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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
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
Bongard Problems
• 6 positive examples of a concept on left
• 6 negative examples on right
• How to learn this concept using a computer?
First-Order Logic using
PROLOG
• Objects
– e3, t1, t2, c1
• Types
Positive Example 3
–
–
–
–
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)
…
Repeat this process for each example in dataset
Inductive Logic Programming
(ILP)
• Search the space of possible rules “positive(E) :…”
• Judge rule quality by positive - negative coverage
positive(E)
positive(E):- has_shape(E, A)
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).
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
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).
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)
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
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?
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
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
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
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
Social Science
Cellular
Process
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]