Research problem

Emerging Techniques

• Modern genomic technologies enable people to acquire vast amount of data.
• Artificial Neural Networks (ANN) show new ways to do feature extraction.

Old methods

• Traditional methods are focused on known-unknowns, i.e. to discover a new disease-related gene. But not suitable for discovering new processes.

Emerging Techniques

Old methods

VS

Emerging Techniques

Old methods

VS

Apply ANN (dA) to large dataset providing new insights

Background Knowledge

Before we start ...

Autoencoder

Kudos to image author(s)

Autoencoder

Kudos to images author(s)

C

A

T

Autoencoder

Kudos to image author(s)

Nonlinearity

dA - Denoising Autoencoder

Kudos to image author(s)

dA - Denoising Autoencoder

Kudos to images author(s)

C

A

T

Key Challenge

Doctor: You need to take a pound of broccoli stalk because node 64726 in our network said so.

Doctor: You need to take a pound of broccoli stalk because node 64726 in our network said so.

Something goes wrong!

Interpretation is much important

• Linking features to sample characteristics
• Linking feature to transcription factors
• Linking feature to patient survival*
• Linking feature to biological pathways

Interpretation is much important

• Linking features to sample characteristics

Sample Characteristics

• ER status (ER: estrogen receptor. Estrogen is a specific hormone from females)
• Molecular subtypes
• ...

How

How

How

How

Interpretation is much important

• Linking features to sample characteristics
• Linking feature to transcription factors
• Linking feature to patient survival
• Linking feature to biological pathways

Interpretation is much important

• Linking feature to patient survival

The output features behave bimodally 

Cut off at 0.5 for each feature

Binarize the feature, evaluate the predictive power toward patient survival

Interpretation is much important

• Linking features to sample characteristics
• Linking feature to transcription factors
• Linking feature to patient survival
• Linking feature to biological pathways

Datasets

Performance of hidden nodes in classifying tumor from normal samples.

Performance of hidden nodes in classifying ER + from ER - samples.

Conclusion

• Features automatically generated by dA has good predictive power
• Using unsupervised model can fill the gap of the current method.
• Future work may involve supervised learning on these auto-generated features to increase accuracy.