(Source: TEKsystems)







Deep Learning for Biomedical Discovery and Data Mining

A tutorial @PAKDD18, Melbourne, June 2018.

Slides (Part I; Part II)

Abstract: The goals of this tutorial are to provide the general PAKDD audience with knowledge and materials about a great venture for KDD research – the intersection between deep learning and biomedicine and to provide the deep learning community with relatively new, high impact research problems within biomedicine. The tutorial introduces the state of the field for deep learning, and argues how biomedicine is an ideal data–intensive domain. It gives a brief review of deep learning, covering classic neural architectures including feedforward, recurrent and convolutional nets and more advanced topics including CapsNet, powerful memory-augmented neural nets (MANN), as well as models for graph data. Two major subtopics of Genomics are covered: nanopore sequencing (which is about converting electrical signals into DNA character sequences), and genomics modeling (which is about making sense of the DNA sequences for multiple biological processes). For healthcare coverage is on data mining of Electronic Medical Records. Two main problems are considered: The first is modeling time-series and the second is mid-term health trajectories prediction. Then I will cover recent advances in data eficient methods: few-shot learning and deep generative models (RBM, VAE and GAN). This describes how to apply these advances to drug designs, and the future outlook into a 5-year horizon and beyond on the joint venture of deep learning and biomedicine.

Prerequisite: the tutorial does not require detailed prior knowledge of biomedicine or deep learning, but basic familiarity with machine learning is assumed.


Part I

Topic 1: Introduction (20 mins)
Topic 2: Brief review of deep learning (30 mins)

  • Classic architectures
  • Capsules & graphs
  • Memory & attention

Topic 3: Genomics (30  mins)

  • Nanopore sequencing
  • Genomics modelling

QA (10 mins)

Part II

Topic 4: Healthcare (40 mins)

  • Time series (regular & irregular)
  • EMR analysis: Trajectories prediction
  • EMR analysis: Sequence generation

Topic 5: Data efficiency methods (40 mins)

  • Few-shot learning
  • Generative models
  • Unsupervised learning of drugs

Topic 6: Future outlook

QA (10 mins)


Genomics & drug design

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Deep learning fundamentals
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