Over a million developers have joined DZone.

Curing Cancer with Data Visualization

· Big Data Zone

Learn how you can maximize big data in the cloud with Apache Hadoop. Download this eBook now. Brought to you in partnership with Hortonworks.

This post was originally written by Jesse Paquette.

Everyone thought that The Netherlands Cancer Institute’s 12-year-old dataset on breast cancer was old news. That was until a researcher with Merck & Co, Pek Lum, analyzed and visualized the dataset with the use of topological data analysis (TDA) and advanced machine learning technology. Her analysis landed her a featured story in the Wall Street Journal and, more importantly, allowed her to identify a previously unknown subgroup of cancer survivors that exhibited particular traits, a step forward in finding a cure.

Topological Data Analysis

04

Pek’s experience led her to become the Chief Data Scientist for Ayasdi, the company that created the software that was critical to her discovery. The Ayasdi Platform allowed Pek to leverage TDA to automatically construct a topological network of the cancer dataset. TDA is based on a branch of mathematics known as topology, the study of shape. Being able to use automation to find similarities within extremely complex datasets allows experts to discover new insights from analysis of the shape the data takes.

Let the Data Speak for Itself

Instead of using more traditional methods of asking a question about a particular part of the data, Pek was able to look at the dataset in its entirety, allowing the data to speak for itself. Pek was able to upload the breast cancer dataset into the Ayasdi Platform to create nodes of similar patients based on genetic make up. These nodes were then connected by edges between two or more nodes that shared a common patient. This created a complex venn diagram of relationships within the data. Further analysis of the topological network uncovered the subgroup of survivors and determined what traits made this group unique.

Driving Breakthrough Insights

TDA is changing the way domain experts around the world are looking at their data, and is offering a new framework in which many different algorithms can be cohesively used to automatically examine extremely complex datasets. More importantly, it allows mechanical engineers, financial service analysts, marketers, and oncologists to visualize their data in new ways to drive breakthrough insights.

Jesse

Jesse Paquette (Lead, Translational Engineer):
Jesse leads the development of new features and pipelines that enhance user experience, particularly in the life science domain. Prior to Ayasdi, Jesse was a computation biologist at UCSF. He maintains an impressive collection of scar tissue, perpetuated be weekly overdoses of pick-up soccer.

Hortonworks DataFlow is an integrated platform that makes data ingestion fast, easy, and secure. Download the white paper now.  Brought to you in partnership with Hortonworks

Topics:

Published at DZone with permission of Christopher Taylor, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

The best of DZone straight to your inbox.

SEE AN EXAMPLE
Please provide a valid email address.

Thanks for subscribing!

Awesome! Check your inbox to verify your email so you can start receiving the latest in tech news and resources.
Subscribe

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}