Dongyang Wang

Teams: Not specified

Organizations: Not specified

https://orcid.org/0000-0001-6440-6980
Volodymyr Savchenko project_administrator

Teams: ODA

Organizations: MMODA

https://orcid.org/0000-0001-6353-0808
Jeanette Reinshagen

Teams: EU-Openscreen

Organizations: Fraunhofer Institute for Translational Medicine and Pharmacology ITMP

https://orcid.org/0000-0002-8080-9170
Elida Schneltzer project_administrator

Teams: Applied Computational Biology at IEG/HMGU

Organizations: Helmholtz Zentrum München

https://orcid.org/0000-0003-4796-1661
Stephen Moss

Teams: Not specified

Organizations: Not specified

https://orcid.org/0000-0002-1399-293X
Pranavathiyani G project_administrator

Teams: Bioinformatics Innovation Lab

Organizations: Pondicherry University

https://orcid.org/0000-0003-4854-8238
Julie Ripoll

Teams: MAB - ATGC

Organizations: Centre National de la Recherche Scientifique (CNRS)

https://orcid.org/0000-0003-1590-8313
eric.rivals@lirmm.fr Rivals project_administrator

Teams: MAB - ATGC

Organizations: Centre National de la Recherche Scientifique (CNRS)

https://orcid.org/0000-0003-3791-3973
Laura Rodriguez-Navas project_administrator

Teams: GalaxyProject SARS-CoV-2, nf-core viralrecon, EOSC-Life - Demonstrator 7: Rare Diseases, iPC: individualizedPaediatricCure, EJPRD WP13 case-studies workflows, TransBioNet, OpenEBench, ELIXIR Proteomics

Organizations: Barcelona Supercomputing Center (BSC-CNS), ELIXIR

https://orcid.org/0000-0003-4929-1219
Evgenii Tretiakov project_administrator

Teams: Harkany Lab

Organizations: Medical University of Vienna

https://orcid.org/0000-0001-5920-2190
Jean-Loup Faulon

Teams: IBISBA Workflows

Organizations: INRAe

https://orcid.org/0000-0003-4274-2953
Perspectives on automated composition of workflows in the life sciences
FAIR Computational Workflows

Abstract (Expand)

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has …mposition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the “big picture” of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.

Authors: Anna-Lena Lamprecht, Magnus Palmblad, Jon Ison, Veit Schwämmle, Mohammad Sadnan Al Manir, Ilkay Altintas, Christopher J. O. Baker, Ammar Ben Hadj Amor, Salvador Capella-Gutierrez, Paulos Charonyktakis, Michael R. Crusoe, Yolanda Gil, Carole Goble, Timothy J. Griffin, Paul Groth, Hans Ienasescu, Pratik Jagtap, Matúš Kalaš, Vedran Kasalica, Alireza Khanteymoori, Tobias Kuhn, Hailiang Mei, Hervé Ménager, Steffen Möller, Robin A. Richardson, Vincent Robert, Stian Soiland-Reyes, Robert Stevens, Szoke Szaniszlo, Suzan Verberne, Aswin Verhoeven, Katherine Wolstencroft

Date Published: 2021

Publication Type: Journal

ABR Threshold Detection
Applied Computational Biology at IEG/HMGU

ABR_Threshold_Detection

What is this?

This code can be used to automatically determine hearing thresholds from ABR hearing curves.

One of the following methods can be used for this purpose:

  • neural network (NN) training,
  • calibration of a self-supervised sound level regression (SLR) method

on given data sets with manually determined hearing thresholds.

Installation:

Run inside the src directory:

Installation as python package

pip install -e ./src (Installation as python
...

Type: Python

Creators: None

Submitter: Elida Schneltzer

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