Big Data OncoAnalytics Architecture
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The document discusses the impact of big data analytics on cancer treatment and management, covering topics such as oncology, the role of artificial intelligence, and the importance of genomic data. It outlines various analytics models, architecture patterns, and future directions in personalized medicine, emphasizing the need for effective data integration. The document highlights how advancements in technology are reshaping cancer care through improved diagnosis and treatment strategies.
Big Data OncoAnalytics Architecture
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- 2. 2 PERSONAL INTERESTS Big Data Analytics Artificial Intelligence Data Warehousing Technology Cloud Computing Data Center Oncology Architecture Astronomy Data ScienceHealthcare Philippe Julio – Big Data Analytics Architect https://www.linkedin.com/in/juliophilippe/ Digital Big data OncoAnalytics Consulting
- 3. 3 ONCOANALYTICS INSIGHT ONCOPHYSICS ONCOGENOMICS See into the future • Objects : x-rays, gamma-rays, magnetic resonance, ultrasounds, laser light... • Quantum mechanics : photon, electron, magnetism… • Objects : tumors, cells, chromosomes, genes, DNA, enzyme, hormone, antibody… • Quantum biology : DNA mutation, cellular respiration… • Objects : data, bits, Qbits • Quantum computer : artificial Intelligence, algorithms, cryptography, search, simulation, linear equations, prediction, recommendation, risk… See into the present Quantum Physics and Artificial Intelligence ONCOANALYTICS Radioscopy vision Computer vision • Objects : tumors, molecules, proteins, hormones, enzymes, biomarkers, amino acids … • Quantum chemistry : biomolecular modeling, chemical energy, spectra analysis… ONCOPHARMACEUTICS Microscopic vision Infinite Analytics Use Cases See into the presentSee into the present Microscopic vision Big data OncoAnalytics
- 4. 4 KEYS QUESTIONS • Where Are We Heading About Fight Against Cancer ? • How Big Data Analytics Is Transforming Oncology ? • Which Big Data OncoAnalytics Architecture Patterns ? • Which Big Data OncoAnalytics Solutions ? • Which Big Data OncoAnalytics Solution Examples ? • How Getting Start Big Data OncoAnalytics Project ? • Which Big Data OncoAnalytics Value Proposition ?
- 5. Where Are We Heading About Fight Against Cancer ?
- 6. 6 • Cancer can start any place in the body • Cells grow out of control and crowd out normal cells • There are more than 200 different types of cancer • Cancer starts when gene changes make one cell or a few cells begin to grow and multiply too much • Cell growth is called a tumor • Primary tumor is the name for where a cancer starts • Secondary tumor or a metastasis is a name for where the cancer spreads to other parts of the body • Oncology is a branch of medicine that specializes in the diagnosis and treatment of cancer. It includes medical oncology, radiation oncology and surgical oncology WHAT IS CANCER AND ONCOLOGY ? Tumor Nodes Metastasis Classification T : (0-4) tumor size or direct extent of the primary tumor N : (0-2) degree of spread to regional lymph nodes M : (0-1) presence of distant metastasis Sub classifications for some cancer types Where are we heading about fight against cancer ? Grade Tumor Growth and Spread Classification Gx: Grade cannot be assessed (undetermined grade) G1: Well differentiated (low grade) G2: Moderately differentiated (intermediate grade) G3: Poorly differentiated (high grade) G4: Undifferentiated (high grade)
- 7. 7 • Building a world where every cancer patient receives the right care at the right place at the right time • Curing or considerably prolonging the life of patients and to ensure the best possible quality of life for cancer survivors • Providing in a equitable and sustainable way the most effective treatments thanks to early detection, accurate diagnosis and staging and adhere to evidence-based standards of care • Forecasting the cancer for offering a real opportunity for clinical benefit that is based on anticipatory action ONCOLOGY GOALS Where are we heading about fight against cancer ?
- 8. 8 CANCER GENOMICS BASICS Genome is unique for every person • All the DNA molecules contained in our cells makes up our genome • Genome sequencing complete in 2004 • 99.9% of our genome is the same • 0,1% is enough to make each one of us unique • On average of 1-3 bases differ from person to person • Differences can change the shape and function of a protein, or they can change how much protein is made, when it's made, or where it's made • In most cells, the genome is packaged into two sets of chromosomes: one set from our mother and one set from our father Cancer is a disease of the genome • In cancer cells, small changes in the genetic letters can change what a genomic word or sentence means • A changed letter (A, C, T, G) can cause the cell to make a protein that doesn’t allow the cell to work as it should • Scientists can discover what letter changes are causing a cell to become a cancer • The genome cancer cell can also be used to tell one type of cancer from another • In some cases, studying the genome in a cancer can help identify a subtype of cancer within that type Cell, Chromosome, DNA, RNA, Gene, Protein, Amino Acid • Human have 23 pairs of chromosomes in every cell (22 pairs of autosomes and 1 pair of gonosomes (XY male, XX female) • All DNA complex molecules containing deoxyribose acids in a specific order determined by the base sequence of 3,2 Billion nucleotides (Adenine, Cytosine, Thymine, Guanine) including 20,000 to 25,000 genes coding a protein • RNA is a molecule composed of nitrogenous bases (Guanine, Uracil, Adenine, Cytosine) similar to DNA but containing ribose rather than deoxyribose. RNA is formed upon a DNA template. There are several classes of RNA molecules.(messager RNA : mNRA, transfer RNA : tRNA and ribosomal RNA : rRNA) • Protein is a large molecule (enzyme, hormone, antibody…) composed of one or more chains of amino acids Where are we heading about fight against cancer ?
- 9. 9 WORLD CANCER STATISTICS 2018 Source : International Agency for Research on Cancer Where are we heading about fight against cancer ? Cancer burden rises to 18.1 million new cases and 9.6 million cancer deaths in the world Cancer is the second leading cause of death in the world The most common cancers in the world are (in decreasing order) those of the lung, breast, colorectum and prostate
- 10. 10 CANCER RISKS FACTORS • Tobacco • Alcohol • Nutrition • Certain class of drugs • Genome • Physical inactivity • Radio frequency • Phytosanitary products • Pollution • Radio activity • Skin exposure (sun, ultraviolet light) • Infectious agents (HPV, HBV, HCV, HIV…) Source : American Institute for Cancer Research Where are we heading about fight against cancer ?
- 11. 11 CANCER MANAGEMENT • Health history review • Physical examination • Laboratory tests (blood, urine…) • Biopsy • Medical imaging (X-ray, PET/CT, MRI, Ultrasound…) • Endoscopy • Genetic tests • Surgery • Chemotherapy • Radiotherapy (Curie-Therapy, Proton Therapy) • Immunotherapy • Genetic CRISPER Cas9 • Stem cell transplant • Hyperthermia • Photodynamic therapy • Laser • Blood products, transfusion • Psychiatry • Psychology • Sophrology, Meditation, Mindfulness, Wellness • Dietetic • Speech therapy • Kinesitherapy • Spiritual • Rehabilitation • Social work • Volunteer Diagnostics Treatments Supportive Cares Where are we heading about fight against cancer ?
- 12. 12 CANCER MEDICINE MODEL Personal medicine taking into account the patient genetic or protein profile Preventive medicine taking into consideration health problems by focusing on wellness and not disease Predictive medicine indicating the most appropriate treatments for the patient and trying to avoid drug reactions Participative medicine leading patients to be more responsible for their health and care Proof medicine proving medical service to patients, particularly when it is based on connected health and remote medicine Pathway medicine connecting between different medical and para-medical actors, outpatient medicine, digital hospital and home care Cancer Medicine of the Future Where are we heading about fight against cancer ?
- 13. 13 • Personal medicine Vision that all people one day will be offered customized care, with treatments that match our genomic profiles and personal histories FUTURE OF THE CANCER TREATMENT • Immunotherapy drugs Immunotherapy is the fruition of a century-old idea: that a person’s own immune system can be stimulated to fight cancer • Cell-based therapies Patient’s own T cells are directly manipulated to more readily attack cancer cells. In this treatment, T cells are collected from a patient’s blood, genetically engineered to recognize certain proteins on cancer cells, and loaded back into the patient’s bloodstream • Epigenetic therapies Cancer could be treated in a different way, by transforming cancer cells back to normal rather than destroying them. CRISPR technology is used to easily alter DNA sequences and modify gene function. The protein Cas9 (or "CRISPR-associated") is an enzyme that acts like a pair of molecular scissors, capable of cutting strands of DNA • Battling metastases Metastatic tumor cells have a remarkable tendency to cling to blood vessels, a survival mechanism that might be important for the spread of many types of cancer Where are we heading about fight against cancer ?
- 14. How Big Data Analytics Is Transforming Oncology ?
- 15. 15 ONCOANALYTICS NEEDS • Providing the best diagnosis and treatment plans for the patient • Using the significant advances in data to better prioritize resources, lowering costs and improving patient outcomes • Developing a cognitive interface between clinicians and technology • Integrating various primary and secondary sources for enhance patients and treatment pathway insights • Providing a high performance architecture, scalable, available and secure supporting a large volume of various data How big data analytics is transforming Oncology ?
- 16. 16 ONCOANALYTICS MATURITY MODEL DESCRIPTIVE ANALYTICS What happened ? DIAGNOSTIC ANALYTICS What did it happen ? PREDICTIVE ANALYTICS What could happen ? PRESCRIPTIVE ANALYTICS What should be done ? Value Maturity ADVANCEDANALYTTICSANALYTTICS How big data analytics is transforming Oncology ?
- 17. 17 • Isoforms proteins biomarkers analysis Estimating cancer patients better survival with isoforms proteins biomarkers • Survivorship analysis for breast cancer Understanding what happens to patients after breast cancer diagnosis • Rapid diagnosis of rare leukemia Identifying cause and life-saving therapy faster for a rare leukemia • Improving early detection of breast cancer Detecting breast cancer at the earliest stage • Approach to developing cancer drugs Developing cancer drugs based on genetic mutations tumors • Biomarkers for relapse in lymphoma patients Understanding why some lymphoma patients relapse and others don’t ONCOANALYTICS USE CASES EXAMPLES How big data analytics is transforming Oncology ? Analytics The future of Cancer Care
- 18. 18 ARTIFICIAL INTELLIGENCE ONCOANALYTICS EMERGING TECHNOLOGIES MACHINE LEARNING Makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data Learns without being explicitly programmed to do so NATURAL LANGUAGE PROCESSING Helps computers communicate with doctors and researchers in their own language, making it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important COMPUTER VISION Trains computers to interpret and understand the visual world. Using images, machines can accurately identify and classify objects and then react to what they “see” DEEP LEARNING Makes the computation of multi-layer neural network feasible IoT PLATFORM CONVERSATIONAL AI PLATFORM KNOWLEDGE GRAPHS BLOCKCHAIN FOR DATA SECURTY QUANTUM COMPUTING Facilitates communication, data flows, devices management and applications functionalities Uses set of technologies that enable computers to simulate real conversations Creates a knowledge domain with the help of intelligent machine learning algorithms Creates continuously growing list digital records in packages (called blocks) which are linked and secured using cryptography Uses a quantum- mechanical phenomena such as superposition and entanglement to perform computation How big data analytics is transforming Oncology ?
- 19. 19 ONCOANALYTICS BIG DATA MODEL Variety Great data variety combining traditional clinical and administrative data, unstructured data (genomics, imaging, text…), socioeconomic data and social data Volume Great use of precision medicine, big data explosion in cancer care, especially as genomic and environmental data become more ubiquitous Velocity Rapidly increasing speed at which new data is being created by technological advances, and the corresponding need for that data to be integrated and analyzed in near real-time Value Significant advances in data to better diagnosis and treatment plans, the patient outcomes, better prioritize resources and lowering costs Veracity Good data quality. Data source is authoritative. Privacy and data protection safeguards. Data are regularly updated. Data are unambiguous, complete, easy to find, understand and use Big Data for OncoAnalytics How big data analytics is transforming Oncology ?
- 20. 20 ONCOANALYTICS ACTORS AND ROLES Gets personalized guidance on treatment decisions by matching each patient’s care against quality standards and data from patients like theirs Has access to metrics and tools that support high- quality efficient cares and costs Receives new insight for discovery through access to a massive body of de-identified patient care data to analyze patterns Has access to metrics and tools that support high- quality efficient data and IT costs Doctor Researcher Manager Administrator How big data analytics is transforming Oncology ?
- 21. 21 ONCOANALYTICS DATA SOURCES Imaging PharmaceuticsFinancial Biologic Genomics Radiology Literature Text Publications DocumentsStudies Video How big data analytics is transforming Oncology ? Patient data Trial data Research data Diagnosis Treatments Supportive cares Social Economic Environment
- 22. 22 ONCOANALYTICS PATIENT PRIVACY Patient data extraction Patient data loading Patient data transformation Private data Public data • Private patient data be used for medical and non-commercial purposes • Only patient and his doctor are authorized to access to private patient data de-identification Data Privacy Method Data Privacy Rules Integration Process • De-identifying patient identification code numbers are de-identified by replacing the original code number by a unique random code number, creating de-identified dataset. It’s reversible process. • Anonymization destroys all links between the de-identified dataset and the original dataset. It’s non-reversible process How big data analytics is transforming Oncology ?
- 23. 23 ONCOANALYTICS PRIVATE PATIENT DATA • Names • All geographic subdivisions (except country) • All elements of dates (except year) • Telephone numbers • Fax numbers • Email addresses • Social security numbers • Medical record numbers • Health plan beneficiary numbers • Account numbers • Certificate/license numbers • Vehicle identifiers and serial numbers • Device identifiers and serial numbers • URL • IP address • Biometric identifiers, including finger and voice prints • Full-face photographs and any comparable images • Any other unique identifying number, characteristic, or code How big data analytics is transforming Oncology ? Private patient data must be de-identified before integration within public databases according to data sharing agreements Data Sharing Agreement
- 24. 24 ONCOANALYTICS DISCOVERY Cellular : looking for patterns in the data of individual cancer cells to discover genetic biomarkers. Finding common features could help us better predict how individual tumors might mutate and what drug treatments might be most effective Patient : patient medical history and DNA data could be used to help define the best combination of therapies and recommendations for them, based on their tumor, their genes and the effects of treatments on patients with similar disease patterns and genetics Population : population data can be analyzed to inform treatment strategies for patients based on their different lifestyles, geographies, and cancer types • Molecular analysis • Survivorship analysis • Early detection of cancer • Cancer drugs development • Benchmarking and standardization cares • Cancer prevention and recommendations • Prediction with better precision • Diagnosis and treatments plan • … Collecting data from various sources by detecting patterns and outliers with the help of guided advanced analytics and visual navigation of data, thus enabling consolidation of cellular, patient and population data How big data analytics is transforming Oncology ? Use Cases Examples
- 25. 25 • Patient diagnosis, treatments and supportive cares • Expenses and investments • Billing and reimbursement • Profits and margin • Cost savings • Quality control of cares • Clinical trials ONCOANALYTICS CLINICAL Diagnostics Treatments Cares support Clinical trials Research Coordination Others Others ExpensesInvestments Time • Return on investment • Payments models • Donation • Forecasting • Predictive financial modeling • Health surveys • … Making use patient data to generate insights, take decisions, increase revenues, enhance cares diagnosis and coordination, minimize abuse and fraud and save on costs Technology How big data analytics is transforming Oncology ? Use Cases Examples Making use investments to generate insights and enhance diagnosis and treatments
- 26. Which Big Data OncoAnalytics Architecture Patterns ?
- 27. 27 BIG DATA ONCOANALYTICS ARCHITECTURE PATTERNS Big data analytics is the often complex process of examining large and varied data sets Big data warehouse is mainly technology, which stands on volume, velocity and variety of data sources Big data fabric is a system that provides seamless, real- time integration and access across the multiple data sources Big data management is the organization, administration and governance of large volumes of both structured and unstructured data Big data infrastructure is the consistent efficient hardware architecture, massively parallel, highly scalable and available to handle very large data volumes up to several petabytes Consisting an integrated and modular architecture for OncoAnalytics Which big data OncoAnalytics architecture patterns ? Doctor Researcher Manager Administrator Patient, Trial and Research data BIG DATA WAREHOUSE BIGDATAMANAGEMENYT AdministrationGovernance DATA DISCOVERY DATA CLINICAL BIGDATAINFRASTRUCTURE StorageServers BIG DATA FABRIC Transformation LoadingExtraction DATA LAKE BIG DATA ANALTICS Diagnostics Analytics Prescriptive Analytics Predictive Analytics Descriptive Analytics Visualization Network
- 28. 28 BIG DATA ANALYTICS PATTERNS Helping on doctors, researchers and managers to run different types of analytics, from dashboard and visualization to big data processing, real-time analytics, and machine learning to guide better decisions Prescriptive Analytics Predictive Analytics Diagnostic Analytics Descriptive Analytics Doctor ManagerResearcher Visualization Using data aggregation and data mining to provide insight into the past Using techniques such as drill-down, data mining and correlations Using techniques such as statistics, predictive modeling and forecasting Using techniques such as graph analysis, simulation, complex event processing, machine learning, neural networks Which big data OncoAnalytics architecture patterns ?
- 29. 29 BIG DATA WAREHOUSE PATTERNS Database Operating System Languages Connectors Hardware Software system Analytics Optimized System (Appliance) • Software system and hardware integrated • Languages (R, SQL, NOSQL…) • Unstructured/structured data • Components redundancy • Massively Parallel Processing • In-memory computing • Resources management • Partitioning, indexing • Column database or HDFS • Compression • Connectivity (Hadoop…) • Scalability, high Availability • Security Centralizing all data at any scale with flexible software and available architecture for massively parallel data processing on a network of lower costs commodity hardware Moving data processing to storage Appliance architecture Storage patterns Which big data OncoAnalytics architecture patterns ? Direct Attach Storage (DAS) • Improve data access performance • Storage inside each server (DAS) • Improve data access performances • Reduce network latency • Improve data flows
- 30. 30 BIG DATA FABRIC PATTERNS • Extraction phase : extracts data from various data sources • Transformation phase : transforms data for storing it in de-identify format or structure for the purposes of querying and analysis • Loading phase : loads data within the big data warehouse • Phases are running in real-time streaming or batch processing Data sources Transform Extract Extract Extract Extract Extract Extract Load Load Load Load Load Load Database Transform Database • While data is being extracted, the transformation phase is executed and the already received data are prepared for loading. As soon as there is some data ready to be loaded into the big data warehouse, the data loading kicks off without waiting for the completion of the previous phase • While data is being extracted, the already received data are prepared for loading. As soon as there is some data ready to be loaded into the big data warehouse, the data loading kicks off and transformation is executed in-database without waiting for the completion of the previous phase Combining relevant data residing in different sources and providing doctors, researchers and managers with a unified view of them Phases ETL ELT Data sources Which big data OncoAnalytics architecture patterns ?
- 31. 31 BIG DATA MANAGEMENT PATTERNS • Maintaining a full audit history across all data in a single place • Tracking, classifying and locating data to comply with governance and compliance rules • Visualizing the upstream and downstream lineage of data to verify reliability • Defining and automating complex data lifecycle activities with integrated metadata policies • Verifying access privileges • Searching metadata and visualizing lineage • Encrypting or decrypting data Organization Metadata Data Security Data Quality Master Data Management Data Life Cycle Management Doctors and researchers relationship, people management and costs control Managing data about other data generally referred to as content data (catalog, dictionary, taxonomy Data security management is a way to maintain data integrity and to make sure that the data is not accessible by unauthorized parties or susceptible to corruption of data Set of characteristics of data : completeness, validity, accuracy, consistency, availability and timeliness fulfills requirements Processes, policies, standards and tools that consistently define and manage the critical data to provide a single point of reference Managing information throughout its lifecycle, from requirements through retirement.. Data archiving and lineage Backup/Restore Storage Array Data restore • Monitoring and scheduling system • Hardware failure , disk or server crash, rack failure • Data deletion, data corruption • Site failure , disaster (fire, water, network, power…) • Backup and restore management Data backup Consisting an integrated, modular environment to manage application data and optimize data-driven over their lifetime Disaster recoveryPrimary Data replication Monitoring Administration Governance Which big data OncoAnalytics architecture patterns ?
- 32. 32 BIG DATA INFRASTRUCTURE PATTERNS • SQL • High availability • Limited scalability • Structured data • Limited data volume • Cluster • Up to several terabytes • SQL and NoSQL • Very high availability • Infinite horizontal scalability • Unstructured and structured data • Massively Parallel Processing • High data volume • Grid • Up to several petabytes Consisting efficient infrastructure from traditional SQL database to big data massively parallel, highly scalable and available to handle very large data volumes Traditional Infrastructure Big Data Infrastructure • CPU add-on • RAM add-on • Disks add-on • I/O controller add-on Infinite Horizontal scalability Scale-out • Server add-on • Network switch add-on • Appliance add-on • Rack add-on Limited Horizontal scalability Scale-out • Server add-on • Network switch add-on • Appliance add-on • CPU add-on • RAM add-on • Disks add-on • I/O controller add-on Which big data OncoAnalytics architecture patterns ? Limited Vertical scalability Scale-in Limited Vertical scalability Scale-in
- 33. 33 BIG DATA CLOUD PATTERNS Infrastructure as a Service (IaaS) Hardware OS Virtualization Database Software Platform as a Service (PaaS) Hardware OS Virtualization Database Software Software as a Service (SaaS) Hardware OS Virtualization Database Software Hospital Cloud Provider The Cloud is hosted within the hospital data center. It is tailored to needs and infrastructure. The data is located within the hospital data center. Investment costs are managed by the hospital The hybrid cloud is a mix of private cloud and public cloud. Cloud services are distributed according to the needs. Costs are split between investment and services The hardware and software are owned and managed by a cloud services provider. It is fast and inexpensive to set up. It adapts quickly to the fluctuation of needs. De-identify data is hosted by the provider. Costs services are billed for use PRIVATE CLOUD PUBLIC CLOUD HYBRID CLOUD Consisting an integrated, modular environment to manage application data and lowering investment costs Which big data OncoAnalytics architecture patterns ?
- 34. Which Big Data OncoAnalytics Solutions ?
- 35. 35 • New tools and techniques are required to efficiently process all information, more data sources emerge • There is no one cure-all for cancer, there is no single tool for data analytics • Supercomputing power required to rapidly process huge structured and unstructured data volume • Solution is classified into 6 domains : The solution lies in greater collaboration, working together to use multiple software each looking for specific features 1. BIG DATA FABRIC 2. BIG DATA WAREHOUSE 3. BIG DATA ANALYTICS 4. BIG DATA MANAGEMENT Which big data OncoAnalytics solutions ? 5. BIG DATA HADOOP 6. BIG DATA CLOUD PLATFORM NO ONE-SIZE-FITS-ALL SOLUTION
- 36. 36 Bid Data Fabric, Q2 2018 • Provides single enterprise-class solution for data integration, data quality, data profiling and text data processing • Allows to integrate data from multiple various sources • Provides rich connectivity to many sources and targets • Manages services BIG DATA FABRIC • Discovers hidden insights with a personalized analytics experience • Provides high performance analytics, analytical data preparation, data discovery • Provides superfast processing for large-scale data manipulation, exploration, advanced analytics, artificial intelligence and machine learning • Manages services Big Data Predictive Analytics and Machine Learning Solutions, Q3 2018 BIG DATA ANALYTICS Source : Forrester Source : Forrester • Complies with regulations • Executes health center programs based on secured data of quality • Ensures analytics and reports be trusted • Leverages understanding and knowledge with data and context • Measures, analyzes and visualizes the cares programs • Executes administration process (backup, disaster recovery…) Data Governance Stewardship and Discovery Providers Q2 2017 BIG DATA MANAGEMENT Source : Forrester Which big data OncoAnalytics solutions ? Big Data Warehouse, Q2 2017 • Provides flexible, high- performance, secure platform • Delivers powerful ready-to-run enterprise platform that is pre- configured and optimized specifically for big data • Builds platform for big data storage, refinement and analytics of structured and unstructured data • Connects to Hadoop • Manages services BIG DATA WAREHOUSE Source : Forrester BIG DATA MARKET ANALYSIS
- 37. 37 • Runs Hadoop deliver quick setup, higher performance and automation • Helps overcome these issues by optimizing the infrastructure with automation, balanced system resources, and integrated testing • Runs Hadoop framework • Uses Apache Spark and Storm in option • Manages services Big Data Hadoop-Optimized Systems Q2 2016 BIG DATA HADOOP Source : Forrester Which big data OncoAnalytics solutions ? HADOOP ADD-ON • HDFS : scalable, Fault tolerant, High performance distributed file system. namenode holds filesystem metadata. Files are broken up and spread over the datanodes • MAPREDUCE : software framework for distributed computation. JobTracker schedules and manages jobs. TaskTracker executes individual map and reduce tasks on each cluster node • YARN : foundation of the new generation of Hadoop. Architectural center that allows multiple data processing engines • SPARK : fast and general engine for large-scale data processing. Faster than MapReduce. combine SQL, streaming, and complex analytics • STORM : a system for processing streaming data in real time and data processing capabilities to Enterprise Hadoop • KAFKA : is a distributed streaming platform. Kafka brokers massive message streams for low-latency analysis in Enterprise Apache Hadoop HADOOP FRAMEWORK BIG DATA HADOOP MARKET ANALYSIS
- 38. 38 BIG DATA CLOUD MARKET ANALYSIS Forrester Wave : Global public cloud platforms For enterprise Developers, Q3 2016 Source : Forrester • Provides cloud solution with massive volumes of medical documents and patient data • Integrates big data fabric, big data warehouse, big data analytics and big data management • Provides artificial intelligence to the clinicians of open-domain question answering • Loads de-identified patient data • Manages cloud services BIG DATA CLOUD PLATFORMBig data fabric, big data warehouse, big data analytics and big data management integrated in only-one OncoAnalytics Cloud Platform AWS CLOUD MICROSOFT CLOUD GOOGLE CLOUDSAP CLOUD IBM CLOUD Watson for Oncology IBM FLATIRON ASCO VARIAN SEVEN BRIDGES IBM CLOUD Watson for Genomics OncoCloud OncoPeer CancerLinQ Cancer Genomics Cloud IBM GOOGLE CLOUD GOOGLE Google Genomics Which big data OncoAnalytics solutions ?
- 39. 39 BIG DATA OPEN SOURCE MARKET ANALYSIS CompuCell3D • Flexible scriptable modeling environment, which allows the rapid construction of sharable virtual tissue in-silico simulations of a wide variety of multi-scale, multi-cellular problems including angiogenesis, bacterial colonies, cancer, developmental biology, evolution, the immune system, tissue engineering, toxicology and even non-cellular soft materials Cell-based Chaste • Mathematical and computational models of biological and physiological systems are rapidly increasing in complexity. This is especially true of fields such as cancer modeling, where the amount of available biological data is increasing exponentially. Modeling approaches therefore span the range from detailed models of molecular level processes, right through to biomechanical models at the tissue level PhysiCell • Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal "virtual laboratory" for such multicellular systems simulates both the biochemical microenvironment (the "stage") and many mechanically and biochemically interacting cells SimCells Dichloroacetetic acid (DCA) cell simulator used to simulate cellular and biochemical processes, calculated by a DCA algorithm. The user, through the use of the interface may create : small molecules, membrane, membrane proteins (which can only exist in membranes), protein/RNA molecules, DNA molecules (which are non-mobile), and genes (which can only exist in membranes). Four open source software proved particularly useful to model tumor growth and the effects of therapies and acknowledged in the cancer modeling Which big data OncoAnalytics solutions ? OncoAnalytics Discovery at cellular level Open source software is software with source code that anyone can inspect, modify, and enhance. "Source code" is the part of software that most computer users don't ever see; it's the code computer programmers can manipulate to change how a piece of software a program or application works
- 40. Which Big Data OncoAnalytics Solution Examples ?
- 41. 41 BIG DATA ONCOANALYTICS ARCHITECTURE Software Architecture • Data Discovery cloud platform • Data Lake Hadoop appliance • Data Clinical appliance • Data Integration software • Data Analytics software • Data Management software • Software support Technical Architecture • Servers • Storage • InfiniBand & Ethernet Network • Hardware support Which Big data OncoAnalytics solution examples ? Doctor Researcher Manager Administrator Patient, Trial and Research data BIG DATA WAREHOUSE BIGDATAMANAGEMENYT AdministrationGovernance DATA DISCOVERY DATA CLINICAL BIGDATAINFRASTRUCTURE StorageServers BIG DATA FABRIC Transformation LoadingExtraction DATA LAKE BIG DATA ANALTICS Diagnostics Analytics Prescriptive Analytics Predictive Analytics Descriptive Analytics Visualization Network
- 42. 42 ANALYTICSSOURCES BIG DATA ONCOANALYTCS DATA FLOWS DISCOVERY CLINICAL Public data Public data Private Patient data DATA LAKE Private Trial data Private Research data Private data Researcher Manager Doctor Doctor Doctor Public data de-identified IMANAGEMENT Administraor Public data de-identified Which Big data OncoAnalytics solution examples ?
- 43. 43 BIG DATA ONCOANALYTICS INFRASTRUCTURE 40Gb Infiniband 1 to 10Gb Ethernet 10Gb Ethernet PRIMARYSITE Doctor ManagerResearcher Administrator Oncology Platform MANAGEMENT Web/Application Servers DISCOVERY SECONDARYSITE INTEGRATION Web/Application Servers ANALYTICS Web/Application Servers MANAGEMENT Web/Application Servers INTEGRATION Web/Application Servers ANALYTICS Web/Application Servers Asynchronous data replication from primary to secondary site 10Gb Ethernet Web Browser Which Big data OncoAnalytics solution examples ? Oncology Platform DISCOVERYCLINICAL Analytics Appliance CLINICAL Analytics Appliance DATA LAKE Hadoop Platform DATA LAKE Hadoop Platform BACKUP Backup Array BACKUP Backup Array Web Browser
- 44. How Getting Start Big Data OncoAnalytics Project ?
- 45. 45 BIG DATA ONCOANALYTICS PROJECT - GETTING START • Have a big data insight – understanding concepts of big data, domains, needs, opportunities, market, social behavior • Discuss with key people – doctors, researchers, managers… • Identify new skills and competencies – data scientists, architects… • Identify alliances – services providers, software and hardware vendors • Build a case – few public proof points or metrics to leverage, create much of it from scratch, focus on single problem and only a handful of metrics • Use internal data in priority – Electronic Medical Records exist in the cares center. Integrate external data in second step • Evangelize big data in financial and social terms – make an evangelization deck, explain how the cares center will benefit from big data and the financial and social opportunities it creates. The objective is for clinicians to embrace it and include it in their plans. Make it friendly • Identify a sponsor – here’s the challenge with big data technology, looking for someone dynamic, who understands the stakes and believes that technology can drive competitive advantage • Capture metrics and use them to tell a story – identify only a few metrics that will be measure and tell a story, people will remember the story long after they forget the numbers in the case • Emphasize on big data opportunity – some people can’t see big data, it’s hard to get passionate about abstract concepts. Need to visualize the problem and the opportunity. Do a demonstration of big data project and show what new results will occur. A picture is always worth than a thousand words How getting start big data OncoAnalytics project ?
- 46. 46 BIG DATA ONCOANALYTICS PROJECT RunningBuilding • Oncology goals • Oncology needs • Actors and roles • Data sources, • Data flows • Data model • Data volume • Software and tools • Macro architecture • Documentation • Data structures • Data flows • Algorithms and data processing • Dashboards and reports • Architecture design • Data administration and governance • Test plan • Documentation DesigningConsulting • Databases building • Data integration • Data analytics • Dashboards and reports • Data administration and governance • Application deployment • Hardware, software installation • Tests • Documentation • Hardware and software support according to the Service Level Agreement • Hardware requirements • Monitoring and scheduling • Data security • Master data • Data quality • Data life cycle • Data backup and disaster recovery • Documentation Managing • Planning • Team organization and coordination • Meeting with doctors, researchers, manager and IT people IT Consultant IT Architect IT Consultant IT Architect IT Developer IT Engineer IT Engineer IT Administrator IT Manager How getting start big data OncoAnalytics project ?
- 47. Which Big Data OncoAnalytics Value Proposition ?
- 48. 48 • Working with clinicians, researchers and IT engineers together to fight against cancer • Patients diagnosis, treatments and care support plan • Cancer predictions and recommendations • High-quality efficient cares and costs • Best practices, publications and cancer literature sharing across many care centers and laboratories • EMR and EHR integration (best practices, documents, genomic, biology, radiology, pharmacology, financial…) • Advanced algorithms based on natural language, artificial intelligence, machine learning and deep learning • Discovery and clinical unified platform integrating hardware software and added value services • Available, scalable and flexible high performance platform • Hardware and software vendors partnership • References architectures BIG DATA ONCOANALYTICS VALUE PROPOSITION Which big data OncoAnalytics value proposition ?
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- 50. 50 • https://www.iarc.fr • https://en.e-cancer.fr • https://www.cancer.org • https://www.cancer.gov • https://www.cancer.net • globalcancermap.com KEY LINKS Big data OncoAnalytics