Machine Learning in

Bioinformatics

b00901028 謝伊妍

b00901146 朱柏憲

b00901155 吳京達

Group 2

Google: Calico

IBM: Watson

Outline • Machine Learning Basics

• Machine Learning and Bioinformatics

• Example 1

• Example 2

• Conclusion

• Machine Learning Basics

• Machine Learning and Bioinformatics

• Example 1:

• Example 2:

• Conclusion

Outline

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x: area

: Parameters

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𝜃0 = 300 𝜃1 = 0

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𝜃0 = 100 𝜃1 = 0.1

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𝜃0 = 0 𝜃1 = 0.2

Cost Function:

Goal:

Cost Function

Gradient Descent

(for fixed , this is a function of x) (function of the parameters )

(for fixed , this is a function of x) (function of the parameters )

(for fixed , this is a function of x) (function of the parameters )

(for fixed , this is a function of x) (function of the parameters )

(for fixed , this is a function of x) (function of the parameters )

(for fixed , this is a function of x) (function of the parameters )

(for fixed , this is a function of x) (function of the parameters )

(for fixed , this is a function of x) (function of the parameters )

(for fixed , this is a function of x) (function of the parameters )

Why don’t we use Normal Equation?

Ans: Computation Time

Some common Machine Learning Algorithms

• K-neighbor

• Gradient Descent

• Neural Network

• etc.

Suggesting Course on Coursera:

1. Machine Learning

2. Neural Networks for Machine Learning

• Machine Learning Basics

• Machine Learning and Bioinformatics

• Example 1:

• Example 2:

• Conclusion

Outline

Figure from: Pedro Larranaga et al., “Machine learning in bioinformatics”

2005, BRIEFINGS IN BIOINFORMATICS Mag

1.Gene finding

2.Tumor detecting

3.Emotion recognition

4.…

• Machine Learning Basics

• Machine Learning and Bioinformatics

• Example 1:

• Example 2:

• Conclusion

Outline

Prediction of Individual Brain Maturity

Using fMRI

What is fMRI?

In This Research

• Algorithm: MVPA(multivariate pattern analysis) voxels based on GLM SVMs(support vector machines)

• Data: fcMRI data from 238 rs-fcMRI scans

SVM

• For a set Rd , trying to find a hyper-plane classify data as two groups

SVM

• H1 H2 : support hyper-planes

• Hyper-plane: WTX = - b (WTX + b = 0)

H1: wTx +b = +1

H2: wTx +b = −1

• H1 to plane: |1-b|/|w|

H2 to plane: |-1-b|/|w|

H1 to H2 : 2/|w|

SVM

• (wTxi) +b≧1 if yi=1 (wTxi) +b≧-1 if yi=-1

→ yi ((wTxi)+b)≧1

• min 1/2(wTw)

• cingulo-opercular 前扣帶迴

• fronto-parietal 額葉

• Sensorimotor 感覺神經

• Occipital 枕葉

• Cerebellum 小腦

Result and Future Work

• Prediction of Alzheimer ’ s disease

• Diagnosis & prognosis disordered brain function

Prediction of Individual Brain Maturity

Using fMRI

What is fMRI?

In This Research

• Algorithm: MVPA(multivariate pattern analysis) voxels based on GLM SVMs(support vector machines)

• Data: fcMRI data from 238 rs-fcMRI scans

SVM

• For a set Rd , trying to find a hyper-plane classify data as two groups

SVM

• H1 H2 : support hyper-planes

• Hyper-plane: WTX = - b

H1: wTx +b = +1

H2: wTx +b = −1

• H1 to plane: |1-b| /|w|

H2 to plane: |-1-b| /|w|

H1 to H2 : 2/|w|

SVM

• (wTxi) +b≧1 if yi=1 (wTxi) +b≦-1 if yi=-1

→ yi ((wTxi)+b)≧1

• min：1/2 * (wTw)

• cingulo-opercular 前扣帶迴

• fronto-parietal 額葉

• Sensorimotor 感覺神經

• Occipital 枕葉

• Cerebellum 小腦

Result and Future Work

• Prediction of Alzheimer ’ s disease

• Diagnosis & prognosis disordered brain function

• Machine Learning Basics

• Machine Learning and Bioinformatics

• Example 1:

• Example 2:

• Conclusion

Outline

Emotion Recognition

Physiological Signals

• Communication channels

• Verbal and Non-verbal

• Heterogeneity

• Pattern recognition

• Feature extraction

Physiological Signals

• Electrocardiogram (ECG)：heart rate

(R-R interval, heart rate variability)

• Electromyogram (EMG)：correlation between cognitive emotion and physiological reactions

• Skin conductivity (SC)：robust and well studied, affected by general mood and immediate emotional response.

• Respiration changes (RSP)：breathing depth, rate of respiration

Emotional Model

Classification

Hilbert Huang Transform (HHT)

Classification Results

• Machine Learning Basics

• Machine Learning and Bioinformatics

• Example 1:

• Example 2:

• Conclusion

Outline