Text Mining:

Intomics has developed our own Text Mining tool, inBio Know™. This tool allows us to find all significant pairwise associations among genes/proteins, anatomies (cells and tissues) and diseases in published scientific literature abstracts. InBio Know™ is a fast, efficient and reliable tool that we apply in our service offerings. Depending on project scope inBio Know™ is utilized in different project stages such as Quality Check of Data, Data Analyses, Result Discussions and Project Conclusions.

Protein-Protein Interaction Network - an important supplement to pathway resources:

Biological pathways provide an intuitive framework for understanding and interpreting biological data.

However, they are limiting with regards to the biology they cover*. KEGG pathways cover ~6 700 human protein coding genes. Reactome pathways cover ~7 700 human protein coding genes. In total pathways are estimated to cover 30-40% of protein biology

To get a much wider coverage of protein biology, Intomics has developed a proprietary protein-protein interaction network, inBio MapTM. This network database of high-confidence, experimentally derived protein-protein interactions covers more than 700,000 physical protein-protein interactions of more than 17,500 human reviewed UniProt proteins. InBio MapTM is estimated to cover 87% of reviewed human proteins in UniProt.

We carefully update inBio Map™ on a regular basis to ensure that the database always reflects the most current published knowledge about human protein-protein interactions. inBio Map™ is a powerful tool that we apply in our service offerings to uncover novel biological insights in areas of client interest.

*Rahmati et al, Nucleic Acids Research, 2017


Standard Operating Procedures:

At Intomics, we understand biomedical big data and know how to handle and integrate biomedical data sets from a wide range of sources. To ensure consistency in our handling of large biomedical data sets we have developed stringent Standard Operating Procedures, that we apply to data applied in projects regardless of their origin and format.  Our methodology is based on a critical and uniform approach rooted in a biological starting point, thus we always put data sets in a biological context at the beginning of any project. Our experience has shown us, that careful biological and technological quality check of data at project start is essential for making end-results after data integration credible, trustworthy and not least reproducible in in vitro and in vivo experiments.