Machine Learning in Bioinformatics

Machine Learning in BioinformaticsDmytro Fishman ([email protected])

BIIT

mailto:[email protected]

Dmytro Fishman

E-mail: [email protected] 21 - 23, Tartu

mailto:[email protected]?subject=

Machine Learning in Bioinformatics


https://siteman.wustl.edu/glossary/cdr0000046470/

Central dogma

Microlevel

http://learn.genetics.utah.edu/content/cells/scale/

What these books contain?

This is a single human genome

~4GB

Big Data

ML in Bioinformatics (part III)

2016

Labs that produce data

Research groups that can analyse data

What is Bioinformatics?

“Bioinformatics is an interdisciplinary field that develops methods and software tools for extracting knowledge from biological data.”

https://en.wikipedia.org/wiki/Bioinformatics

What is Bioinformatics?“Bioinformatics is • an interdisciplinary field • develops methods and software tools • for extracting knowledge from biological

data.”

What is Bioinformatics?

Presenting this knowledge to non-experts

“Bioinformatics is • an interdisciplinary field • develops methods and software tools • for extracting knowledge from biological

data.”

Bioinformatics

Computer Science Biology

Statistics

Bioinformatics


Statistics

Bioinformatics


StatisticsP-hacking


Feat

ure

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Feature #1

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Healthy

Sick

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Healthy

Sick

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Anything in common?

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Females

Males

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Sick

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Females

Males

Supervised Learning

Unsupervised Learning

Linear Classifiers Decision TreesFeature #1

> 20 <= 20Feature #2

> 1000 <= 1000

1

Neural NetworksInput Hidden

Layer

0

k-Nearest Neighbors

Others

Self-Organizing Map

https://topepo.github.io/caret

Many more

Feat

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Feature #1

OutputLayer

1

0 1

1

https://topepo.github.io/caret


Anna, 32, Female, DT2,…Metadata

Anna, 32, Female, DT2,…

Medical Signal

Metadata


Medical Signal

Metadata

Medical Imaging


Medical Signal

Metadata

Medical Imaging


Medical Signal

Metadata

Medical Imaging

Clinical Data


ACCTTGCTTACC…

Medical Signal

Metadata

Medical Imaging

Clinical Data

Genetical Information


ACCTTGCTTACC…

Medical Signal

Metadata

Medical Imaging

Clinical Data


Metabolite #1

Metabolite #2

Metabolite #3

First individual

Metabolite #1

Metabolite #2

Metabolite #3

First individual

M1

M2

M3

Metabolite #1

Metabolite #2

Metabolite #3

First individual

M1

M2

M3

Metabolite #1

Metabolite #2

Metabolite #3

Second individual

M1

M2

M3

Metabolite #1

Metabolite #2

Metabolite #3

Individual #N

M1

M2

M3

Metabolite #1

Metabolite #2

Metabolite #3

M1

M2

M3

Patients Healthy

Individual #N

Metabolite #1

Metabolite #2

Metabolite #3

M1

M2

M3

Patients Healthy

Individual #N

Metabolite #1

Metabolite #2

Metabolite #3

M1

M2

M3

Patients Healthy

P(disease) = 0.6*M1 + 0*M2 - 0.7*M3

Individual #N

Metabolite #1

Metabolite #2

Metabolite #3

M1

M2

M3

Patients Healthy

P(disease) = 0.6*M1 + 0*M2 - 0.7*M3

Individual #N

Dima


ACCTTGCTTACC…

Medical Signal

Metadata

Medical Imaging

Clinical Data


…AACCTTTACATTAAACTGGGACCCGG… Individual #1…AACCTTTACATTATACTGGGAGCCGG… Individual #2

…CTAAACCACATTATACTGGGACCCGG… Individual #N…



Human genomes are 99.5% similar



Mutations



Deletions



Insertions



Compute pairwise distance



Compute pairwise distance

#1 #2 … #N

#1 0 29 … 34

#2 29 0 … 56

… … … … …

#N 34 56 … 0

Distance Matrix



#1 #2 … #N

#1 0 29 … 34

#2 29 0 … 56

… … … … …

#N 34 56 … 0

Distance Matrix



#1 #2 … #N

#1 0 29 … 34

#2 29 0 … 56

… … … … …

#N 34 56 … 0

Distance Matrix

PCA



#1 #2 … #N

#1 0 29 … 34

#2 29 0 … 56

… … … … …

#N 34 56 … 0

Distance Matrix

PCA

Principal component analysis of the combined autosomal genotypic data of individuals from Russia and seven European countries (Finnland, Estonia, Latvia, Poland, Czech Republic, Germany [5] and Italia [22]). Khrunin et al.


ACCTTGCTTACC…

Medical Signal

Metadata

Medical Imaging

Clinical Data


Neuroimaging

Neuroimaging EEG Signal


FourierTransform

Data


Data


Data Machine Learning



P( ) = .92



P( ) = .92

Flying



P( ) = .92

Soft Introduction to Brain-Computer Interfaces by Ilya Kuzovkin (www.ikuz.eu)

Flying

http://ikuz.eu/

Cardiogram


P( ) = .92

Cardiograph


ACCTTGCTTACC…

Medical Signal

Metadata

Medical Imaging

Clinical Data


AD NDC

Alzheimer’s Disease

Disease Stage Prediction

Diabetic Retinopathy

AD NDC



Diabetic Retinopathy Alzheimer’s Disease

Heart Volume Prediction

Heart Failure

AD NDC



Mitotic Cells Count

Breast Cancer



Heart Failure

AD NDC

AD NDC





Heart Failure

Mitotic Cells Count

Breast Cancer

Stained Cells Count

Cancer

AD NDC





Heart Failure

Mitotic Cells Count

Breast Cancer

Stained Cells Count

Cancer

Protein Classification

AD NDC





Heart Failure

Mitotic Cells Count

Breast Cancer

Stained Cells Count

Cancer

Protein Classification

Accurate classification of protein subcellular localization from high throughput microscopy images using deep learning. Pärnamaa et al.


ACCTTGCTTACC…

Medical Signal

Metadata

Medical Imaging

Clinical Data


National Health System [email protected]

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Anna, 32, Femalesee full profile

see all recent scans

see all genetic risks

Susceptible to DT2

09 10 11 12 01 02 03 04

normal

Suga

r

2010 2011 2011

see other tests results

Enter symptoms here…

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

Birth

A37: Whooping cough

J21.8: Acute bronchiolitis

S82: Fracture of lower leg

V22.0: Normal Pregnancy

Dr. Peterson

related medical reports

Logout


ACCTTGCTTACC…

Data Integration for better decision making is the key challenge

Quiz

Genes are us…

and them.SOURCE:,JAVIER,HERRERO,,EUROPEAN,MOLECULAR,BIOLOGY,LABORATORY—EUROPEAN,BIOINFORMAT,ICS,INSTITUTE

Genes are us…


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50%

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Machine Learning in Bioinformatics