Lecture 1:Introduction to Data Mining

for Bioinformatics

Fall 2005Peter van der Putten(putten_at_liacs.nl)

Databases and Data Mining

Course Outline

Date Time Room4-Nov-05 13.45 - 15.30 174   Lecture: Introduction

18-Nov-05 13.45 - 15.30 174   Lecture: Predictive Data Mining15.45 - 17.30 306/308   Practical Assignments

25-Nov-05 13.45 - 15.30 403   Lecture: Descriptive Data Mining & Search2-Dec-05 13.45 - 15.30 174   Lecture: Bioinformatics Data Mining Cases

15.45 - 17.30 306/308   Practical Assignments

• Objective– Understand the basics of data mining– Gain understanding of the potential for applying it in

the bioinformatics domain– Limited hands on experience

• Schedule

• Evaluation– Practical assignment (2nd) plus take home exercise

Agenda Today

• What is data mining?• A short summary of life• Data mining revisited

What is data mining?

Genomic Microarrays – Case Study

• Problem:– Leukemia (different types of Leukemia cells look very

similar)– Given data for a number of samples (patients), can

we• Accurately diagnose the disease? • Predict outcome for given treatment?• Recommend best treatment?

• Solution– Data mining on micro-array data

Example: ALL/AML data

• 38 training patients, 34 test patients, ~ 7,000 patient attributes (micro array gene data)

• 2 Classes: Acute Lymphoblastic Leukemia (ALL) vs Acute Myeloid Leukemia (AML)

• Use train data to build diagnostic model

ALL AML

Results on test data: 33/34 correct, 1 error may be

mislabeled

Sources of (artificial) intelligence

• Reasoning versus learning• Learning from data

– Patient data– Customer records– Stock prices– Piano music– Criminal mug shots– Websites– Robot perceptions– Etc.

Some working definitions….

• ‘Data Mining’ and ‘Knowledge Discovery in Databases’ (KDD) are used interchangeably

• Data mining = – The process of discovery of interesting, meaningful

and actionable patterns hidden in large amounts of data

• Multidisciplinary field originating from artificial intelligence, pattern recognition, statistics, machine learning, bioinformatics, econometrics, ….

A short summary of life

Bio Building BlocksBiotech Data Mining Applications

The Promise….

. . . .

The Promise….

. . . .

The Promise….

. . . .

DNA, Proteins, Cells

DNA, Proteins, Cells

From DNA to Proteins

Discovering the structure of DNAJames Watson & Francis Crick

- Rosalind Franklin

The structure of DNA

DNA Trivia

• DNA stores instructions for the cell to peform its functions

• Double helix, two interwoven strands• Each strand is a sequence of so called

nucleotides• Deoxyribonucleic acid (DNA) comprises 4

different types of nucleotides (bases): adenine (A), thiamine (T), cytosine (C) and guanine (G)– Nucleotide uracil (U) doesn’t occur in DNA

• Each strand is reverse complement of the other• Complementary bases

– A with T– C with G

DNA Trivia

• Each nucleus contain 3 x 10^9 nucleotides• Human body contains 3 x 10^12 cells• Human DNA contains 26k expressed genes,

each gene codes for a protein in principle• DNA of different persons varies 0.2% or less• Human DNA contains 3.2 x 10^9 base pairs

X-174 virus: 5,386

– Salamander: 100 109

– Amoeba dubia: 670 109

Primary Protein Structure

• Proteins are built out of peptides, which are poylmer chains of amino acids

• Twenty amino acids are encoded by the standard genetic code shared by nearly all organisms and are called standard amino acids (100 amino acids exist in nature)

Protein Structurefrom Primary to Quaternary

Proteins: 3D Structure

A representation of the 3D structure of myoglobin, showing coloured alpha helices. This protein was the first to have its structure solved by X-ray crystallography by Max Perutz and Sir John Cowdery Kendrew in 1958, which led to them receiving a Nobel Prize in Chemistry. http://en.wikipedia.org/wiki/Protein

Proteins: 3D Structure

Molecular surface of several proteins showing their comparative sizes. From left to right are: Antibody (IgG), Hemoglobin, Insulin (a hormone), Adenylate Kinase (an enzyme), and Glutamine Synthetase (an enzyme).

Proteins: 3D Structure

G Protein-Coupled Receptors (GPCR) represent more than half the current drug targets

DNA Codes for Proteinsbut Proteins also Control Gene Expression

• Protein regulation occurs at each step of synthesis

Repressor Protein Switching Genes On and Off

Regulatory Protein Coordinating Gene Expression

Importance of Combinatorial Gene Control

• combinations of a few gene regulatory proteins can generate many different cell types during development

Some working definitions….

• Bioinformatics =– Bioinformatics is the research, development, or

application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data [http://www.bisti.nih.gov/].

– Or more pragmatic: Bioinformatics or computational biology is the use of techniques from applied mathematics, informatics, statistics, and computer science to solve biological problems [Wikipedia Nov 2005]

• NCBI Tools for data mining:– Nucleotide sequence analysis– Proteine sequence analysis– Structures– Genome analysis– Gene expression

• Data mining or not?.

Bio informatics and data mining

• From sequence to structure to function• Genomics (DNA), Transcriptomics (RNA), Proteomics

(proteins), Metabolomics (metabolites) Pattern matching and search

• Sequence matching and alignment• Structure prediction

– Predicting structure from sequence– Protein secondary structure prediction

• Function prediction– Predicting function from structure– Protein localization

• Expression analysis– Genes: micro array data analysis etc.– Proteins

• Regulation analysis

Bio informatics and data mining

• Classical medical and clinical studies• Medical decision support tools• Text mining on medical research literature (MEDLINE)• Spectrometry, Imaging• Systems biology and modeling biological systems• Population biology & simulation

• Spin Off: Biological inspired computational learning– Evolutionary algorithms, neural networks, artificial immune

systems

Examples of my related research

• Topology preserving property of self-organizing maps– Neural network for clustering & classification inspired by cortical

maps

• Benchmarking Artificial Immune Systems• Predicting throat cancer survival rate

– Value of fusing data from various sources for this purpose

• Automated recognition of sick yeast cells in images (with prof. Verbeek)

• Recommender systems in bioinformatics– Amazon.com style recommendations

Data mining revisited

Some working definitions….

• ‘Data Mining’ and ‘Knowledge Discovery in Databases’ (KDD) are used interchangeably

• Data mining = – The process of discovery of interesting, meaningful and

actionable patterns hidden in large amounts of data

• Multidisciplinary field originating from artificial intelligence, pattern recognition, statistics, machine learning, bioinformatics, econometrics, ….

Some working definitions….

• Concepts: kinds of things that can be learned– Aim: intelligible and operational concept description– Example: the relation between patient characteristics

and the probability to be diabetic

• Instances: the individual, independent examples of a concept– Example: a patient, candidate drug etc.

• Attributes: measuring aspects of an instance– Example: age, weight, lab tests, microarray data etc

• Pattern or attribute space

Data mining tasks

• Predictive data mining– Classification: classify an instance into a category– Regression: estimate some continuous value

• Descriptive data mining– Matching & search: finding instances similar to x– Clustering: discovering groups of similar instances– Association rule extraction: if a & b then c– Summarization: summarizing group descriptions– Link detection: finding relationships– …

Data Mining Tasks: Search

f.e. age

f.e.

wei

ght

Finding best matching instances

Every instance is a point in pattern space. Attributes are the dimension of an instance, f.e. Age, weight, gender etc.

Pattern spaces may be high dimensional (10 to thousands of dimensions)

Data Mining Tasks: Clustering

f.e. age

f.e.

wei

ght

Clustering is the discovery of groups in a set of instances

Groups are different, instances in a group are similar

In 2 to 3 dimensional pattern space you could just visualise the data and leave the recognition to a human end user

Data Mining Tasks: Clustering

f.e. age

f.e.

wei

ght

Clustering is the discovery of groups in a set of instances

Groups are different, instances in a group are similar

In 2 to 3 dimensional pattern space you could just visualise the data and leave the recognition to a human end user

In >3 dimensions this is not possible

Data Mining Tasks: Classification

age

weig

ht

Goal classifier is to seperate classes on the basis of known attributes

The classifier can be applied to an instance with unknow class

For instance, classes are healthy (circle) and sick (square); attributes are age and weight

Examples of Classification Techniques

• Majority class vote• Machine learning & AI• Decision trees• Nearest neighbor• Neural networks• Genetic algorithms / evolutionary computing• Artificial Immune Systems• Good old statistics• …..

Example Classification Algorithm 1Decision Trees

20000 patients

age > 67

18800 patientsgender = male?

1200 patientsWeight > 85kg

800 customersDiabetic (%10) etc.400 patients

Diabetic (%50)

no

noyes

yes

no

Decision Trees in Pattern Space

age

weig

ht

Goal classifier is to seperate classes (circle, square) on the basis of attribute age and income

Each line corresponds to a split in the tree

Decision areas are ‘tiles’ in pattern space

Example classification algorithm 3:Neural Networks

• Inspired by neuronal computation in the brain (McCullough & Pitts 1943 (!))

• Input (attributes) is coded as activation on the input layer neurons, activation feeds forward through network of weighted links between neurons and causes activations on the output neurons (for instance diabetic yes/no)

• Algorithm learns to find optimal weight using the training instances and a general learning rule.

invoer:bvb. klantkenmerken

uitvoer:bvb. respons

• Example simple network (2 layers)

• Probability of being diabetic = f (age * weightage + body mass index * weightbody mass index)

Neural Networks

Weightbody mass index

Probability of being diabetic

age body_mass_index

weightage

Neural Networks in Pattern Space

Classification

f.e. age

f.e.

wei

ght

Simpel network: only a line available (why?) to seperate classes

Multilayer network:

Any classification boundary possible

Descriptive data mining:association rules

• Discovery of interesting patterns• Rule format: if A (and B and C etc) then Z• Example:

– If customer buys potatoes (A) and sauerkraut (B) then customer buys sausage (Z)

• Important measures– Support condition: how often do potatoes and sauerkraut occur

together (A,B)– Confidence rule: how often do sausages then occur / support

conditions (is A,B C always true?)

• Could be used for instance for mining gene expression data

Quiz Question

What have we learned today

• An introduction into applying data mining for bioinformatics

• A short history of life• Basic data mining concepts