@article{AhmedZeeshanHuberetal.2014,
  author    = {Ahmed, Zeeshan and Zeeshan, Saman and Huber, Claudia and Hensel, Michael and Schomburg, Dietmar and M{\"u}nch, Richard and Eylert, Eva and Eisenreich, Wolfgang and Dandekar, Thomas},
  title     = {'Isotopo' a database application for facile analysis and management of mass isotopomer data},
  series = {Database},
  volume    = {2014},
  journal   = {Database},
  number    = {bau077},
  doi       = {10.1093/database/bau077},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-120102},
  year      = {2014},
  abstract  = {The composition of stable-isotope labelled isotopologues/isotopomers in metabolic products can be measured by mass spectrometry and supports the analysis of pathways and fluxes. As a prerequisite, the original mass spectra have to be processed, managed and stored to rapidly calculate, analyse and compare isotopomer enrichments to study, for instance, bacterial metabolism in infection. For such applications, we provide here the database application 'Isotopo'. This software package includes (i) a database to store and process isotopomer data, (ii) a parser to upload and translate different data formats for such data and (iii) an improved application to process and convert signal intensities from mass spectra of \(^{13}C\)-labelled metabolites such as tertbutyldimethylsilyl-derivatives of amino acids. Relative mass intensities and isotopomer distributions are calculated applying a partial least square method with iterative refinement for high precision data. The data output includes formats such as graphs for overall enrichments in amino acids. The package is user-friendly for easy and robust data management of multiple experiments.},
  language  = {en}
}
@article{AhmedZeeshanDandekar2016,
  author    = {Ahmed, Zeeshan and Zeeshan, Saman and Dandekar, Thomas},
  title     = {Mining biomedical images towards valuable information retrieval in biomedical and life sciences},
  series = {Database - The Journal of Biological Databases and Curation},
  volume    = {2016},
  journal   = {Database - The Journal of Biological Databases and Curation},
  doi       = {10.1093/database/baw118},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-162697},
  pages     = {baw118},
  year      = {2016},
  abstract  = {Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries.},
  language  = {en}
}