BioXM™ Knowledge Management Environment

The BioXM™ Knowledge Management Environment is a fully customizable knowledge management (KM) solution for better integrated and knowledge-based drug discovery and development. This project-centered, distributed software platform provides a central inventory of information and knowledge. Users create, manage and visualize scientific models as an extendible network of interrelated concepts. The BioXM platform facilitates communication and collaboration within research environments, allowing you to focus on science.

Biomax has started to roll out a new, major version of the BioXM Knowledge Management Environment to its customers. BioXM version 5 represents the next generation of Biomax's premier technology platform. The new version provides powerful new functionality and lays the groundwork for future development.

New features for the internationalization of BioXM-built solutions, transparent handling of time zones and physical units, and the representation of users of the system as an integral part of the configured solution further strengthen the capabilities of BioXM as an ideal collaboration platform serving multiple geographical sites and user groups in global organizations. Seamless integration of relational database management systems, an expanded concept for high-throughput experiments, and a significantly enhanced portal system improve the BioXM platform for large-scale use.

Our long-time customers say it best

"By now we have used the new BioXM system in several different areas. The system is outstanding in terms of flexibility and its ability to be tuned to our needs. Its solution-building approach is unprecedented: rolling-out a new application to hundreds of end-users takes only weeks. Most importantly, we are able to continuously adapt the system while staying in production, which greatly reduces cost."

— Dr. Marco de Groot, Scientist Bioinformatics at DSM

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Novel solutions

The BioXM system allows customers to complement and leverage their existing resources by enabling an agile process to develop new life science applications through easy configuration and customization. With the BioXM system, new solutions can be deployed quickly to large user communities in fields such as: pharmaceutical and clinical research, biobanking, next-generation sequencing, sequence analysis, pathway analysis, literature mining, systems biology and comparative genomics.

One of the areas in which the BioXM platform has been used for knowledge management includes a project for the US National Cancer Institute (NCI) with Sophic Systems Alliance to create an up-to-date index of all human cancer-related genes, which includes compound and disease relationships.

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Agile solution building

The BioXM system is a sustainable platform for agile solution building, which offers:

  • Speed — rapid development turn-around time
  • Flexibility — enable immediate feedback from users
  • Quality — high scientific precision and analytical power
  • Ease of use — simple Web front end

Benefits for research

  • Supports data-driven and hypothesis-driven research
  • Supports decision making
  • Connects and visualizes data, information and knowledge
  • Facilitates new insights and new working hypothesis
  • Enables full data integration for discovering novel relationships and patterns in biological networks
  • Builds new data connections and networks on-the-fly
  • Enhances existing data integration platforms (SRS, BioRS, etc.), for example, by adding a semantic layer describing data connectivity
  • Enables mapping of proprietary knowledge on top of public ontologies

Benefits for corporations

  • Provides a powerful KM solution for drug discovery development
  • Supports decision makers
  • Shares data and knowledge between individual user groups
  • Enables full data integration, fast retrieval and complete visualization of both scientific and business data
  • Captures relationships between any information: pharmacogenomics, epidemiology, patent data, translational medicine, etc.
  • Provides maximum flexibility without additional programming
  • Ensures fast return on investment
References

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Journal of oncology, 368751 (2013) Abstract Full text

Toward an integrated knowledge environment to support modern oncology.
Blake PM, Decker DA, Glennon TM, Liang YM, Losko S, et al.
Cancer J. 5:38 (2011) Abstract

Knowledge management for Systems Biology: a general and visually driven framework applied to translational medicine.
Maier D, Kalus W, Wolff M, Kalko S G, Roca J, et al.
BMC Systems Biology 5:38 (2011) Abstract
More information about the BioBridge Project

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Allergy (2011) Abstract
More information about the MeDALL project

Aus der Forschung an das Krankenbett
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Finding Promising Protein Biomarkers
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Creating a Bridge between Modelica and the Systems Biology Community
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Proceedings 7th Modelica Conference Como, Italy, Sep. 20-22, 2009 PDF

Chapter 13: Semantic Data Integration and Knowledge Management to Represent Biological Network Associations
Sascha Losko and Klaus Heumann Abstract
in Protein Networks and Pathway Analysis (table of contents)
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Springer

Functional Modules integrating essential cellular functions are predictive of the response of leukaemia cells to DNA damage.
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Bioinformatics Advance Access, published online on September 17, 2008 Abstract

Integration of transcriptomics data into systems biology modeling in the BioBridge portal. Gómez-Garrido A, et al.
CCIS Series 2008 Abstract

Modeling and exploration of biological networks using the BioXM Knowledge Management Environment.
Ilgenfritz H, Kalus W, Heumann K and Losko S
Presented at International Symposium on Networks in Biology. Amsterdam, The Netherlands (2007) PDF, 3 MB

Knowledge Networks of Biological and Medical Data: An Exhaustive and Flexible Solution to Model Life Science Domains.
Losko S, Wenger K, Kalus W, Ramge A, Wiehler J and Heumann K
Lecture Notes in Computer Science 4075: 232-239 (2006) Abstract

Integration of biological data using BioXM Knowledge Management Environment.
Ramge A, Losko S and Heumann K
Presented at Bioinformatics Munich: from Genomes to Systems Biology Workshop Munich, Germany (2006) PDF,1.2 MB