Using Relational Structure for Learning and Modeling in Biomedical and Social Domains

1
Using Relational Structure for Learning and
Modeling in Biomedical and Social Domains

• Mark Goadrich
• Computer Science and Mathematics
• Centenary College of Louisiana
• Natural Science Colloquium
• November 6th, 2007

2
Overview

• First-Order Logic and Machine Learning
• The world is full of Objects
• Model these Objects to understand the world
• Inductive Logic Programming
• Objects and Relations/Properties
• Agent-Based Modeling
• Objects and Interactions/Behaviors

3
Bongard Problems

• 6 positive examples of a concept on left
• 6 negative examples on right
• How to learn this concept using a computer?

4
First-Order Logic using PROLOG

• Objects
• e3, t1, t2, c1
• Types
• example(e3)
• triangle(t1)
• triangle(t2)
• circle(c1)

• Relations
• has_shape(e3, t1)
• has_shape(e3, t2)
• has_shape(e3, c1)
• inside(t2, c1)
• left(t2, t1)
• size(c1, 2.5)
• above(t2, t1)

Positive Example 3
Repeat this process for each example in dataset
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Inductive Logic Programming (ILP)

• Search the space of possible rules positive(E)
  -
• Judge rule quality by positive - negative
  coverage

positive(E)- has_shape(E, A)
positive(E)
positive(E)- has_shape(E, A), triangle(A)
positive(E) - has_shape(E, A), has_shape(E, B),
triangle(A), circle(B),
inside(A, B).
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Research Issues in ILP

• Enormous space to search for rules
• Enormous number of examples
• Incorporation of continuous features
• Learning of probabilistic rules
• Evaluation of rule quality
• Survey of ILP domains and future interests

7
Mutagenesis

• Designing effective and selective drugs
• Represent chemicals as atoms and bonds between
  them
• atm(127, 127_1, c, 22, 0.191 )
• bond(127, 127_1, 127_6, 7 )
• Learned mutagenic rule
• mutagenic(A) - atm(A, B, c, 27, C), bond(A, D,
  E, 1), bond(A, B, E, 7).

8
Breast CancerDetection

• Large dataset of abnormalities found in
  mammograms
• Not enough radiologists
• Relational features
• More than one abnormality per mammogram
• More than one mammogram per person over time
• malignant(A) - not asymmetric(A),
  in_same_mammorgram(A, A2), spiculated_margin(A2),
  not distorted(A2)

9
Robot Scientist

• Represent Metabolic Pathways as a Regulatory
  Network Graph
• Knock out genes, and then systematically deduce
  the unknown function
• Try to learn the network from time-series
  microarray data

10
Social Networks

• People are connected by friendships into networks
• Each person has likes/dislikes, possibly
  influenced by their network
• Can we learn your interests based on who you know
  and what they like? Targeted advertisements?

11
Netflix Prize

• What movies should Netflix recommend you watch
  next?
• Large relational dataset
• Movies
• Titles
• Ratings
• Friends
• Friends ratings
• Genre
• 1 million if you achieve 10 improvement over
  their algorithm Cinematch

12
Zendo

• Board game about inductive logic
• Master creates a rule which some 3-D pyramid
  structures fit and others do not
• Players build structures and try to guess the
  Master rule
• Easier to design computer Master to decide if a
  structure fits the rule
• Harder to design computer Player that must
  efficiently guess the rule

13
Crab Claws

• What physical characteristics distinguish between
  two species?
• Within the same species, what changes due to
  growth, diet and their relation to predation?
• Find the shock graph of each image
• Use ILP to learn differences based on these graphs

14
Agent-Based Modeling

• Objects have interactions with each other
• Flocks of Birds, Schools of Fish
• Separation
• Alignment
• Cohesion
• Objects interact with their environment
• Ant Foraging, Pheromones, Traffic Laws
• Agent-Based Modeling (ABM)
• Create discrete-time computational simulation
• Align models with known behavior
• Vary parameters to test new hypotheses

15
Cellular Process
Social Science
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Conclusions

• First-Order Logic combines with ILP and ABM to
  create a powerful representation of the world
• Research Opportunities
• Social Networks
• Zendo Player
• Claws and Shock Graphs
• Cellular Simulation
• Social Simulation
• Insert your favorite dataset here