idw – Informationsdienst Wissenschaft

Nachrichten, Termine, Experten

Grafik: idw-Logo
idw-Abo
Medienpartner:
Wissenschaftsjahr


Teilen: 
29.05.2019 16:40

New repository facilitates access to intelligent algorithms for genome research

Dr. Ulrich Marsch Corporate Communications Center
Technische Universität München

    In order to find out which genes are responsible for diseases such as cancer or diabetes, scientists nowadays frequently resort to using machine-learning models. In order to give clinical researchers access to the latest algorithms, Prof. Julien Gagneur from the Technical University of Munich (TUM) has set up a new repository called "Kipoi" in collaboration with scientists from other universities and research institutes.

    Although the importance of machine learning methods in genome research has grown steadily in recent years, researchers have often had to resort to using obsolete software. Scientists in clinical research often did not have access to the most recent models. This will change with the new free open access repository: Kipoi enables an easy exchange of machine learning models in the field of genome research. The repository was created by Julien Gagneur, Assistant Professor of Computational Biology at the TUM, in collaboration with researchers from the University of Cambridge, Stanford University, the European Bioinformatics Institute (EMBL-EBI) and the European Molecular Biology Laboratory (EMBL).

    Trained models freely available

    "What makes Kipoi special is that it provides free access to machine learning models that have already been trained," says Julien Gagneur. "What we are doing with Kipoi is not just sharing data and software, but sharing models and algorithms that are already trained on the most relevant data. These models are ready to use, because all the cumbersome work of applying them to data has already been done," says Anshul Kundaje, Assistant Professor at Stanford. More than 2,000 trained models are currently freely accessible on Kipoi. In a recent study published in Nature Biotechnology, the researchers show that the new repository will accelerate exchange in the genomics community and thereby advance genome research.

    Fast algorithms and easy operation

    Because Kipoi simplifies access to already trained models, researchers can perform transfer learning. This means that a model that has already been trained with a particular dataset is capable of learning a similar task faster. Kipoi also simplifies the process of feeding data into the models stored there: Standardized file formats and software frameworks reduce the installation and execution of a model to three simple commands. Those who previously had no experience in machine learning can thus also easily use the repository.

    Understanding individual genomes

    As Kipoi is oriented towards models that link genotype and phenotype, the new platform will make it easier to identify genetic causes of disease: "Kipoi puts the latest deep learning models trained on massive genomics data at the fingertips of clinical researchers," says Julien Gagneur. "This provides very exciting opportunities to understand individual genomes, for instance to pinpoint genetic variants causing diseases or to interpret mutations occurring in tumors.”

    However, the extent of the platform's contribution to genomic research will also depend on the genomics community. "We hope that in the future more researchers will bring their models to our repository," says Oliver Stegle, team leader at the EMBL-EBI. "That is the only way we can make genomics analysis accessible and ultimalely make a wider range of predictive machine learning tools available to the genomics community."


    Wissenschaftliche Ansprechpartner:

    Prof. Dr. Julien Gagneur
    Professor für Computational Biology
    Technical University of Munich
    Phone: +49 (89) 289 - 19411
    gagneur@in.tum.de


    Originalpublikation:

    Avsec, Z.; Gagneur, J.; Kundaje, A.; Stegle, O. et al. (2019). The Kipoi repository accelerates community exchange and reuse of predictive models for genomics. Nature Biotechnology.
    DOI: 10.1038/s41587-019-0140-0.
    Published online 28/05/2019: https://www.nature.com/articles/s41587-019-0140-0


    Weitere Informationen:

    https://kipoi.org/
    http://www.gagneurlab.in.tum.de/index.php?id=home
    https://www.professoren.tum.de/en/gagneur-julien/


    Merkmale dieser Pressemitteilung:
    Journalisten, Wissenschaftler
    Biologie, Informationstechnik, Mathematik, Medizin
    überregional
    Forschungs- / Wissenstransfer, Kooperationen
    Englisch


    Hilfe

    Die Suche / Erweiterte Suche im idw-Archiv
    Verknüpfungen

    Sie können Suchbegriffe mit und, oder und / oder nicht verknüpfen, z. B. Philo nicht logie.

    Klammern

    Verknüpfungen können Sie mit Klammern voneinander trennen, z. B. (Philo nicht logie) oder (Psycho und logie).

    Wortgruppen

    Zusammenhängende Worte werden als Wortgruppe gesucht, wenn Sie sie in Anführungsstriche setzen, z. B. „Bundesrepublik Deutschland“.

    Auswahlkriterien

    Die Erweiterte Suche können Sie auch nutzen, ohne Suchbegriffe einzugeben. Sie orientiert sich dann an den Kriterien, die Sie ausgewählt haben (z. B. nach dem Land oder dem Sachgebiet).

    Haben Sie in einer Kategorie kein Kriterium ausgewählt, wird die gesamte Kategorie durchsucht (z.B. alle Sachgebiete oder alle Länder).

    Cookies optimieren die Bereitstellung unserer Dienste. Durch das Weitersurfen auf idw-online.de erklären Sie sich mit der Verwendung von Cookies einverstanden. Datenschutzerklärung
    Okay