Presentation

Leiden University. The university to discover.

From Data Mining to Knowledge Fitting

Joost N. Kok, Leiden Institute of Advanced Computer Science

Leiden University. The university to discover.Sunday, April 9, 20232

Information Ladder

- Data- Information- Knowledge - Understanding - Insight - Wisdom

Data Mining definitions

- Secondary analysis of data- Induction of understandable useful models and patterns from data

- Algorithms for large quantities of data

-Data Mining is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data

useful

novel, surprising

comprehensible

valid (accurate)

Data Mining

- Data Mining = Data Search using a Knowledge Bias

Data Mining- Data Mining is somewhat comparable to

Statistics (and is often based on it), but takes it further in the sense that whereas

- statistics aims more at validating given hypotheses

- in data mining often millions of potential patterns are generated and tested, in the hope of finding some that are potentially useful

Typical Data Mining Results

-Forecasting what may happen in the future-Classifying people or things into groups by recognizing patterns-Clustering people or things into groups based on their attributes-Associating what events are likely to occur together-Sequencing what events are likely to lead to later events

Different types of problems- “Data mining” problems / tasks often fall in

one of the following categories:- Classification- Regression- Clustering- Discovering associations- Probabilistic modelling

From “Querying” to “Mining”

Are there any occurrences of GAAT in this string?

How many occurrences of AAT are there in this string?

Which substrings of length 4 occur at least 2 times?

Which substrings (of any length) occur significantly moreoften in the

white string than in the black string?

Standard databasetechnology solves suchquestions

Data mining technologycan sometimes solve suchquestions (computationsmay be (too) heavy)

Science fictionWhy is the virus to the left resistant to my drug, and the one to the right

Scientific Data Lifecycle

Databases

Ontologies

IntegrationDisambiguation

DataKnowledgeDiscovery

tistic

Knowledge Fitting

Building Blocks

Link Integration

Source Source Source Source

Federated Database

Distributor

Data Warehousing

Warehouse

Scripting Languages

- A scripting language is a programming language that controls software applications.

- Examples: Python, Perl- Standards for uniform access to

databases

Ontologies

- Ontology is about the description of things and their relationships.

- Ontologies are taxonomies that define concepts and relationships among them.

- The subclass / is-a relationship is most predominant in ontologies

OWL =Web Ontology Language

Building Blocks

Service Orientation

- SOA = Service-Oriented Architecture- SOA: Distributed Software Architecture

that allows for building applications through individual component composition

Visualisation

- Intelligent Data Analysis

- Intelligent = Methods- Intelligent = Human Interaction

- First step:

- visualisation of the data

DNA Visualisation

- Long patterns over small alphabets are hard to find …

- ababababababababababababababababababababababa . . .- (ab)

- abbbababaaababbabbbababaaababbabbbababaaababb . . .- (abbbababaaababb)

- abaaaababbbbabaaaababbbbabaaaababbbbabaaaabab . . .- (abaaaa · babbbb)

DNA Visualisation

- Associate each nucleotide with a dimension

- Four nucleotides => four dimensions- Build a structure in four dimensions- Project to three dimensions

DNA Visualisation

- We expect to see the following things in the projection:

- A non-predictable walk for information rich parts of the DNA

- A true random walk for random parts- Lines (or approximate lines) for

repeating parts of the DNA- Large identical substrings in the DNA

can easily detected

DNA Visualisation- Select four three-dimensional vectors.

- The vectors should be of comparable length

- The four vectors should add up to 0

- Every subset of three vectors should be independent.

DNA Visualisation

The first 160,000 nucleotides of the human Y-chromosome

40,000–100,000 of the chromosome 1 (human)

DNA Visualisation

- Simple, large and extremely large (approximate) repeats can easily be detected

- Demo- http://www.liacs.nl/home/jlaros/projects/dnavis/

Data Mining

Subgroup Discovery

- How to find comprehensible subgroups in large amounts of data?

- As an example: subtypes in complex diseases

- Different types of input

Class A Class B

Classification versus Subgroup Discovery

Class A Class B

Classification vs Subgroup Discovery

- Classification - predictive induction - constructing sets of classification rules- aimed at learning a model for classification

or prediction- rules are dependent

- Subgroup Discovery- descriptive induction - constructing individual subgroup-describing

rules - aimed at finding interesting patterns in

target class examples

Towards Knowledge Fitting- Trends:

- A lot of valuable data is not any longer being shared due to various reasons: privacy issues, data is difficult and expensive to collect, etc.

- The amount of publicly available knowledge increases daily.

- Patterns and models need to be complemented with knowledge that convinces the user.

Knowledge Fitting =

Knowledge Mining using a Data Bias

Data Mining =

Data Search using a Knowledge Bias

SUBGROUP MINING SCENARIO

- Prepare the data

- Model the subgroups

- Characterize and compare the subgroups

- Evaluate the subgroups

Package available in R

Subgroup Mining Scenario

Group Modeling

- Model based cluster analysis.- The data is modeled by a mixture of Gaussians.- Many models, many BIC scores.

Group Characteristics

Subgroup Evaluation- We report in tables statistical results and

generalization estimates

GENOMICS SCENARIO

Leiden University. The university to discover.49

Gene Expression Data

- Genomics: the study of genes and their function

- MicroArray Data

- a very large number of attributes (genes) relative to the number of examples (observations)

- typical values: 7000-16000 attributes, 50-150 examples

Gene Expression Data

Patient # Tumor Type Gene #1 Gene #2 Gene #3 … Gene #10,000 1 A 5.00 1.33 3.45 … 4.22 2 A 0.98 0.87 1.04 … ? 3 B 0.33 1.40 0.42 … 0.24 … … … … … … …

100 B 0.89 0.90 1.00 … 0.66

fewcases

many features

…#1 #2 #100

Ranking of differentially expressed genes

The genes are ordered in a ranked list, according to their differential expression between the classes.

The challenge is to extract meaning from this list, to describe subgroups.

The conjunction of terms of ontologies are used as a vocabulary for the description of sets of genes.

Subgroup DiscoveryDiscovery of gene subgroups which

- are “higher” in the ranked list- can be compactly summarized

using• knowledge (GO, ENTREZ, KEGG)• Interactions between genes

Enrichment Score

Descriptions

- FANTOM = Frequent pAtterN Tree-based Ontology Miner

- FANTOM is a knowledge fitting tool that uncovers “interesting” descriptions of gene sets

- Interesting: high Gene Set Enrichment Score

- Search for patterns is exhaustive

Inputs

- FANTOM takes as inputs:- A ranked list of genes (default ID is from

ENTREZ), together with a score.- Ontologies (default are GO and KEGG)- Mappings (to map ENTREZ or another

ID to the ontologies)- Interaction data (if available)- Cutoffs

- minimum GSES - minimum amount of gene participants in a rule

Typical Statistics

- Experiment comparing two different mouse hearts:

- Generated rule options: 200k-2m- Actual rules: 10-40k- Rules after pruning: 5-500- Runtime: 5 minutes - 4 hours

Knowledge Fitting =

Knowledge Mining using a Data Bias

Data Mining =

Data Search using a Knowledge Bias

Intelligent Bridges

Movies

Cyttron

- The Cyttron consortium aims at developing a "super microscope", imaging the living cell with atomic resolution.

- Images gathered with X–ray diffraction, electron microscopy, and other sources will be combined through advanced software solutions.

- www.cyttron.org

- The Computer Science Institute of Leiden University

- Leiden Institute of Advanced Computer Science

- www.liacs.nl

Research Clusters

- Algorithms- Foundations of Software Technology- Computer Systems- Imagery and Media- Technology and Innovation Management

Acknowledgements

- Jeroen Laros (LIACS)- Jeroen de Bruin (LIACS)- Fabrice Colas (LIACS)- Nada Lavrac (JSI)- Igor Trajkovski (JSI)- Jan Bot (TU Delft)- Ingrid Meulebelt (LUMC)- Eline Slagboom (LUMC)- Peter-Bram ‘t Hoen (LUMC)- Tineke van Veen (LUMC)- Stephanie van Roden (LUMC)

Algorithms Cluster @ LIACS

Presentation

Documents

Transcript of Presentation

Investor Presentation Presentation Title

Production Presentation VUMS Presentation

España presentation spain presentation

Merged Presentation KICC Presentation.

Investor Presentation April 2015s1.q4cdn.com/.../2015/150410-Investor-Presentation-Apr.pdfInvestor Presentation April 2015 This management presentation (the “presentation”) was

Presentation Zen Presentation

A Presentation on Presentation

FDA Presentation FDA Presentation

Investor Presentation...Investor Presentation November 2016 This presentation, including the accompanying oral presentation (collectively, this “presentation”),does not constitute

Drought presentation presentation

Presentation Educ 285 Presentation

BCE Presentation- presentation updated

First Presentation+2nd Presentation

Presentation 2501 AB2501 Presentation

4Q ’17 Earnings Presentation · Earnings Presentation. This presentation, including the accompanying oral presentation (collectively, this “presentation”),does not constitute

Presentation skills - Zen presentation

Presentation on Presentation 1

3Q 2017 Investor Presentation · Investor Presentation. This presentation, including the accompanying oral presentation (collectively, this “presentation”),does not constitute

Data Presentation Techniques. Data Presentation Techniques Data Presentation Techniques.

Investor Presentation...This presentation