Posts Tagged ‘cancer’

Cognition Network Technology: Taking On Cancer By Repositioning Knowledge In A Semantic Network

rsz_defThis week saw Frost & Sullivan award its 2013 Company of the Year to Definiens, a provider of image analysis solutions and data mining solutions for life science, tissue diagnostics, and clinical digital pathology. Definiens’ gaining of the title owes much to its work around tissue datafication that’s leveraging its Definiens Cognition Network Technology, which the company says mimics the human mind’s cognitive powers to reposition knowledge within a semantic network.

“What we do essentially is look at ways to be able to better diagnose cancer and develop therapies,” says Merrilyn Datta, CMO at Definiens. The company looks to extract data from tumor images, historically available as slides from biopsies, datafying the tissues involved to create digital images and then using its Cognition Network Technology to extract all the different relevant objects in that image and correlate them to patient outcomes. “That can be extremely, extremely powerful,” says Datta.

The image analysis technology was developed by Physics Nobel Laureate Gerd Binning, and includes a set of principles aimed at emulating the human mind’s cognitive powers, which are defined by the ability to intuitively aggregate pixels into ‘objects’ and understand the context and relationships between those objects rather than the computer’s normal way of just examining images pixel by pixel. These principles include:  context, which is established and utilized through the technology’s creation of a hierarchical network of pixel clusters representing nested structures within the image; navigation, for supporting efficient navigation inside the network in order to enable specific local processing and addressing of specific contexts; and evolution of the network, in which the individual stages of segmentation and classification are alternated and the structures represented within the network are created and constantly improved in a series of loops, whereby each classification can be enhanced with local context and specific expert knowledge.

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Big Data Startup Ayasdi Launches; Machine Learning Platform Combines Computer Science And Topological Data Analysis

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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Semantic Data Integration For Free With IO Informatics’ Knowledge Explorer Personal Edition

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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Heads Up To Computational Biologists: Go Semantic and Go Further With Your Data

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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