Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

ICIC 2017: The Next Era: Deep Learning for Biomedical Research


Published on

Srinivasan Parthiban (VINGYANI, India)
Deep learning is hot, making waves, delivering results, and is somewhat of a buzzword today. There is a desire to apply deep learning to anything that is digital. Unlike the brain, these artificial neural networks have a very strict predefined structure. The brain is made up of neurons that talk to each other via electrical and chemical signals. We do not differentiate between these two types of signals in artificial neural networks. They are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Another buzzword that was used for the last few years across all industries is “big data”. In biomedical and health sciences, both unstructured and structured information constitute "big data". On the one hand deep learning needs lot of data whereas “big data" has value only when it generates actionable insight. Given this, these two areas are destined to be married. The couple is made for each other. The time is ripe now for a synergistic association that will benefit the pharmaceutical companies. It may be only a short time before we have vice presidents of machine learning or deep learning in pharmaceutical and biotechnology companies. This presentation will review the prominent deep learning methods and discuss these techniques for their usefulness in biomedical and health informatics.

Published in: Healthcare
  • I think you need a perfect and 100% unique academic essays papers have a look once this site i hope you will get valuable papers, ⇒ ⇐
    Are you sure you want to  Yes  No
    Your message goes here
  • Hello! High Quality And Affordable Essays For You. Starting at $4.99 per page - Check our website!
    Are you sure you want to  Yes  No
    Your message goes here

ICIC 2017: The Next Era: Deep Learning for Biomedical Research

  1. 1. `The Next Era: Deep Learning for Biomedical Research Srinivasan Parthiban ICIC 2017, Heidelberg, Germany October 23-24, 2017
  2. 2. Mastering the Game of Go 2016 2017
  3. 3. Pharma Industry has a problem: Cost to develop a New Drug > $ 2.6 B and Growing
  4. 4. Big breakthroughs happen when what is suddenly possible meets what is desperately needed Thomas L. Friedman
  5. 5. The Beginning of the AI Revolution in Pharma Fierce Biopharma 2016 BBC, August 2017 Nature, July 2017 DDD 2017 August, 2017
  6. 6. AI Annual Global Financing History Healthcare emerges as hottest area of investment
  7. 7. Artificial Intelligence Machine Learning Deep Learning Cognitive Science
  8. 8. The Deep Learning Recipe Algorithms ComputeBig Data
  9. 9. Neurons and the Brain neuron cell body synapse axon nucleus dendrites of next neuron axon tips neuron cell body nucleus synapse dendrites axon of previous neuron
  10. 10. Deep Learning Primitives Fully Connected Layer Convolutional Layer Recurrent Neural Network Layers Long Short Term Memory (LSTM) Cells
  11. 11. Dog
  12. 12. Radiology Pathology Dermatology The Algorithm Will See You Now
  13. 13. AI and Your Skin Source: Nature 542, 115–118 (02 February 2017)
  14. 14. Electronic Health Records
  15. 15. Electronic Health Records
  16. 16. Biopharmaceutical Research and Development Process
  17. 17. Biomedical Literature is filled with Billions of pages of useful information
  18. 18. Causes of Clinical Failure
  19. 19. Data Repositories Database Unique Compounds Experimental facts Main data types ChEMBL v.21 1,592,191 13,968,617 PubChem HTS assays and data mined from literature BindingDB 529,618 1,207,821 Experimental protein-small molecule interaction data PubChem >60M >157M Bioactivity data from HTS assays Reaxys >74M >500M Literature mined property, activity and reaction data SciFinder (CAS) >111M >80M Experimental properties 13C and 1H NMR spectra, reaction data GOSTAR >3M >24M Target-linked data from parents and articles AZ IBIS - >150M AZ in-house SAR data points OCHEM >600k >1.2M Mainly ADMET data collected from literature
  20. 20. +1 -2 -5 +1 +3 +5 Experiment with Different Moves Receive a Score for Each Move Interacting with the Environment Use Math to Represent the Goal of Walking Reinforcement Learning
  21. 21. The Dawn of a New Era for Data Driven Medicine? Multitask Learning
  22. 22. Generative Adversarial Networks
  23. 23. Neural Networks for Meaningful Leads Drug Database
  24. 24. One-Shot Learning You show Giraffe once to a 3 year old kid, you do not need to teach the kid again
  25. 25. Deep Learning in Drug Discovery
  26. 26. HIV Protease Inhibitors are Powerful anti-AIDS drugs Inhibitor Protease Quality Inhibitors for Quality Results
  27. 27. New Players are Entering the Healthcare Space
  28. 28. How to think about AI… Today 2040 2060 Artificial Narrow Intelligence Artificial General Intelligence Artificial Super Intelligence Less than human intelligence Equal to human intelligence Far Greater than human intelligence
  29. 29. Thank You Chennai, Tamil Nadu, The World is Going to Change With or Without You… Get Ready!