Machine Learning

ePlatypus: an ecosystem for computational analysis of immunogenomics data

Motivation The maturation of systems immunology methodologies requires novel and transparent computational frameworks capable of integrating diverse data modalities in a reproducible manner. Results Here, we present the ePlatypus computational …

Establishing trust in automated reasoning

Since its beginnings in the 1940s, automated reasoning by computers has become a tool of ever growing importance in scientific research.So far, the rules underlying automated reasoning have mainly beenformulated by humans, in the form of program …

Evaluating Content-Related Validity Evidence Using a Text-Based Machine Learning Procedure

Validity evidence based on test content is critical to meaningful interpretation of test scores. Within high-stakes testing and accountability frameworks, content-related validity evidence is typically gathered via alignment studies, with panels of …

Open Science Practices Need Substantial Improvement in Prognostic Model Studies in Oncology Using Machine Learning

Objective: To describe the frequency of open science practices in a contemporary sample of studies developing prognostic models using machine-learning methods in the field of oncology. Study design and setting: We conducted a systematic review, …

Preregistration of Machine Learning Research

It is interesting to note that human intelligence thrives on what Peirce called abductive inferences (Peirce and Turrisi 1997, 241-56), which are neither inductive nor deductive. Abductive inferencing basically entails an informed guess as to the …