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Ingentium D3K™ - Deep, Dynamic Diseases-Focused Knowledgebases


It is impossible to keep abreast of all the advances being made across the medical and healthcare community and be able to use these in an effective and timely fashion

Asking key questions and getting fast and complete answers

Generating hypothesis and finding all the relevant data to refute or support them

Integrating ALL the relevant information that includes internal data of which you have access

Find new and novel relationships that only looking at broad, deep and diverse sets of information can facilitate

About our Platform

The Ingentium Knowledge Platform™ (IKP™) is a cloud-based infrastructure that serves as a knowledge hub and facilitates access to all organizational information to enable Big Data analytics. IKP employs leading edge semantic and cognitive technologies as well as cutting edge development architecture to facilitate integration for decision support and knowledge management across all areas of the enterprise. Additionally, by virtue of its highly modular architecture, IKP™ also serves as an enterprise development platform for automating scientific, business, and collaborative applications.

IKP™ provides the capability to create information commons that provide query, browsing and analysis capabilities focused on specific disease areas and health related topics.  In addition, from the one or more information commons, knowledge networks can be assembled to offer unparalleled views with query and analysis of broad collections of information across multiple data types.

IKP™ operates from a “Big Data” perspective. This means the infrastructure is cloud-based and both compute and storage are independently scalable in response to load. From an architectural perspective, IKP™ is an extreme articulation of a service-oriented architecture in that all communication between ILP™ and other media sources is carried out via RESTful API calls. IKP™ takes this a step further by introducing fault tolerance through a pull-only communication scheme. Finally, IKP™ takes a very different approach in terms of data integration; it integrates semantically and cognitively as opposed to integrating by data structure relations. The usage of semantic integration allows the user to retrieve information from a pool that is broader and deeper than through relational methods, and the information returned is of higher quality, sensitivity, and selectivity. In a nutshell, you get the all information you need from all facets of your organization to support your decision.

Data Classes


MP3 format audio files, including PodCasts.


Metadata and possibly content retrieved from Amazon and Google.


Internet search for latest news and/or information


Internet search for documents files (text, presentations, …). Documents may also be uploaded from local file systems.


Drug information from DrugBank, Kegg, FDA and many others.


Gene information from EntrezGene and NCBI.


Image infrormation from Google, Bing,  OpenI and many others.


Pathway information from Kegg, Reactome, BioCyc, SMPdb, WikiPathways and many others.


Reference information from PubMed, PubMed Central, arXiv and many others.


Video information from YouTube and DailyMotion. Videos may also be uploaded from local file systems.


Other classes include analytical data files, chemicals, clinical trials, disease markers, metabolites, electronic health records, patents and sequences.

Knowledge Networks

Knowledge Networks are created by identifying connections between specific items in the Data Classes listed above that represent their relationships (edges).