Wie die Bioinformatik hilft, Sprachgeschichte zu rekonstruieren
In: Grenzüberschreitungen
In: Grenzüberschreitungen
In: Empfehlungen des BioÖkonomieRats [6]
Unter Hochdruck wird auf der ganzen Welt nach einem Impfstoff gegen das neue Corona-Virus gesucht. Die Bioinformatik spielt dabei eine große Rolle. Denn auch die molekulare Struktur eines Virus lässt sich in einem präzisen Sinn als eine informationsverarbeitende Maschine verstehen, die auf einem Computer simuliert werden kann. In einem neuen Kapitel zu "Bioinformatik – Schlüssel zum Kode des Lebens" zeigt Klaus Mainzer, dass durch eine Zusammenführung von Bioinformatik, Machine Learning, KI-Forschung und Big Data die Frage, wie Algorithmen helfen können, Sars-CoV-2 zu entschlüsseln und auszuschalten und – darüber hinaus – auch die evolutionären Gesetze erkannt werden können, nach denen Viren mutieren: So könnte es mittelfristig gelingen, kommende Pandemien zu antizipieren und gleich bei ihrem Auftreten zu bekämpfen. Mit einem solchen Forschungsprogramm sind auch wichtige Fragen von Ethik und Recht berührt: Wie bleiben wir Menschen Maßstab der Technik?
Der Aufbau einer Bioinformatik-Infrastruktur in Deutschland wird vom BMBF seit dem Jahr 2015 betrieben. Dazu wurde die Fördermaßnahme "Deutsches Netzwerk für Bioinformatik-Infrastruktur (de.NBI)" (www.denbi.de) ins Leben gerufen mit der Aufgabe, Forschenden in den Lebenswissenschaften die Analyse großer Datenmengen zu ermöglichen. Nach Einrichtung der Infrastrukturbereiche Serviceangebote, Trainingskurse und Cloud-Computing steht nun eine Verstetigung dieser Bioinformatik-Infrastruktur durch Integration in die Helmholtz-Gemeinschaft an. Die Bundesregierung hat hierzu in ihrer jüngsten Finanzplanung dem Forschungszentrum Jülich Finanzmittel zur Verfügung gestellt, um die etablierte Bioinformatik-Infrastruktur langfristig zu betreiben.
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In: Berichte aus der Medizinischen Informatik und Bioinformatik
In: Berichte aus der medizinischen Informatik und Bioinformatik
Die Methoden des maschinellen Lernens erweisen sich nicht nur in der Bioinformatik als sehr effektiv, sondern auch in anderen Bereichen, vor allem wegen ihrer Universalität. Im Falle des überwachten Lernens haben alle Ansätze eines gemeinsam: Es wird versucht, anhand von Daten bestimmte, generalisierbare Verteilungseigenschaften zu lernen (Training), um somit auch auf ungesehenen Daten zuverlässige Vorhersagen treffen zu können. Ein weiteres Modell im Bereich des maschinellen Lernens ist das Konzept-Lernen (hier PAC-Lernen). In dieser Dissertation wird im Rahmen des PAC-Lernens eine für die Praxis relevante Konzeptklasse und die dafür notwendigen Lernalgorithmen entwickelt und analysiert. Das PAC-Lernen wird in zwei verschiedenen Anwendungsbereichen durch einen Vergleich gegenüber bewährten Verfahren validiert, zum einen in der Betriebswirtschaft, zur Insolvenzvorhersage und zum anderen in der Bioinformatik, zur Erkennung von Hotspots in Protein-Protein-Wechselwirkungen ...
Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings. ; Funding Agencies|European Research Council (ERC) under the European UnionEuropean Research Council (ERC) [694665]; French government, through the 3IA Cote DAzur Investments in the Future project [ANR-19-P3IA-0002]; EPSRCUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/N018702/1, MR/T020296/1, ISLRA-2009]; European Space AgencyEuropean Space AgencyEuropean Commission; Belgian Science Policy Office-ProdexBelgian Federal Science Policy Office; Research Foundation Flanders (FWO Vlaanderen)FWO [12M3119N, G0D7216N]; Wellcome Trust Investigator AwardWellcome Trust [096646/Z/11/Z]; Wellcome Trust Strategic AwardWellcome Trust [104943/Z/14/Z]; Polish National Agency for Academic ExchangePolish National Agency for Academic Exchange (NAWA) [PN/BEK/2019/1/00421]; Ministry of Science and Higher Education (Poland)Ministry of Science and Higher Education, Poland [692/STYP/13/2018]; AGH Science and Technology, Poland [16.16.120.773]; Linkoping University (LiU) Center for Industrial Information Technology (CENIIT); LiU Cancer [VINNOVA/ITEA3 17021 IMPACT]; Swedish Foundation for Strategic ResearchSwedish Foundation for Strategic Research [RMX18-0056]; "la Caixa" FoundationLa Caixa Foundation [100010434]; European UnionEuropean Commission [847648, LCF/BQ/PI20/11760029]; Ministerio de Ciencia e Innovacion" of SpainSpanish Government [RTI2018-094569-B-I00]; National Institute for Biomedical Imaging [5R01EB027585-02]
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This comprehensively revised second edition of Computational Systems Biology discusses the experimental and theoretical foundations of the function of biological systems at the molecular, cellular or organismal level over temporal and spatial scales, as systems biology advances to provide clinical solutions to complex medical problems. In particular the work focuses on the engineering of biological systems and network modeling. Logical information flow aids understanding of basic building blocks of life through disease phenotypes
In: Cancer Drug Discovery and Development
Bioinformatics can be loosely defined as the collection, classification, storage, and analysis of biochemical and biological information using computers and mathematical algorithms. Bioinformatics represents a marriage of biology, medicine, computer science, physics, and mathematics, fields of study that have historically existed as mutually exclusive disciplines. Bioinformatics in Cancer and Cancer Therapy, edited by Gavin Gordon, provides an historical and technical perspective on the analytical techniques, methodologies, and platforms used in bioinformatics experiments in order to show how a bioinformatics approach has been used to characterize various cancer-related processes, and to demonstrate how a bioinformatics approach is being used to bridge basic science and the clinical arena to positively impact patient care and management.
A prominent aspect of most, if not all, central nervous systems (CNSs) is that anterior regions (brain) are larger than posterior ones (spinal cord). Studies in Drosophila and mouse have revealed that Polycomb Repressor Complex 2 (PRC2), a protein complex responsible for applying key repressive histone modifications, acts by several mechanisms to promote anterior CNS expansion. However, it is unclear what the full spectrum of PRC2 action is during embryonic CNS development and how PRC2 intersects with the epigenetic landscape. We removed PRC2 function from the developing mouse CNS, by mutating the key gene Eed, and generated spatio-temporal transcriptomic data. To decode the role of PRC2, we developed a method that incorporates standard statistical analyses with probabilistic deep learning to integrate the transcriptomic response to PRC2 inactivation with epigenetic data. This multi-variate analysis corroborates the central involvement of PRC2 in anterior CNS expansion, and also identifies several unanticipated cohorts of genes, such as proliferation and immune response genes. Furthermore, the analysis reveals specific profiles of regulation via PRC2 upon these gene cohorts. These findings uncover a differential logic for the role of PRC2 upon functionally distinct gene cohorts that drive CNS anterior expansion. To support the analysis of emerging multi-modal datasets, we provide a novel bioinformatics package that integrates transcriptomic and epigenetic datasets to identify regulatory underpinnings of heterogeneous biological processes. ; Funding Agencies|Swedish Research CouncilSwedish Research CouncilEuropean Commission [621-2013-5258]; Knut and Alice Wallenberg FoundationKnut & Alice Wallenberg Foundation [KAW2011.0165, KAW2012.0101]; Swedish Cancer Foundation [140780, 150663]; University of QueenslandUniversity of Queensland; Australian Government Research Training ProgramAustralian Government
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