What if there was a Google for biomedical data? What if there was a nice, clean search space where a researcher could type in any key term and get highly relevant data instantly? A valuable prospect as today this isn’t so easy. Hence why the National Institutes of Health (NIH), National Center for Advancing Translational Science (NCATS), which spends billions of dollars for biomedical research, has a plan. They are currently in a feasibility phase through the end of the year.
With all the billions that NIH spends (taxpayer money) what happens to the data reports Ruth Hailu of State News?
NCATS’ Biomedical Data Translator Program
The NIH’ National Center for Advancing Translational Sciences (NCATS) touts this new ongoing effort called Translator. They explain that because of the significant scientific and technological advances over the past decades, there is an enormous amount of biomedical research data and data available from disease classifications, health records, clinical trials, and adverse event reports that could be useful for understanding health and disease and for developing and identifying treatments for diseases.
What if the data could be effectively and collectively mined? Imagine the incredible potential for insights into the relationship between molecular and cellular processes (the targets of rational drug design) and the signs and symptoms of diseases. Currently, these very rich yet different data sources are housed in various locations, often in forms that are not compatible or interoperable with each other. All of these factors limit the ability to get more applicable treatments to patients as quickly as possible.
Hence, NCATS launched the Biomedical Data Translator program, called “Translator” for short. This multi-year, iterative effort will culminate in the development of a comprehensive, relational, N-dimensional Biomedical Data Translator that integrates multiple types of existing data sources, including objective signs and symptoms of disease, drug effects, and intervening types of biological data relevant to understanding pathophysiology.
NCATS reports that each data type will be comprehensive (e.g., all diseases, all pathways, all SNPs). It also will be possible for a user to access the Translator for any data type and identify all connections in any other data type. This will enable a shift from the current symptom-based diagnosis of disease to one that is based on a set of molecular and cellular abnormalities and can be targeted by various preventive and therapeutic interventions.
This takes taxpayer money. Is it recreating the wheel? After all, there are many groups (including Google) involved with efforts to accumulate, organize, and catalog information.
NCATS position on other efforts and redundancy: Although these efforts provide proof of principle, the Translator will be broader in scope with the goal of revealing potential relationships across the spectrum of data types, from signs and symptoms to molecules and drugs.
Examples of the types of queries the Translator will enable for the first time could include, but are not limited to, the following:
- Show every disease that has symptom X and/or affects a particular cell type.
- Show all molecular pathways that, when perturbed, lead to malfunction of organelle A in organ B in people with X-Y-Z genomic characteristics.
- Show all the treatments currently being investigated that perturb any pathway that is dysfunctional in diseases characterized by clinical sign X.
This effort will require unprecedentedly broad teams of experts to work together in a highly collaborative manner with active program management. Input from clinicians during the design and feasibility assessment will be critical to ensuring appropriate inclusion of clinical data.Source: STAT