• Our aim is to advance our understanding of biological systems,

    ranging from single species to multi-species systems and ecosystems,

    based on data from large-scale bioanalytical methods.

  • We develop, improve and apply

    computational methods

    for the interpretation of molecular information in biology.

  • We establish and analyse

    quantitative mathematical models.

CUBE News

Latest publications

DeepNOG: fast and accurate protein orthologous group assignment

Roman Feldbauer, Lukas Gosch, Lukas Lüftinger, Patrick Hyden, Arthur Flexer, Thomas Rattei
2020 - Bioinformatics, in press

SciPy 1.0: fundamental algorithms for scientific computing in Python.

SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, van der Walt SJ, Brett M, Wilson J, Millman KJ, Mayorov N, Nelson ARJ, Jones E, Kern R, Larson E, Carey CJ, Polat İ, Feng Y, Moore EW, VanderPlas J, Laxalde D, Perktold J, Cimrman R, Henriksen I, Quintero EA, Harris CR, Archibald AM, Ribeiro AH, Pedregosa F, van Mulbregt P
2020 - Nat. Methods, in press

scikit-hubness: Hubness Reduction and Approximate Neighbor Search

scikit-hubness is a Python package for efficient nearest neighbor search in high-dimensional spaces. Hubness is an aspect of the curse of dimensionality in nearest neighbor graphs. Specifically, it describes the increasing occurrence of hubs and antihubs with growing data dimensionality: Hubs are objects, that appear unexpectedly often among the nearest neighbors of others objects, while antihubs are never retrieved as neighbors. As a consequence, hubs may propagate their information (for example, class labels) too widely within the neighbor graph, while information from antihubs is depleted. These semantically distorted graphs can reduce learning performance in various tasks, such as classification, clustering, or visualization. Hubness is known to affect a variety of applied learning systems, or improper transport mode detection.

Currently, there is a lack of fully-featured, up-to-date, user-friendly software dealing with hubness. Available packages miss critical features and have not been updated in years, or are not particularly user-friendly. In this paper we describe scikit-hubness, which provides powerful, readily available, and easy-to-use hubness-related methods.

Feldbauer R, Rattei T, Flexer A
2020 - The Journal of Open Source Software, 5: 1957