@article{KunzLiangNillaetal.2016,
  author    = {Kunz, Meik and Liang, Chunguang and Nilla, Santosh and Cecil, Alexander and Dandekar, Thomas},
  title     = {The drug-minded protein interaction database (DrumPID) for efficient target analysis and drug development},
  series = {Database},
  volume    = {2016},
  journal   = {Database},
  doi       = {10.1093/database/baw041},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-147369},
  pages     = {baw041},
  year      = {2016},
  abstract  = {The drug-minded protein interaction database (DrumPID) has been designed to provide fast, tailored information on drugs and their protein networks including indications, protein targets and side-targets. Starting queries include compound, target and protein interactions and organism-specific protein families. Furthermore, drug name, chemical structures and their SMILES notation, affected proteins (potential drug targets), organisms as well as diseases can be queried including various combinations and refinement of searches. Drugs and protein interactions are analyzed in detail with reference to protein structures and catalytic domains, related compound structures as well as potential targets in other organisms. DrumPID considers drug functionality, compound similarity, target structure, interactome analysis and organismic range for a compound, useful for drug development, predicting drug side-effects and structure-activity relationships.},
  language  = {en}
}
@article{KunzWolfSchulzeetal.2016,
  author    = {Kunz, Meik and Wolf, Beat and Schulze, Harald and Atlan, David and Walles, Thorsten and Walles, Heike and Dandekar, Thomas},
  title     = {Non-Coding RNAs in Lung Cancer: Contribution of Bioinformatics Analysis to the Development of Non-Invasive Diagnostic Tools},
  series = {Genes},
  volume    = {8},
  journal   = {Genes},
  number    = {1},
  doi       = {10.3390/genes8010008},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-147990},
  pages     = {8},
  year      = {2016},
  abstract  = {Lung cancer is currently the leading cause of cancer related mortality due to late diagnosis and limited treatment intervention. Non-coding RNAs are not translated into proteins and have emerged as fundamental regulators of gene expression. Recent studies reported that microRNAs and long non-coding RNAs are involved in lung cancer development and progression. Moreover, they appear as new promising non-invasive biomarkers for early lung cancer diagnosis. Here, we highlight their potential as biomarker in lung cancer and present how bioinformatics can contribute to the development of non-invasive diagnostic tools. For this, we discuss several bioinformatics algorithms and software tools for a comprehensive understanding and functional characterization of microRNAs and long non-coding RNAs.},
  language  = {en}
}