TY - CHAP A1 - Lausser, Ludwig A1 - Szekely, Robin A1 - Kessler, Viktor A1 - Schwenker, Friedhelm ED - Pancioni, Luca ED - Schwenker, Friedhelm ED - Trentin, Edmondo T1 - Selecting Features from Foreign Classes T2 - Artificial Neural Networks in Pattern Recognition: 8th IAPR TC3 Workshop, ANNPR 2018, Siena, Italy, September 19–21, 2018: Proceedings UR - https://doi.org/10.1007/978-3-319-99978-4_5 Y1 - 2018 UR - https://doi.org/10.1007/978-3-319-99978-4_5 SN - 978-3-319-99978-4 SN - 978-3-319-99977-7 SP - 66 EP - 77 PB - Springer CY - Cham ER - TY - JOUR A1 - Hühne, Rolf A1 - Kessler, Viktor A1 - Fürstberger, Axel A1 - Werle, Silke D. A1 - Platzer, Matthias A1 - Sühnel, Jürgen A1 - Lausser, Ludwig A1 - Kestler, Hans A. T1 - 3D Network exploration and visualisation for lifespan data JF - BMC Bioinformatics N2 - Background The Ageing Factor Database AgeFactDB contains a large number of lifespan observations for ageing-related factors like genes, chemical compounds, and other factors such as dietary restriction in different organisms. These data provide quantitative information on the effect of ageing factors from genetic interventions or manipulations of lifespan. Analysis strategies beyond common static database queries are highly desirable for the inspection of complex relationships between AgeFactDB data sets. 3D visualisation can be extremely valuable for advanced data exploration. Results Different types of networks and visualisation strategies are proposed, ranging from basic networks of individual ageing factors for a single species to complex multi-species networks. The augmentation of lifespan observation networks by annotation nodes, like gene ontology terms, is shown to facilitate and speed up data analysis. We developed a new Javascript 3D network viewer JANet that provides the proposed visualisation strategies and has a customised interface for AgeFactDB data. It enables the analysis of gene lists in combination with AgeFactDB data and the interactive visualisation of the results. Conclusion Interactive 3D network visualisation allows to supplement complex database queries by a visually guided exploration process. The JANet interface allows gaining deeper insights into lifespan data patterns not accessible by common database queries alone. These concepts can be utilised in many other research fields. UR - https://doi.org/10.1186/s12859-018-2393-x KW - lifespan KW - ageing KW - gene network KW - 3D visualization KW - ageing factor database KW - AgeFactDB KW - differentially expressed genes Y1 - 2018 UR - https://doi.org/10.1186/s12859-018-2393-x UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27327 SN - 1471-2105 VL - 19 (2018) PB - BioMed Central CY - London ER -