The document discusses the application and implementation of deep learning in various scientific fields, particularly in healthcare and drug discovery. It covers the importance of deep learning, its algorithms, and methodologies while emphasizing the role of data in achieving accurate results. Additionally, it highlights case studies and future directions for utilizing deep learning effectively in scientific research.

1. APPLICATION AND
IMPLEMENTATION OF
DIFFERENT DEEP
LEARNING
Jacky Chow (Jie Zou)

2. AGENDA
2
Part I. Background in Big Data and AI
Part II. Case Study – Coronavirus
Part III. Major Applications:
- digital and personalized health
- new drug discovery and drug development
- genomics
- radiology
- population health and disease management
- medical insurance
Part IV. Discussion Panel
AGENDA
• Part I. What?
• Part II. Why?
• Part III. How？
• Part IV. Conclusion

3. PART 1. WHAT？
3
This Photo by Unknown Author is licensed under CC BY-SA

4. What is Deep Learning?
4
● Deep Learning is a subfield of machine learning
concerned with algorithms inspired by the structure and
function of the brain called artificial neural networks.
Source：
https://machinelearningmastery.com/what-is-deep-learning/
https://livebook.manning.com/book/probabilistic-deep-learning-with-python/chapter-
2/v-6/12

5. 5
DEEP LEARNING IS HIERARCHICAL FEATURE
LEARNING OR LAYERED REPRESENTATIONS
LEARNINGAND HIERARCHICAL REPRESENTATIONS
LEARNING
“Deep learning algorithms seek to exploit the unknown structure in the input
distribution in order to discover good representations, often at multiple levels, with
higher-level learned features defined in terms of lower-level features”

6. 6
This Photo by Unknown Author is licensed under CC BY-SA
6
PART II. WHY？
In the field of machine
learning(ML), Numerous
studies have been
published resulting in
various models. And
deep learning (DL) has
recently begun to receive
Widely concerned, this is
mainly due to its better
performance than the
classic model.

7. PART II. WHY?
7
Most of the fundamental breakthroughs in the core problems of
machine learning have been driven by the progress of deep neural
networks.
From Predictions to
Understanding:
Identify the timing of buy
trade(or if an asset is
cheap/expensive, should
you buy/sell it)
Prediction Problems :
the most straightforward
way to apply deep
learning
Complex Transformations
of Input Data:
the amount of generated
data has increased
dramatically, DL can
provide efficient analysis
and automated processing.
Deep Learning can be used to used in all of these scientific fields
efficiently!

8. PART II：WHY？
Data is the life of Deep Learning
8
Generally, the more data you
provide, the more accurate the
results in the DL.
Coincidentally, there are huge
data sets in the scientific field,
such as PB-level data of animal
experiments, physical and
chemical experiments, patient
data, papers, and reports.
This Photo by Unknown Author is licensed under CC BY

9. PART III: HOW?
Our focus was on DL implementations for scientific
applications
9
And I will introduce other studies of the applications of DL in the
scientific discovery field on similar topics, such as:
 Deep learning applications in biological research
 The application of deep learning in medicine
 Set up structured data, such as organic compounds
 Model different invariances-apply the invariance of specific
Lie groups to molecular property prediction
Main:
From Survey
"A Survey of Deep
Learning for Scientific
Discovery "

10. PART III. HOW ?
How to use DL in Scientific Discovery?
10
Bayers’ theorem
1
Deep learning models
2
Deep Learning Libraries
and Resources
3
• Doing More with Less Data;
• Interpretability, Model Inspection and
Representation Analysis
4
Key Methods (Supervised Learning)

11. The basis of price prediction is probability theory and statistics, one of
the most important assumptions is the Bayers’ theorem.
11
PART III:HOW?
1
Bayers’ theorem

12. 12
PART III:HOW?
The proposed deep learning models that the source
papers have used for their research.
• Convolutional neural network(CNN)
• Recurrent Neural Networks（RNN)
• Long Short Term Memory (LSTM)
• Deep Belief Networks (DBNs)
2

13. 13
CONVOLUTIONAL NEURAL NETWORK(CNN)
Typical CNN architecture

14. 14
RECURRENT NEURAL NETWORKS（RNN)
In contrast to feed-forward neural networks, in
recurrent neural networks, data can flow in any
direction. They can learn the time series
dependency well.

15. 15
LONG SHORT TERM MEMORY
(LSTM)
LSTM are also good at learning from
sequential data, i.e. time series.

16. 16
DEEP BELIEF NETWORKS (DBNS)

17. 17
PART III:HOW?
Deep Learning Libraries and Resources that the source
papers have used for their research.
• Software Libraries for Deep Learning
• Research Overviews, Code, Discussion
• Models, Training Code and Pretrained Models:
• Data Collection, Curation and Labelling Resources
• Visualization, Analysis and Compute Resources
2

18. Application in Scientific
18
PART III. HOW?
• Key Methods (Supervised Learning)
Transfer Learning
Domain Adaptation
Multitask Learning
Weak Supervision (Distant Supervision)
3

19. Transfer learning is a classic scientific research model using
deep learning, and a similar model will be used in our master
project (AI for healthcare).
19
TRANSFER LEARNING

20. Application in Scientific
20
PART III. HOW? • Doing More with Less Data
 Self-Supervised Learning
 Semi-Supervised Learning
 Co-training
 Data Augmentation
 MIL
 GAN
 Meta-learning
• Interpretability, Model Inspection and Representation
Analysis
 Feature Attribution and Per Example Interpretability
 Model Inspection and Representational Analysis
open-sourced book：Interpretable Machine Learning
• Other studies
4

21. MIL is a supervised learning problem where individual examples are
unlabeled; instead, bags or groups of samples are labeled.
A bag is considered
negative if all its instances
are negative（A
bag is positive, if at
least one instance in
the bag is positive）.
It is used in Medical field, drug activity prediction, text categorization, image
retrieval and classification.
Sources: https://medium.com/swlh/multiple-instance-learning-
21
MULTI-INSTANCE LEARNING

22.  Memory-based approach
 Method based on predicted gradient
 Method of using Attention attention mechanism
 Learn from the LSTM method
 Meta Learning Method for RL
 Through the method of training a good base
model, and simultaneously applying to
supervised learning and reinforcement learning
 Ways to use WaveNet
 Ways to predict Loss
22
META-LEARNING

23. 23
META-LEARNING

24. 24
— OTHER STUDIES:
CNN should be the main method.

25. CONCLUSION
What can be done to improve and future directions and suggestions
25
Improvement, Future Directions,
and Suggestions
We can pay more attention to the latest developments, including GAN, Meta-learning…
-- Part iv:

26. 26
REFERENCES:
[1] A Survey of Deep Learning for Scientific Discovery
[2] Deep learning sharpens views of cells and genes
[3] Recent Advances of Deep Learning in Bioinformatics and Computational Biology
[4] Deep learning for biology
[5] Multiple Instance Learning with MNIST dataset using Pytorch
[6] Review of Multi-Instance Learning and Its applications
[7] Santoro, Adam, Bartunov, Sergey, Botvinick, Matthew, Wierstra, Daan, and Lillicrap, Timothy. Meta-
learning with memory-augmented neural networks. In Proceedings of The 33rd International Conference on
Machine Learning, pp. 1842–1850, 2016.
[8] Munkhdalai T, Yu H. Meta Networks. arXiv preprint arXiv:1703.00837, 2017.
[9] Ravi, Sachin and Larochelle, Hugo. Optimization as a model for few-shot learning. In International
Conference on Learning Representations (ICLR), 2017.
[10] Flood Sung, Zhang L, Xiang T, Hospedales T, et al. Learning to Learn: Meta-Critic Networks for
Sample Efficient Learning. arXiv preprint arXiv:1706.09529, 2017.https://arxiv.org/pdf/1706.09529.pdf
[11] Ravi, Sachin and Larochelle, Hugo. Optimization as a model for few-shot learning. In International
Conference on Learning Representations (ICLR), 2017.

27. THANK
YOU
Jacky Chow
jie.zou@sjsu.edu
SJSU MSSE-Data Science