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.

II-SDV 2017: The Next Era: Deep Learning for Biomedical Research

2,148 views

Published on

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: Internet
  • Hey guys! Who wants to chat with me? More photos with me here 👉 http://www.bit.ly/katekoxx
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here

II-SDV 2017: The Next Era: Deep Learning for Biomedical Research

  1. 1. The Next Era: Deep Learning for Biomedical Research II-SDV Conference Nice, France 23 - 25 April 2017 Srinivasan Parthiban Parthys Reverse Informatics Chennai, Tamil Nadu, India
  2. 2. Silicon Valley Waves of Innovation
  3. 3. Artificial Intelligence Automation of intelligence
  4. 4. Artificial Intelligence Machine Learning Deep Learning Cognitive Science 1950s 1980s 2010s Early AI stirs excitement ML begins to flourish DL breakthroughs drive AI boom
  5. 5. Big Data Availability The World’s Technological Capacity to Store, Communicate, and Compute Information Hilbert, M., & Lopez, P (2011), Science, 332 (6025), 60-65
  6. 6. Computational Power TPU
  7. 7. Traditional Programming Computer Data Program Output Traditional Programming Vs Machine Learning Computer Abundant Data Output Program Machine Learning
  8. 8. Machine Learning Unsupervised learning Supervised learning Reinforcement learning Optimization "I know how to classify this data, I just need you(the classifier) to sort it."
  9. 9. Supervised Learning Optimization Supervised learning Monday stock prices Tuesday stock prices Optimization Supervised learning what we know what we want to know Transforms One Dataset into Another
  10. 10. Unsupervised Learning Optimization Unsupervised learning List of datapoints List of cluster labels Groups your data
  11. 11. Shallow Deep Unsupervised Neural Networks Probabilistic Models Supervised Supervised Boosting Perceptron SVM RBM AE Sparse Coding Decision Tree GMM Neural Net RNN Conv. Net D-AE DBN DBM BayesNP S P RBM: Restricted Boltzmann Machine, DBM: Deep Boltzman Machine, DBN : Deep Belief Network, GMM: Gaussian Mixture Model AE: Auto Encoder, D-AE: Denoising Auto Encoder SVM: Support vector machine, SP: sigma-pi (sum n product), RNN: recurrent neural Network BayesNP: Non-parametric Bayesian
  12. 12. Neurons and the Brain
  13. 13. A Mostly Complete Chart of Neural Networks (1 of 2)
  14. 14. (2 of 2)
  15. 15. AI is still Dumb You See AI Sees Lot of supervision (labeled data)
  16. 16. Convolutional Neural Network
  17. 17. Image Classification/Captioning Cat Dog Horse Elephant Tiger Training ? Inputs Outputs
  18. 18. Convolutional Neural Network It’s an elephant!
  19. 19. Recurrent Neural Networks (RNN) 𝒙 𝒕: the input at time step 𝑡 𝒔 𝒕: the hidden state at time 𝑡 𝒐 𝒕: the output state at time 𝑡 Prediction of next word: the clouds are in the sky I grew up in France ………… ….. I speak fluent French The issue : Vanishing Gradient over time
  20. 20. LSTM and GRU Long Short-Term Memory i - input gate f – forget gate o – output gate c – memory cell and c˜ - new memory cell content Gated Recurrent Unit z – update gate r – reset gate h - hidden state h˜ - new hidden state LSTM GRU
  21. 21. Design Patterns for RNN Image captioning Sentiment analysis Machine translation Classify image frame by frame A man sitting in rooftop restaurant with his laptop Welcome to France Bienvenue en France II-SDV Conference is absolutely a great event Image Classification Cat
  22. 22. A group of people shopping at an outdoor market. There are many vegetables at the fruit stand. Machine image recognition and descriptive captions generated Language Generating RNN Vision Deep CNN
  23. 23. Deep Learning is Hot
  24. 24. Cars are now driving themselves …
  25. 25. The Technology is Working March 2016: World Go Champion beaten by machine
  26. 26. Medical Imaging & Diagnostics AI versus MD What happens when diagnosis is automated?
  27. 27. Radiology Pathology Dermatology The Algorithm Will See You Now
  28. 28. Automatic Detection of Metastatic Breast Cancer
  29. 29. Dermatologist-level classification of skin cancer with deep neural networks Procedure for calculating inference class probabilities from training class probabilities Source: Nature 542, 115–118 (02 February 2017)
  30. 30. First FDA Approval For Clinical Cloud-Based Deep Learning In Healthcare Arterys Prostate MRI: An image is worth the 1000 blood tests. MaxwellMRI Deep Learning spots disease early using Chest-X rays Enlitic CT scans: Algorithms inform cardiovascular and metabolic state of patients, and predicts the risk of heart attack and stroke Zebra Medical Vision
  31. 31. AI heatmap: Deals Distribution by Category Q1‘12-Q’17 (as of 3/23/17) Source: CBinsights Healthcare emerges as hottest area of investment
  32. 32. Binding Affinity from Features of Small Molecules and Biological Targets
  33. 33. Molecules to Features to Properties
  34. 34. 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
  35. 35. Architecture of Adversarial Autoencoder (AAE) for New Molecule Development Oncotarget, 2017, Vol. 8, (No. 7), pp: 10883-10890
  36. 36. Toxicity Prediction
  37. 37. Our Preliminary Model for ADMET prediction We have compiled a robust library of over 155k records across 36 different ADMET properties to facilitate 10-fold cross validation and confirm scalability SMILES were used to represent the molecules in the database Converted each molecule into Descriptors 856 2D/3D descriptors and 1024 unique “fingerprints” We have implemented a process to identify the most important descriptors upfront and focus resources on those key data points Our analysis yielded 27 descriptors that helps predict %GS This subset is then fed into each of the 10 algorithms for model fitting Our initial results are promising. Pred Obs
  38. 38. Why Deep Learning? How do data science techniques scale with amount of data? Older learning algorithms Deep learning
  39. 39. Deep Learning Frameworks % of papers mentioning the framework in March 2017 the fraction of papers that mention the framework somewhere in the full text (anywhere — including bibliography etc). For papers uploaded on March 2017, we get the numbers in this table. % of papers framework has been around for (months) 9.1 tensorflow 16 7.1 caffe 37 4.6 theano 54 3.3 torch 37 2.5 keras 19 1.7 matconvnet 26 1.2 lasagne 23 0.5 chainer 16 0.3 mxnet 17 0.3 cntk 13 0.2 pytorch 1 0.1 deeplearning4j 14
  40. 40. The Rockstars of Deep Learning Yoshua BengioYann Lecun Geoff Hinton Andrew Ng IDSIA Switzerland Jürgen Schmidhuber
  41. 41. Compute Data Algorithm Algorithms Data Compute % of Budget 100% The Last Decade Now
  42. 42. AI reads Science Your Science Assistant
  43. 43. Thank you parthi@reverseinformatics.com

×