F
Falk Schreiber
Researcher at University of Konstanz
Publications - 225
Citations - 8611
Falk Schreiber is an academic researcher from University of Konstanz. The author has contributed to research in topics: Biological network & Visualization. The author has an hindex of 45, co-authored 220 publications receiving 7611 citations. Previous affiliations of Falk Schreiber include Martin Luther University of Halle-Wittenberg & Monash University, Clayton campus.
Papers
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Journal ArticleDOI
The Systems Biology Graphical Notation
Nicolas Le Novère,Michael Hucka,Huaiyu Mi,Stuart L. Moodie,Falk Schreiber,Falk Schreiber,Anatoly Sorokin,Emek Demir,Katja Wegner,Mirit I. Aladjem,Sarala M. Wimalaratne,Frank T Bergman,Ralph Gauges,Peter Ghazal,Hideya Kawaji,Lu Li,Yukiko Matsuoka,Alice Villéger,Sarah Elizabeth Boyd,Laurence Calzone,Mélanie Courtot,Ugur Dogrusoz,Tom C. Freeman,Akira Funahashi,Samik Ghosh,Akiya Jouraku,Sohoung Kim,Fedor A. Kolpakov,Augustin Luna,Sven Sahle,Esther Schmidt,Steven Watterson,Steven Watterson,Guanming Wu,Igor Goryanin,Douglas B. Kell,Chris Sander,Herbert M. Sauro,Jacky L. Snoep,Kurt W. Kohn,Hiroaki Kitano +40 more
TL;DR: The Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists, believes that it will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge.
Journal ArticleDOI
VANTED: A system for advanced data analysis and visualization in the context of biological networks
TL;DR: VANTED greatly helps researchers in the analysis and interpretation of biochemical data, and thus is a useful tool for modern biological research.
Book
Analysis of Biological Networks
Björn H. Junker,Falk Schreiber +1 more
TL;DR: This course will review the state-of-the-art algorithms and analysis techniques in the field, and demonstrate their applications to the study of real biological networks.
Journal ArticleDOI
Centrality Analysis Methods for Biological Networks and Their Application to Gene Regulatory Networks
TL;DR: It is shown that common centrality measures result in different valuations of the vertices and that novel measures tailored to specific biological investigations are useful for the analysis of biological networks, in particular gene regulatory networks.
Journal ArticleDOI
HTPheno: An image analysis pipeline for high-throughput plant phenotyping
TL;DR: HTPheno, an open source image analysis pipeline, supplies a flexible and adaptable ImageJ plugin which can be used for automated image analysis in high-throughput plant phenotyping and therefore to derive new biological insights, such as determination of fitness.