MLconf NYC Pek Lum
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MLconf NYC Pek Lum

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MLconf NYC Pek Lum MLconf NYC Pek Lum Presentation Transcript

  • Data has Shape, Shape has Meaning Pek Lum, Ph.D. Chief Data Scientist VP of Solutions
  • 2000 2005 2008 2010 2013 NSF funds Stanford Math Professor Gunnar Carlsson to research Topological Data Analysis Floodgate and private investors provide seed capital Khosla Ventures and Institutional Venture Partners lead growth financing rounds DARPA invests in applying TDA to massive, complex, multi-modal DoD data AYASDI founded with DARPA and IARPA funding Company Timeline COMPANY CONFIDENTIAL 2 Tony Tether, Director Defense Advanced Research Projects Agency (2001-2009) Ayasdi’s approach is using Topological Data Analysis one of the top 10 innovations developed at DARPA in the last decade.
  • Ayasdi named one of the Top 10 Most Innovative Companies in Big Data for 2013 Won the Gigaom Structure Data Award for Most Promising Machine Learning/Artificial Intelligence Startup In collaboration with UCSF won the NFL/GE Head Health challenge for their work in mild-traumatic brain injury COMPANY CONFIDENTIAL 8 Recent Awards
  • The Problem The Solution How can the Life Sciences field fully leverage data to gain knowledge and extract insights towards better outcomes for health? Why is complex (& big) data not tractable or accessible to everyone who has a stake in the results? From computational folks to physicians who collected the data? Easy access to game changing analytical methods. Computer- augmented analysis. Automation. Visualization. No need to think of queries first- answers first, hypothesis building after that. The Impact Obtaining insights from data can become more democratized, more collaborative among disparate disciplines, more meaningful faster
  • TDA as a framework for ML methods Automation AYASDITDA My talk today Interactive visualization What is TDA? Why TDA Shape of Data Speed to insights Leveraging big data for the rest of us
  • Data has shape and! shape has meaning.!
  • TDA methods will transform the way that doctors triage patients, through construction of non-linear, non-invasive medical statistics to assess patients in intensive and critical care situations. Introducing The Shape of Data A mathematical concept that began in the 1700 s. Uses the shape of data to find unknown phenomena. Topological Data Analysis (TDA) Math + Computer Science + User Experience Automated discovery of shapes
  • What do I mean by data having shape ? Age, Weight and Height sampled uniformly at random In reality, age, weight and height are correlated and that data has a shape
  • 1. Coordinate free representations are vital when one is studying data collected with different technologies- studies done at different times, multi-variate data types collected ! 2. Deformation invariance has an effect of introducing a degree of robustness into the analysis, which is important in the study of real world data- human heterogeneity is complex and needs an approach that is deformation (variation) resistant ! 3. Compact representations are important for visualizing large and complex datasets- this is so that signals can be easily identified in the form of “shapes” in the network What does the TDA approach bring to the table?
  • TDA summarizes the shape of data with no pre-conceived model of what it should be
  • Bringing TDA (math world) into Real Data World=Ayasdi Software Automation Interactive, intuitive visualization Handling “Big Data” No coding needed Topology for the rest of us TDA as a framework for ML methods
  • A node represents a group of similar objects. ! Edges between nodes are drawn when the nodes are very similar to each other. Nodes that are not connected are less similar to each other. ! The coloring reflects values of interest. The position of a node on the screen is irrelevant - only its connection to other nodes matters. Ayasdi Network Orientation
  • © 2012 Ayasdi inc.Case Study > Identification of patient subtypes Ayasdi Network Orientation Topological Map of Patient-Patient Relationships according to their tumors molecular characteristics (in this case, gene expression)! Each node contains subsets of patients! These patients are eccentric (away from the center of the data)! These patients are close to the center of the data! Color scheme!
  • The Problem The Approach Landscape of cancer and path towards more precise treatments Cancer is very heterogenous. Data generated from TCGA is extremely large and generally not viewable all at once and certainly very hard to access and analyze by non- computational folks View 12 cancers at once before drilling down to sub-populations The Results The ability to view all cancers at once allows very quick scans of the landscape of unique and common mutations. We also show that TP53 mutation is the common link between Triple Negatives Breast Cancer and Ovarian Cancer. The Cancer Genome Atlas
  • Breast invasive carcinoma! Kidney renal clear cell carcinoma! Bladder Urothelial Carcinoma! Cervical squamous cell carcinoma and endocervical adenocarcinoma! Lung squamous cell carcinoma! Ovarian serous cystadenocarcinoma! Uterine Corpus Endometrioid Carcinoma! Colon adenocarcinoma! Glioblastoma multiforme! Prostate adenocarcinoma! Rectum adenocarcinoma! Acute Myeloid Leukemia! 12 cancer types from The Cancer Genome Atlas DNA exome sequencing: High Volume, High Complexity! Over 2400 tumors, 12 cancer types, over half a million unique variants analyzed simultaneously!
  • Landscape of p53 mutations across all 12 cancers! Breast invasive carcinoma! Kidney renal clear cell carcinoma! Bladder Urothelial Carcinoma! Cervical squamous cell carcinoma and endocervical adenocarcinoma! Lung squamous cell carcinoma! Ovarian serous cystadenocarcinoma! Uterine Corpus Endometrioid Carcinoma! Colon adenocarcinoma! Glioblastoma multiforme! Prostate adenocarcinoma! Rectum adenocarcinoma! Acute Myeloid Leukemia! red= enriched for p53 mutations; blue=not enriched!
  • GATA3 mutations quite unique to breast cancer
  • Identifying commonalities between breast and ovarian cancers Triple negative! breast cancers! Ovarian! cancers! Breast! cancers!A! A! B! Merging both DNA mutations + gene expression data together!
  • Triple negative! breast cancers! mutations in p53! red= many; dark blue= none! Ovarian! cancers! Breast! cancers! TP53 mutation in the tumors is the common theme between TNBC and Ovarian cancers
  • FOXA1 gene levels! GO_pathway_positive regulation of potassium ion transport!PSAT1 gene levels! Mutations in TP53! Triple negative breast cancer! Ovarian tumors! Common target for TNBC and Ovarian Cancer?
  • Data has Shape, Shape has Meaning Pek Lum, Ph.D. Chief Data Scientist VP of Solutions