@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}
}
@article{KarulinKaracsonyZhangetal.2015,
  author    = {Karulin, Alexey Y. and Karacsony, Kinga and Zhang, Wenji and Targoni, Oleg S. and Moldova, Ioana and Dittrich, Marcus and Sundararaman, Srividya and Lehmann, Paul V.},
  title     = {ELISPOTs produced by CD8 and CD4 cells follow Log Normal size distribution permitting objective counting},
  series = {Cells},
  volume    = {4},
  journal   = {Cells},
  number    = {1},
  doi       = {10.3390/cells4010056},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-149648},
  pages     = {56-70},
  year      = {2015},
  abstract  = {Each positive well in ELISPOT assays contains spots of variable sizes that can range from tens of micrometers up to a millimeter in diameter. Therefore, when it comes to counting these spots the decision on setting the lower and the upper spot size thresholds to discriminate between non-specific background noise, spots produced by individual T cells, and spots formed by T cell clusters is critical. If the spot sizes follow a known statistical distribution, precise predictions on minimal and maximal spot sizes, belonging to a given T cell population, can be made. We studied the size distributional properties of IFN-γ, IL-2, IL-4, IL-5 and IL-17 spots elicited in ELISPOT assays with PBMC from 172 healthy donors, upon stimulation with 32 individual viral peptides representing defined HLA Class I-restricted epitopes for CD8 cells, and with protein antigens of CMV and EBV activating CD4 cells. A total of 334 CD8 and 80 CD4 positive T cell responses were analyzed. In 99.7\% of the test cases, spot size distributions followed Log Normal function. These data formally demonstrate that it is possible to establish objective, statistically validated parameters for counting T cell ELISPOTs.},
  language  = {en}
}
@article{MilaneseMendePaolietal.2019,
  author    = {Milanese, Alessio and Mende, Daniel R and Paoli, Lucas and Salazar, Guillem and Ruscheweyh, Hans-Joachim and Cuenca, Miguelangel and Hingamp, Pascal and Alves, Renato and Costea, Paul I and Coelho, Luis Pedro and Schmidt, Thomas S. B. and Almeida, Alexandre and Mitchell, Alex L and Finn, Robert D. and Huerta-Cepas, Jaime and Bork, Peer and Zeller, Georg and Sunagawa, Shinichi},
  title     = {Microbial abundance, activity and population genomic profiling with mOTUs2},
  series = {Nature Communications},
  volume    = {10},
  journal   = {Nature Communications},
  doi       = {10.1038/s41467-019-08844-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-224089},
  year      = {2019},
  abstract  = {Metagenomic sequencing has greatly improved our ability to profile the composition of environmental and host-associated microbial communities. However, the dependency of most methods on reference genomes, which are currently unavailable for a substantial fraction of microbial species, introduces estimation biases. We present an updated and functionally extended tool based on universal (i.e., reference-independent), phylogenetic marker gene (MG)-based operational taxonomic units (mOTUs) enabling the profiling of >7700 microbial species. As more than 30\% of them could not previously be quantified at this taxonomic resolution, relative abundance estimates based on mOTUs are more accurate compared to other methods. As a new feature, we show that mOTUs, which are based on essential housekeeping genes, are demonstrably well-suited for quantification of basal transcriptional activity of community members. Furthermore, single nucleotide variation profiles estimated using mOTUs reflect those from whole genomes, which allows for comparing microbial strain populations (e.g., across different human body sites).},
  language  = {en}
}