@article{NevesLeser2015,
  author    = {Neves, Mariana and Leser, Ulf},
  title     = {Question answering for Biology},
  series = {Methods : focusing on rapidly developing techniques},
  volume    = {74},
  journal   = {Methods : focusing on rapidly developing techniques},
  publisher = {Elsevier},
  address   = {San Diego},
  issn      = {1046-2023},
  doi       = {10.1016/j.ymeth.2014.10.023},
  pages     = {36 -- 46},
  year      = {2015},
  abstract  = {Biologists often pose queries to search engines and biological databases to obtain answers related to ongoing experiments. This is known to be a time consuming, and sometimes frustrating, task in which more than one query is posed and many databases are consulted to come to possible answers for a single fact. Question answering comes as an alternative to this process by allowing queries to be posed as questions, by integrating various resources of different nature and by returning an exact answer to the user. We have surveyed the current solutions on question answering for Biology, present an overview on the methods which are usually employed and give insights on how to boost performance of systems in this domain. (C) 2014 Elsevier Inc. All rights reserved.},
  language  = {en}
}
@article{JargoschKroegerGralinskaetal.2016,
  author    = {Jargosch, M. and Kroeger, S. and Gralinska, E. and Klotz, Ulrike and Fang, Z. and Chen, W. and Leser, U. and Selbig, Joachim and Groth, Detlef and Baumgrass, Ria},
  title     = {Data integration for identification of important transcription factors of STAT6-mediated cell fate decisions},
  series = {Genetics and molecular research},
  volume    = {15},
  journal   = {Genetics and molecular research},
  publisher = {FUNPEC},
  address   = {Ribeirao Preto},
  issn      = {1676-5680},
  doi       = {10.4238/gmr.15028493},
  pages     = {17},
  year      = {2016},
  abstract  = {Data integration has become a useful strategy for uncovering new insights into complex biological networks. We studied whether this approach can help to delineate the signal transducer and activator of transcription 6 (STAT6)-mediated transcriptional network driving T helper (Th) 2 cell fate decisions. To this end, we performed an integrative analysis of publicly available RNA-seq data of Stat6-knockout mouse studies together with STAT6 ChIP-seq data and our own gene expression time series data during Th2 cell differentiation. We focused on transcription factors (TFs), cytokines, and cytokine receptors and delineated 59 positively and 41 negatively STAT6-regulated genes, which were used to construct a transcriptional network around STAT6. The network illustrates that important and well-known TFs for Th2 cell differentiation are positively regulated by STAT6 and act either as activators for Th2 cells (e.g., Gata3, Atf3, Satb1, Nfil3, Maf, and Pparg) or as suppressors for other Th cell subpopulations such as Th1 (e.g., Ar), Th17 (e.g., Etv6), or iTreg (e.g., Stat3 and Hifla) cells. Moreover, our approach reveals 11 TFs (e.g., Atf5, Creb3l2, and Asb2) with unknown functions in Th cell differentiation. This fact together with the observed enrichment of asthma risk genes among those regulated by STAT6 underlines the potential value of the data integration strategy used here. Thus, our results clearly support the opinion that data integration is a useful tool to delineate complex physiological processes.},
  language  = {en}
}
@misc{HesseMatthiesSinzigetal.2019,
  author    = {Hesse, G{\"u}nter and Matthies, Christoph and Sinzig, Werner and Uflacker, Matthias},
  title     = {Adding Value by Combining Business and Sensor Data},
  series = {Database Systems for Advanced Applications},
  volume    = {11448},
  journal   = {Database Systems for Advanced Applications},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-18590-9},
  issn      = {0302-9743},
  doi       = {10.1007/978-3-030-18590-9_80},
  pages     = {528 -- 532},
  year      = {2019},
  abstract  = {Industry 4.0 and the Internet of Things are recent developments that have lead to the creation of new kinds of manufacturing data. Linking this new kind of sensor data to traditional business information is crucial for enterprises to take advantage of the data's full potential. In this paper, we present a demo which allows experiencing this data integration, both vertically between technical and business contexts and horizontally along the value chain. The tool simulates a manufacturing company, continuously producing both business and sensor data, and supports issuing ad-hoc queries that answer specific questions related to the business. In order to adapt to different environments, users can configure sensor characteristics to their needs.},
  language  = {en}
}