Using Relational Structure for Learning and Modeling in Biomedical and Social

DomainsMark Goadrich

Computer Science and Mathematics

Centenary College of Louisiana

Natural Science ColloquiumNovember 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– example(e3)– triangle(t1)– triangle(t2)– circle(c1)Positive Example 3

• 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

Cellular Process

Social Science

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]