Pistoia Alliance debates AI in life science
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The webinar explores some of the current opportunities for AI within Life Science and look ahead to what we can expect to see over the coming years. These are the accompanying slides.
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Pistoia Alliance debates AI in life science
- 1. 2 October, 2017 Where will AI/Deep learning have an impact in Life Science & Health Pistoia Alliance Debates 27 September 2017 Nick Lynch
- 2. This webinar is being recorded
- 3. ©PistoiaAlliance Poll Question 1: What role do you play in your company A. IT B. data scientist/informatician C. scientist D. information professional E. other
- 4. ©PistoiaAlliance The Panel 4 Peter Henstock Senior Manager, Business Technology group, Pfizer Sean Ekins, CEO and Founder Collaborations Pharmaceuticals David Pearah , CEO, HDF Group
- 5. Poll Question 2: What is your familiarity with AI/Deep learning? A. I am using AI/Deep learning B. I am experimenting with AI/Deep learning C. I am aware of AI/Deep learning D. I know next to nothing about it
- 6. ©PistoiaAlliance David Pearah, CEO HDF Group Learning from other industry sectors
- 8. ©PistoiaAlliance Data Science and Artificial Intelligence Hype? Yes. Real Substance and Impact? Yes.
- 9. ©PistoiaAlliance Artificial Intelligence (AI) Field of computer science that allows computers to “seem human” in some way by replicating human cognitive functions (e.g., learning and problem solving) Machine Learning (ML) Subset of AI approaches that gives computers the ability to learn from and make predictions on data without being explicitly programmed (i.e. learn on their own from new data) Deep Learning (DL) Simulates many (deep) hierarchical layers of neurons in the human brain: by running large amounts of data through this simulation, it develops its own understanding of the concepts inherent in the data
- 10. ©PistoiaAlliance • Storage and processing power as a cheap, on-demand utility: • Graphics Processing Units (GPUs) • Cloud computing allows affordable GPUs at scale • Critical mass in open source software community • Powerful new applications for known AI techniques (e.g., deep learning) • Global, online AI community sharing advances daily • Open source software from the community and tech giants (e.g., Google TensorFlow) • Huge AI investments from tech titans who see AI as a strategic asset • Exponential growth in data to analyze using DL. In life science: • Electronic health records • Genomic data • Patient monitoring and treatment devices (e.g., EKG, Pulse, Oxygen, IV Pumps, etc..) • Consumer biomonitoring devices (e.g., FitBit, Apple Watch, smartphones) • Environmental data • Data registries • Medical literature and supporting primary data Deep Learning (DL): Why Now?
- 11. ©PistoiaAlliance
- 12. ©PistoiaAlliance Artificial Intelligence Machine Learning Knowledge Representation and Reasoning Automated Planning Natural Language Processing Multi-Agent Systems Robotic s Reinforcement Learning Supervised Learning Semi-supervised Learning Unsupervised Learning Markov Decision Processes (e.g. Policy iteration) Classification/Regression Clustering Summarization Anomaly Detection Distance-based (e.g.: LOF) Model-based (e.g.: MMPP) Graphical and Statistical (e.g.: Exponential Smoothing) Dimensionality Reduction (e.g. PCA, SVD) Association and Sequence models (e.g.: apriori algorithm) Density-based (e.g.: DBSCAN) Hierarchical (e.g.: Single-linkage) Centriod-based (e.g.: K-Means) Distribution-based (e.g.: Mixture of Gaussians) Instance-based (e.g.: KNN, CBR) Decision Tree (e.g.: Random Forest) Artificial Neural Networks (e.g. Perceptron) Bayesian Networks (e.g.: Naïve Bayes) Kernel-based (e.g. SVM)
- 13. ©PistoiaAlliance Creating artificial intelligence solutions using supervised learning with a neural network: Dogs 2 Collecting and annotating data sets 3 Training via Computation 4 Independent Validation of the Algorithm 5 Deployment and Monitoring 1 Define a Narrative AI Use Case Cats
- 16. ©PistoiaAlliance 16 I/O library optimized for scale + speed Self- documenting container optimized for scientific data + metadata Users who need both features HDF5 + Deep Learning 1 6 HDF5 already integrated into every major DL Framework (TensorFlow, Caffe, Keras, etc.)
- 17. ©PistoiaAlliance v v v What does the HDF Group do? • HDF5 Community Edition + Enterprise Edition • Connectors: ODBC + Cloud (Beta) • Add-Ons: compression + encryption • HDF Support Packages (Basic + Pro + Premier) • Support for h5py + PyTables + pandas (NEW) • Training • HDF: new functionality + performance tuning for specific use cases • HPC software engineering with scientific expertise • Deep Learning expertise Products Support Consulting 1 7
- 18. ©PistoiaAlliance Questions? Comments? Dave Pearah, CEO David.Pearah@hdfgroup.org www.hdfgroup.org
- 19. ©PistoiaAlliance Poll Question 3: What is your company’s primary use for AI/Deep learning A. Early Discovery/ Pre-clinical B. Development & Clinical C. Imaging Analysis D. Other E. Don’t use AI
- 20. Sean Ekins, CEO, Collaborations Pharmaceuticals, Inc. Deep Learning in Pharmaceutical Research
- 21. ©PistoiaAlliance AI in Pharma is not new! 222 October, 2017 • Neural Networks • Genetic algorithms • SVM • ‘Used’ for decades • Why it never took off: – Compute power – Lack of training data – Limited support – Most Scientists did not believe them…needed a paradigm shift – Pharma mergers culled 10,000’s scientists DEEP LEARNING
- 22. ©PistoiaAlliance Big data in 2002 vs 2017 232 October, 2017 Now -TB data ~19,000 cpds
- 23. ©PistoiaAlliance HTS phenotypic screen Molecule Screening database Machine learning models Vendor library Top scoring molecules assayed in vitro Bernoulli Naive Bayes, Logistic linear regression, AdaBoost Decision Trees, Random Forest, Support Vector Machines (SVM), Deep Neural networks (DNN) Speeding drug discovery with AI ▶ Molecular pattern recognition of biological data ▶ Descriptors identify these patterns ▶ Define active and inactive features ▶ Used to generate predictions for drug activity at a certain target (organism, protein of interest)
- 24. ©PistoiaAlliance What is Deep Learning 252 October, 2017
- 25. ©PistoiaAlliance Deep Learning uses 262 October, 2017 • facial recognition algorithms – Facebook tagging photos • self-driving cars • robot assistants http://tinyurl.com/hak4lcv http://tinyurl.com/y8vjv8lp
- 26. ©PistoiaAlliance Deep Learning in Pharmaceutical Research 272 October, 2017 • Bioinformatics – Protein disorder – Refine docking complexes – Model CLIP-seq data – High content image analysis data – Biomarkers – Protein contacts – Cancer diagnosis • Pharmaceutical – Solubility – Gene expression data – Formulation – QSAR – Merck DL out performed random forests in 11 /15 and 13/15 datasets – Tox21 Where else could we apply DL in drug discovery? Pharmacoeconomics?
- 27. ©PistoiaAlliance Gaps in Deep Learning for Pharmaceutical research 282 October, 2017 • TensorFlow • Deeplearning4j • Facebook (Torch) • Microsoft (CNTK) • Which metrics to use? • Which descriptors? • Are the DL over training? • Lack of prospective testing.
- 28. ©PistoiaAlliance Recent Deep Learning papers 292 October, 2017
- 29. ©PistoiaAlliance Comparison of TB Machine-Learning Models (1µM) 302 October, 2017 Logistic Regression (LR) Adaboosted Decision Trees (ADA) Random Forest (RF) Naive-bayes (BNB) Support Vector Machines (SVM) Deep Neural Networks (DNN) ▶ TB data from literature ▶ ~19,000 molecules ▶ ECFP6 descriptors ▶ Used previously with Bayesian methods ▶ Multiple metrics ▶ 5 fold cross val ▶ Classic ML -Open source Scikit-learn http://scikit- learn.org/stable/ ▶ Deep Neural Networks (DNN) using Keras https://keras.io/, and Tensorflow www.tensorflow.org,
- 30. ©PistoiaAlliance Small scale Machine Learning comparison 312 October, 2017 • Comparing different algorithms and using FCFP6 fingerprints • Deep learning seems to improve model ROC statistics in 4/6 cases. • Data sets range from 100s – >300K • All classification models • Next steps evaluate all the datasets in ChEMBL, PubChem, ToxCast etc 31 Korotcov et al., Submitted
- 31. ©PistoiaAlliance Building Machine Learning models Assay Central 322 October, 2017 • Curate data and build models • Provide models and collections as jar files Add DL algorithm to Assay Central
- 32. ©PistoiaAlliance Acknowledgments 332 October, 2017 • Kim Zorn Assay Central Guru • Alex Clark Assay Central • Thomas Lane PhD intern UNC • Dan Russo PhD intern Rutgers • Jacob Gerlach High School Intern • Valery Tkachenko Deep Learning Consultant • Alex Korotcov Deep Learning Consultant • Thanks also to: Renee Arnold, Peter Swaan Funding from NIGMS NIH R43GM122196
- 33. ©PistoiaAlliance Poll Question 4: What is the greatest barrier to application of AI at your org A. Technical & skills expertise B. Access to data C. Data quality D. Management support/understanding E. Other
- 34. Peter Henstock - Business Technology, Pfizer Inc. Why is pharma lagging in the AI arena whereas other industries are already transformed
- 36. ©PistoiaAlliance What does Waze do? • Obtain public data: maps & locations • Acquire & organize data for AI analyses – Leverage historical traffic data – Integrate new traffic information • Utilize AI algorithms – Fastest route predictions • Present timely information through UI
- 37. ©PistoiaAlliance Why Isn’t AI Working Yet for Pharma? drugwaze Rescreening 55% chance of new series 6 weeks $1.2MM Optimization 14% issue series 1 Solubility cause 23% issue series 2 Safety cause 5% issue series 3 8.2 months to Phase 1 Predicted FDA approval chance: 37% Recommended actions: 1) Resolve the
- 38. ©PistoiaAlliance Keys to Success • Obtain public data • Acquire & organize data for AI analyses • Utilize AI algorithms • Present timely information through UI
- 39. ©PistoiaAlliance Need for a Chief Data Officer Value Proposition https://www.123rf.com/photo_17347316_businessman-pulling-rope-on-white-background.html $ $ $ Acquire and organize data for AI
- 40. ©PistoiaAlliance Analytics First, Then AI • Readiness for Analytics & AI –Curated data sources –Automated data management processes –Structured data analytics • “If your company isn’t good at analytics, it’s not ready for AI” – Harvard Business Review June 7, 2017
- 41. ©PistoiaAlliance Keys to Success • Obtain public data • Acquire & organize data for AI analyses • Utilize AI algorithms • Present timely information through UI
- 42. ©PistoiaAlliance Harvard Business Review October 2012
- 43. ©PistoiaAlliance Modern Data Scientist Math Statistics AI Hacking Database Computing Story Telling Visualization Domain Knowledge Analysis
- 44. ©PistoiaAlliance AI & Pharma Skillset Intersection https://www.quora.com/What-is-the-difference-between-Data-Analytics-Data-Analysis-Data-Mining-Data-Science- Machine-Learning-and-Big-Data-1 Software Engineering Bioinformatics Architecture & Systems Clinical Statistics HPC/Linux Farm AI & Machine Learning Scientists
- 45. ©PistoiaAlliance Does Pharma Have the Right Skills? ManagementBusiness Computer Science Biology Chemistry Medicine Law Statistics Physics BS MS/MBA PhD/MD/JD
- 46. ©PistoiaAlliance Does Pharma Have the Right Skills? ManagementBusiness Computer Science Biology Chemistry Medicine Law Statistics Physics BS MS/MBA PhD/MD/JD Need depth & breadth across AI areas
- 48. ©PistoiaAlliance Threat of High Salaries for “Expertise” Paul Minton: Waiter ($20K) data scientist ($100K) “As Tech Booms, Workers Turn to Coding for Career Change”. July 28, 2015 New York Times
- 50. ©PistoiaAlliance AI is a harder concept to grasp • Pharma & IT grasp replacement technologies – Virtual machine replaces physical machine – Cloud storage replaces local disks – Agile replaces waterfall method – High Throughput Screening replaces “screening” – High Content Screening replaces imaging • AI and Machine Learning – Provide a data-driven complement to many disciplines – Apply from early discovery to marketing – Span journals, data, omics, images, decision-making
- 51. ©PistoiaAlliance Volume of Tasks • Easy to develop AI solutions around a single task – Waze navigates – Amazon sells – LinkedIn links – Facebook advertises • Pharma/Biotech tasks are varied – Text mining for targets – Screening and imaging technologies – Using ‘Omics – Drug optimization – Clinical trials – Patient reports and communication – Predictions on activity, safety, trial enrollment, outcomes…
- 52. ©PistoiaAlliance Machine Learning Methods of AI ML Mastery
- 55. ©PistoiaAlliance AI Is Having a Stifled Impact in Pharma • Bottom-Up Proof Cycle – Scientific domain culture – Continually need to prove AI’s value to every group – Leveraging 1 data set at a time for 1 AI problem – Gains are localized to small groups • Minimal investment – Sitting on more data than most industries – Failing to analyze and leverage this data – Hiring less AI expertise than small tech startups – Relying on expensive external collaborations
- 56. ©PistoiaAlliance How to Succeed 1) Organize the data for AI “Data, rather than software, is the barrier” 2) Invest in AI talent “Simply downloading and “applying” open-source software to your data won’t work. AI needs to be customized to your business context and data. This is why there is currently a war for the scarce AI talent that can do this work.” 3) Develop an AI strategy “After understanding what AI can and can’t do, the next step for executives is incorporating it into their strategies. [This] is the beginning, not the end….” What Artificial Intelligence Can and Can’t do Now” Harvard Business Review Nov 9, 2016 Andrew Ng
- 57. ©PistoiaAlliance Audience Q&A Please use the Question function in GoToWebinar
- 58. ©PistoiaAlliance Beyond BMI: Body Composition Phenotyping in the UK Biobank The next Pistoia Alliance Discussion Webinar: Date: October 25, 2017 check http://www.pistoiaalliance.org/events/ for the latest information
Editor's Notes
- So at a high level, there are five basic steps to building a supervised neural network to differentiate a dog from a cat in a picture. We define a narrative AI use case Then we collect and annotate data sets related to that use case Then we use computation to training an algorithm to accomplish the use case Then we conduct an Independent Validation of the Algorithm Finally, we can deploy the use case and monitor it for any issues that may come up
- But there’s a big problem…. What you see here is only a small portion of the photo that was submitted to this classification service
- This is the full photo! So as you can see, the story of this picture isn’t that Roo is a hound. It’s that Roo is a troublemaker who just shredded the Tilkin family’s couch! And this service missed both the couch and the culprit. That inability to appreciate the larger context is something that AI is still weak at doing.