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  • BIORI
  • BMAP (biomap)
  • CMAP (cellmap)
  • Amber-GPU
BIORI (Biomedical Thesauri)
1) Most bio texts, unlike other general IT texts, are related to each other. Therefore, the level of complexity of bio texts is a thousand-fold higher than that of other general texts.
2) Biori can perform multi-word searches like Google, hence Bio-Googling or Biori.
3) Moreover, Biori has public text databases stored and indexed on a local machine, which makes it possible to search in real time.
4) One of many features of Biori is "ONE-STOP Search." It lets the user select search terms from within the result of a previous search to get more refined outcome.
5) Each unique word receives a numeric value and gene names are especially weighted, and the final result receives a score.
6) Additional search terms can be appended to the current ones in the search panel to improve the quality of the search results.
7) Web-link display: All results provide a hyperlink to their original sources.
8) Local-link display: All results provide a hyperlink to the indexed sources in the local database.
9) result to file: All results can be downloaded as a separate file.
10) weighted search: search terms preceding "@" are weighted and given more importance.
* This system is free of charge, and continutes to be updated. Please give us a feedback for improvement.
BMAP ( biomap )
1) BMAP searches public databases with genes, compounds, or SNPs as a query. It searches all relaxing queries homologous to the query, and thus partially covers areas in silico, traditionally done only in wet labs.
2) Through web programming, BioMaps are created. The results created by the BioMap system is in a standard format and can be automatically programmed to show on the web. Some public APIs may be provided for developers.
3) The BioMaps themselves may not be of high value, but based on the contents of the BioMaps, analytics is carried out for more interrelational analysis, the result of which is collected in Analytic Summary.
4) Analytics Summary offers statistical analyses such as Disease Top 10, Pathway Top 10, PPI Validation, and PCI Validation. These analyses can be linked with other pre-calculated, high-value proteome functional maps or blockbuster maps.
5) All results in Analytics Summary provide data that can be used as an input for many public softwares. With these softwares, additional visual/statistical analyses can be performed.
6) Finally, these results, with images and statistical analyses, are put together as a single BioMap report.
CMAP (cell map)
<Examples>
Animal & Human Diseases
Plant Diseases
1) CMAP is a function annotation map. It automatically divides many relations within a proteome/genome into different categories with respect to function/association to diseases.
2) Categories can be defined to meet the user's needs (e.g., antibiotics, anticancer drugs, chronic diseases, new bio drugs and their target groups).
3) All possible protein/chemical interaction maps, which are predicted in silico, are created under each category.
4) Pages containing annotations and predicted information about each categorized function are created.
5) Direct/indirect(through bridge genes) interaction maps among the categorized functions are created and their evidence is attached.
6) CMAP can create networks based on not just known interactions, but also interactions among homologues. Therefore huge networks are created from a systemic approach, which would otherwise be impossible to create.
7) Protein/chemical interactions between functional protein groups (CMAP) are represented as a full network and its image is created.
VEC simulator ( Amber-GPU )
<Examples>
VLP
MHC-Epitope
PCI
Folding
1) Through BMAP(created from Genes, Compounds, or SNPs), CMAP, and Biori search, a specific target can be defined.
2) Protein-drug, antibody-antigen, and many other complex modeling topics require a fast simulation environment. Under normal settings, these simulations are impossible. However, GPGPU (General Purpose Graphic Process Unit) provides a cheaper, yet faster processing power, thus makes it possible with the technology we have.
3) Technical limitations can be overcome with the help of 3D manipulators such as GEMM, and some areas such as MHC-epitope may benefit from partial feasibility.
4) Currently, the most feasible area is Epitope Virtual Screening, and MHC structure and peptide simulations can be partially simulated.
5) Traditional areas such as Virtual Drug Screening may also utilize GEMM and other 3D manipulators.
6) VEC simulator can also be used in many other complex modeling subjects such as virus-like particle design.