Automated theory formation first steps in bioinformatics
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Automated Theory Formation: First Steps in Bioinformatics. Simon Colton Computational Bioinformatics Laboratory. Machine Learning (ML) Questions. Given some background information Concepts, hypotheses (axioms) Given some positive examples And some negative examples Find me an explanation

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Automated theory formation first steps in bioinformatics

Automated Theory Formation:First Steps in Bioinformatics

Simon Colton

Computational Bioinformatics Laboratory


Machine learning ml questions

Machine Learning (ML)Questions

  • Given some background information

    • Concepts, hypotheses (axioms)

  • Given some positive examples

    • And some negative examples

  • Find me an explanation

    • Why the positives are positive

    • And the negatives are negative


Example predictive toxicology

Example: Predictive Toxicology

  • Given some theory from chemistry

    • Structure of molecules, well known substructures

  • Given some examples of toxic drugs

    • And some examples of non-toxic drugs

  • Question: Why are the toxic drugs toxic?


Automated theory formation atf questions

Automated Theory Formation (ATF) Questions

  • Given some background information

    • Concepts, hypotheses (axioms)

  • And some objects of interest

    • Numbers, Molecules, etc.

  • Find something interesting

  • Interesting things could be:

    • Concepts, examples, hypotheses, explanations


Atf overview

ATF Overview

  • Scientific theories contain (at least):

    • Concepts: salt, acid, base

    • Hypotheses: acid + base => salt + water

    • Explanations: transfer of electrons, dissolving

  • So, ATF should do (at least):

    • Concept formation, Conjecture making

    • Hypothesis proving and disproving.

  • Also needs to:

    • Measure interestingness, present results, etc.


Hr theory formation system

HR Theory Formation System

  • Developed in maths

    • Designed to be general purpose system

  • Concept-based theory formation

    • Tries to make concept

    • Makes conjecture when it can’t make a concept

    • Tries to explain conjectures

  • Conjecture-based theory formation

    • Fix faulty conjectures with concept formation

    • PhD work of Alison Pease, based on Lakatos


Concept formation in hr

Concept Formation in HR

  • 10 General Production Rules

    • Take in old concepts, produce new concepts

Size

[a,b] : b|a

Split

[a,n]:n = |{b:b|a}|

[a] : 2|a

Negate

Split

Compose

[a]:2=|{b:b|a}|

[a] : not 2|a

[a]:2=|{b:b|a}| & not 2|a

(Odd Prime Numbers)


Conjecture making

Conjecture Making

  • Empirical checks are performed

    • After each attempt to invent a new concept

  • If the concept has no examples

    • Makes non-existence conjecture

  • If concept has same examples as previous

    • Makes an equivalence conjecture

  • If another concept subsumes the concept

    • Makes an implication conjecture


Conjecture extraction

Conjecture Extraction

  • Suppose HR makes equivalence conjecture:

    • P(a) & Q(a)  R(a) & S(a)

  • Extracts:

    • P(a) & Q(a) => R(a), P(a) & Q(a) => S(a)

    • R(a) & S(a) => P(a), R(a) & S(a) => Q(a)

  • Tries to Extract: P(a) => R(a), Q(a) => R(a), etc.

    • Prime implicates (require proving, though)

  • Important: gets Horn Clauses

    • Can be expressed in Prolog…..


Explanation generation

Explanation Generation

  • In mathematical domains

    • HR relies on automated theorem provers

    • And Model generators

      • To find counterexamples

    • E.g., group theory: a*a=a  a=id (prove easily)

  • In biological/chemistry domains

    • Possibly: visualisation tools, reaction pathways


Greatest hits

Greatest Hits

  • Please ask me over coffee about:

    • Pre-processing constraint problems

    • Learning properties of quadratic residues

    • Inventing integer sequences

    • Puzzle generation

    • Adding to the TPTP library

    • Setting mathematical tutorial questions


Long term aim in bioinformatics

Long term aim in Bioinformatics

  • Develop an ATF system similar to HOMER

    • But working in biological domains

  • Biologist provides little background info

    • In a format they are happy with

  • Program provides results

    • Intelligent, interesting, not too much,

    • And very little rubbish

  • Automated assistant for biology


Short term aim in bioinformatics

Short term aim in Bioinformatics

  • HR can work with biological data

    • Takes input similar to Muggleton’s Progol

  • Use HR to solve ML problems

    • See how bad an idea that is

  • Use theory formation to improve ML

    • Integrate HR and Progol somehow


Na ve approach to ml tasks

Naïve Approach to ML Tasks

  • Give HR the same input as Progol

    • Get it to form a theory

  • Look at the theory

    • Extract concepts which do well on the task

    • i.e., they look similar to target concept

  • Not a goal-based approach

    • Bad idea (slow)


Less na ve approach

Less Naïve Approach

  • Improve search using “forward look-ahead”

    • ICML Paper

  • This has evolved to “reactive search”

    • Uses HR’s own Java interpreter

    • HR reacts to certain events in theory formation

      • Scripts supplied by the user

  • HR also makes “near-conjectures”

  • Faster approach, but still fairly slow


Example mutagenesis42 data

Example – Mutagenesis42 Data

  • Mutagenesis similar to carcinogenisis

  • 42 drugs supplied with atom-bond details

    • Atom type, number & charge, bond type (1-8)

  • 13 are mutagenic (active), 29 are not active

  • Progol learned this concept (88% accurate)

    • active(A) :- bond(A,B,C,2), bond(A,D,B,1),atm(A,D,c,21,E)

1

2

c,21

?

?


Hr s results

HR’s Results

  • Using reactive search, four PRs, 30K steps

  • HR learned this concept:

    • active(A) :- bond(A,B,C,1), atm(B,F,21), bond(A,C,D,E)

    • Also 88% accurate

    • But, Progol’s answer “better”

    • Because higher information content (fewer ?s)

    • Biologists sometimes want more information

      • Is this really a simpler answer?

1

?

?,21

?

?


Automated theory formation first steps in bioinformatics

But…..

  • HR also made these equivalence conjectures

    • And extracted them (+100 more) for us

      atm(B,X,21)  atm(B,c,21)

      atm(B,X,38)  atm(B,n,38)

      bond(A,B,C,X1) & atm(C,X2,38)  bond(A,B,C,1) & atm(C,X3,38)

      bond(A,X1,B,X2) & atm(B,X3,38)  bond(A,B,X4,2), atm(B,X5,38)

  • We used these to re-write HR’s answer

    • By hand, but hope to automate


Giving us this answer

Giving us this answer:

  • Remember that Progol’s Answer was:

1

2

c,21

?

n,38

1

2

c,21

?

?

  • So, we filled in one of the blanks!


Are we making a meal of this

Are we making a meal of this?

  • Yes, possibly for the mutagenesis data

    • I was worried about the difficulty of this problem

  • In the last week I’ve written a

    • 200-line Prolog program which runs quite fast

    • And can be distributed over multiple processors

    • And can be easily understood by biologists

  • And gets these results….


Template search results

Template search – Results

  • Nice result one (88% accurate, lots of info)

1

2

c,21

n,38

o,40

2

o,40

  • Nice result two (95% accurate)

1

1

2

7

1

7

c,21

c,?

c,195

n,38

o,40

c,22

?

c,22

h,3

-0.132

0.145


Template search assumptions

Template Search - Assumptions

  • Connected substructures

    • Are interesting answers

    • Progol’s answers are all substructures

  • More specific substructures are not so bad

    • Biologists may even want lots of information

    • Don’t forget that they want to do science

  • Each learned concept will be true of

    • At least one active (positive) molecule


Template search overview

Template Search - Overview

?

?

?,?

?,?

?,?

  • User chooses template for substructures

  • User specifies how many ?s are allowed

    • E.g., 3 out of 8 in the above template

  • Algorithm starts with the first positive

    • Extracts all substructures in the template

  • Then takes the next positive,

    • for each substructure in the set

      • Add the LGG so that it fits both positives

      • Don’t go under the IC limit


Template search final part

Template Search – Final Part

  • For all the substructures

    • Take a disjunction

      • Which achieves the best accuracy

  • Distribution of this algorithm possible

    • We’re getting a big Linux farm

    • PPP – Processor Per Positive

      • finds substructures true of one positive

      • combine answers at the end


Conclusions future work

Conclusions & Future Work

  • Automated Theory Formation

    • May be useful to bioinformatics

    • Use HR’s theory to improve Progol’s results

      • Possibly by pre-processing Progol’s input

      • Or by post-processing the learned concept

  • Template search

    • Maybe a good idea? Possibly not new….

    • Not bad results for the Mutagenesis42 dataset


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