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 …

geoFOR: A collaborative forensic taphonomy database for estimating the postmortem interval

Accurately assessing the postmortem interval (PMI), or the time since death, remains elusive within forensic science research and application. This paper introduces geoFOR, a web-based collaborative application that utilizes ArcGIS and machine …

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 …