6 papers · ranked by Valyu relevance
Hannah Martinez, Nicole Guittari, Timothy Gion, Robert Hider Jr + 5 more
BossDB is a free and publicly accessible archive for storing and sharing petascale neuroimaging data. Focused on FAIR (i.e., findable, accessible, interoperable, and reusable) principles, it utilizes cloud-based infrastructure and software tools to facilitate data access and analysis. BossDB specializes in storing…
Alexander Alsalihi, Robert M. Flight, Hunter N. B. Moseley
The recount3 online resource provides tens of thousands of uniformly processed RNA-seq samples across human and mouse from major sequencing repositories like the Sequence Read Archive. While access to these datasets has traditionally been centered in the R/Bioconductor ecosystem, the growing prominence of Python in…
Saroja Somasundaram, Nelson J. Johansen, Trygve E. Bakken, Jeremy A. Miller
High-throughput chromatin accessibility assays such as ATAC-seq generate thousands of candidate regulatory elements (peaks), yet no standardized tool exists for assembling the diverse quantitative features needed to prioritize peaks for functional validation. Here we present PyPeakRankR, an open-source Python package…
Scott Huberty, James Desjardins, Tyler Collins, Mayada Elsabbagh + 1 more
EEG recordings are typically long and contain large amounts of data, making manual cleaning a time-consuming and error-prone task. Automated preprocessing pipelines can facilitate the efficient and objective extraction of artifacts, enabling standardized and reproducible analyses. However, automated preprocessing…
Jonas Engicht, R. Maximilian Bee, Tobias Koch
This paper introduces an open-source Python package for simplified, customizable computerized adaptive testing (CAT) using Bayesian methods for ability estimation. It addresses the lack of sophisticated packages for CAT in the Python programming language. Moreover, it bridges the gap between the construction and…
Ali Muzaffar, Hani Ragab Hassen, Hind Zantout, Michael A. Lones + 1 more
We report the findings of a reimplementation of 18 foundational studies in feature-based machine learning for Android malware detection, published during the period 2013-2023. These studies are reevaluated on a level playing field using a contemporary Android environment and a balanced dataset of 124,000 applications.…