process. This …

An increasing number of model-agnostic interpretation techniques for machine learning
(ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) …

We established a genome-wide compendium of somatic mutation events in 3949 whole
cancer genomes representing 19 tumor types. Protein-coding events captured well …

Praise for the first edition:"[This book] succeeds singularly at providing a structured
introduction to this active field of research.… it is arguably the most accessible overview yet …

Scientific research of all kinds should be guided by statistical thinking: in the design and
conduct of the study, in the disciplined exploration and enlightened display of the data, and …

Network inference algorithms are valuable tools for the study of large-scale neuroimaging
datasets. Multivariate transfer entropy is well suited for this task, being a model-free measure …

Certain cell types function as factories, secreting large quantities of one or more proteins that
are central to the physiology of the respective organ. Examples include surfactant proteins in …

The standard approach to the analysis of genome-wide association studies (GWAS) is
based on testing each position in the genome individually for statistical significance of its …

Purpose The purpose of the study is to determine the clinically relevant accuracy of implant
placement in the edentulous maxilla using computer planning and a mucosa‐supported …

The familywise Type I error rate is a familiar concept in hypothesis testing, whereas the per‑
family Type I error rate is rarely addressed. This article uses Monte Carlo simulations and …