Big Data

Big Data

The amount of biomedical data that is being amassed is increasing at a dramatic pace. The accumulated data is so “big” and complex that special algorithms and techniques are being devised to analyze it.
 

Our approach to big data

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Machine Learning allows computers to “learn” from data, producing more powerful predictive models and giving physicians novel insights. >Explore
Machine Learning
 
We mine large collections of DNA and RNA sequencing data, public and our own, for nuggets of actionable information. >Explore
Mining Sequencing Data
We make a lot of our research findings available to the public through open databases. Our databases provide results fast, exhaustively and link to a large number of external resources. >Explore
Databases
 
Algorithm design is necessarily invoked in the presence of big data. Our algorithms and tools span a wide range of areas like pattern recognition, CRISPR design, miRNA target site prediction, etc. >Explore
Algorithms and Tools

Why Big Data

Big data permits unbiased investigations and has proven an invaluable source of unexpected discoveries in very diverse settings. Big data can capture an individual’s health profile, can help design novel diagnostics, can suggest candidate therapeutic targets, provide novel insights for mechanisms and events that we thought were well understood, etc.

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Big Data – Highlights

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CMC’s team top ranked in “DREAM Challenge”.>Read
MINTbase v2. Among the new features, you can now browse TCGA data. >Read
miRNA isoforms (isomiRs) can discriminate amongst 32 TCGA cancers. >Read
Jefferson Harnessing “Big Data” to Broaden Cancer Research. >Read
Off-Spotter: Genomic instances per gRNA(s), PAM, mismatches & seed. >Read

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