This week a new Big Data startup company launched, Ayasdi, co-founded by Stanford mathematics professor Gunner Carlsson and based on his DARPA-funded research in the area of applied topology, with $10+million in Series A funding led by Khosla Ventures and Floodgate.
The technology, dubbed the Insight Discovery platform, is explained to be the “first machine learning platform that combines computer science and a branch of mathematics known as Topological Data Analysis (TDA) that visualizes the entire dataset.” Hundreds of machine learning algorithms, it says, go to work exploring datasets to in minutes automatically discover insights that can’t be determined through query-based or ad hoc approaches.

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Bioinformatics software provider IO Informatics recently released its free Knowledge Explorer Personal Edition. Version 3.6 of the Personal Edition can handle most of what Knowledge Explorer Professional 3.6, launched in October, can, but it does all its work in memory without direct connectivity to a back-end database.

“In particular, a lot of the strengths of Knowledge Explorer have to do with modeling data as RDF and then testing queries, visualizing and browsing the data to see that you have the ontologies and data mappings you need for your integration and application requirements.” says Robert Stanley, IO Informatics president and CEO. The Personal version is aimed at academic experts focused on data integration and semantic data modeling, as well as personal power users in life sciences and other data-intensive industries, or anyone who wants to learn the tool in anticipation of leveraging their enterprise data sets for collaboration and integration projects.

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The GO Browse Genomic Data Browser application that took top honors at the recent Tetherless World Constellation hackathon, co-sponsored by Elsevier, should shortly be available as a live demo. It’s on the to-do list for Jim McCusker, the PhD student at TWC and part-time software developer at the Yale University School of Medicine who created the application as a visual way to browse linked medical datasets on the genetics of cancer.

The data sources included comparisons of different cancers based on cell lines curated by the National Cancer Institute. “Basically, it measures the level of gene expression for every gene in the human genome,” says McCusker of the data. “The great thing is you can then do automated differential gene expression, so you can do statistical tests to see what genes are significantly expressed from one cancer to the rest.” GO Browse presents this information in a visual way to show more differentially expressed categories of genes based on cell processes.

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