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
ILP takes snapshots of the world
ABM simulates a dynamic world

3. Bongard Problems 6 positive examples of a concept on left
Bongard problems. From Mikhail Bongard, Soviet Computer Scientist
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) …
We make lots of facts for each example. This is what is true about the world.
Positive Example 3
Repeat this process for each example in dataset

5. 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)
Animate sample rule at level 1 and 2
positive(E) :- has_shape(E, A), has_shape(E, B),
triangle(A), circle(B), inside(A, B).

6. 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, )
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).
Bond(compound, atom1, atom2, bondtype)
Atm(compound, atom, element, atomtype, charge)
(A) 3,4,4'-tri-nitro-biphenyl
(B) 2-nitro-1,3,7,8-tetrachlorodibenzo-1,4-dioxin
(C) 1,6,-dinitro-9,10,11,12-tetrahydrobenzo[e]pyrene
(D) nitrofurantoin

8. Breast Cancer Detection
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
Friend’s 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. Social Science
Cellular Process

16. 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]