Biology subjects

Jadebeck, J. F.

Publications and source records attributed to Jadebeck, J. F..

3 recordsLinked to original sources

bioRxiv2024

MooViE - Engine for single-view visual analysis of multivariate data

SummaryUnderstanding input-output relationships within multivariate datasets is an ubiquitous task in the life and data sciences. For this, visual analysis is indispensable for providing expressive summaries and preparing decision-making. We present the visual analysis approach and software MooViE, which is designed to strike the balance between being tailored to the specific data semantic and while being broadly applicable. MooViE supports the data exploration process for extracting important information from the data and captures the result in a fresh single-view visualization. MooViE is implemented in C++ to facilitate fast access and effective interaction with comprehensive multivariate datasets. We showcase the engine for various application fields, relevant to the life sciences. Availability and ImplementationThe source code is available under MIT license at https://jugit.fz-juelich.de/IBG-1/ModSim/MooViE and https://github.com/JuBiotechMooViE, with detailed documentation and usage instructions (https://moovie.readthedocs.io), as well as zenodo-archived releases (https://doi.org/10.5281/zenodo.10997388). Platform independent Docker images are also available (jugit-registry.fz-juelich.de/ibg-1/modsim/moovie/moovie). ContactKatharina Noh k.noeh@fz-juelich.de

bioinformatics↗

bioRxiv2023

hopsy - a methods marketplace for convex polytope sampling in Python

SummaryEffective collaboration between developers of Bayesian inference methods and users is key to advance our quantitative understanding of biosystems. We here present hopsy, a versatile open source platform designed to provide convenient access to powerful Markov chain Monte Carlo sampling algorithms tailored to models defined on convex polytopes (CP). Based on the high-performance C++ sampling library HOPS, hopsy inherits its strengths and extends its functionalities with the accessibility of the Python programming language. A versatile plugin-mechanism enables seamless integration with domain-specific models, providing method developers with a framework for testing, benchmarking, and distributing CP samplers to approach real-world inference tasks. We showcase hopsy by solving common and newly composed domain-specific sampling problems, highlighting important design choices. By likening hopsy to a marketplace, we emphasize its role in bringing together users and developers, where users get access to state-of-the-art methods, and developers contribute their own innovative solutions for challenging domain-specific inference problems. Availability and ImplementationSources, documentation and a continuously updated list of sampling algorithms are available at https://jugit.fz-juelich.de/IBG-1/ModSim/hopsy, with Linux, Windows and MacOS binaries at https://pypi.org/project/hopsy/. Contactk.noeh@fz-juelich.de

systems biology↗

bioRxiv2022

CHRRT: boosting coordinate hit-and-run with rounding by thinning

Thinning is a sub-sampling technique to reduce the memory footprint of Markov chain Monte Carlo. Despite being commonly used, thinning is rarely considered efficient. For sampling convex polytopes uniformly, a highly relevant use-case in systems biology, we here demonstrate that thinning generally boosts computational and, thereby, sampling efficiencies of the widely used Coordinate Hit-and-Run with Rounding (CHRR) algorithm. We benchmark CHRR with thinning (CHRRT) with simplices and constrained-based metabolic networks with up to thousands of dimensions. With appropriate thinning, CHRRT offers a substantial increase in computational efficiency compared to unthinned CHRR, in our examples of up to three orders of magnitude, as measured by the effective sample size per time (ESS/t). Our experiments reveal that the performance gain of CHRRT by optimal thinning grows substantially with polytope (effective model) dimension. Based on our experiments, we provide practically useful advice for tuning thinning to efficient and effective use of compute resources. Besides allocating computational resources optimally to permit sampling convex polytopes uniformly to convergence in a fraction of time, exploiting thinning unlocks investigating hitherto intractable models under limited computational budgets. CHRRT thereby paves the way to keep pace with progressing model sizes within the existing constraint-based reconstruction and analysis (COBRA) tool set. Sampling and evaluation pipelines are available at https://jugit.fz-juelich.de/IBG-1/ModSim/fluxomics/chrrt.

systems biology↗