1. 9th Annual "Humies" Awards 2012 — Philadelphia, Pennsylvania
Genetic Programming Based Feature Generation for
Automated DNA Sequence Analysis
Uday Kamath, Amarda Shehu ,Kenneth A De Jong
Department of Computer Science
George Mason University
Fairfax,VA, 22030
{ukamath, amarda,

2. Bioinformatics and Molecular Biology
LarrañagaP et al. Brief Bioinform2006;7:86-112

3. Promoter Site Identification
Background
Promoters signal the
beginning of a coding
region
They are important
signals for initiation of
DNA->RNA transcription.
Challenges
Complex
Gene-specific
Many decoys
Copyright 2012 the British Journal of Anaesthesia

4. DNA Splice Site Identification
Background
Splice sites mark
boundaries between
exons and introns in a gene
Challenges
No known sequence pattern
Diverse sequence length
Diverse exon lengths
Diverse number and lengths of introns
0.1 to 1% true splice sites, rest decoys
Asa Ben-Hur, Cheng Soon Ong, Sören Sonnenburg, Bernhard Schölkopf, and Gunnar Rätsch
TUTORIAL: SUPPORT VECTOR MACHINES AND KERNELS FOR COMPUTATIONAL BIOLOGY
[2008]

5. Evolutionary (GP) Approach

6. Finding Functional Features
GP Functional Features
Terminals
A,C,T,G
Integers for position/region
Basic Non Terminals
Motif (combination of ACTG)
Position based Motifs
Correlation based Motifs
Region based Motifs
Composition based Motifs
Complex Non Terminals
Conjuntions
Disjunctions
Negations
Features Evolved combining accuracy/precision

7. Why Human Competitive ?
B) The result >= than a result that was accepted as a new scientific result
E) The result >= than the most recent human-created solution to a long-standing problem
F) The result >= than a result that was considered an achievement when was first discovered
G) The result solves a problem of indisputable difficulty in its field

8. Why Human Competitive ?
B) The result >= than a result that was accepted as a new scientific result
F) The result >= than a result that was considered an achievement when was first discovered
Splice Site Prediction
Research compares state of the art Enumeration, Iterative, Probabilistic
methods, Kernel methods etc.
Best Precision with statistical significant improvements on most datasets
Promoter Prediction
Research compares results with 7 state of the art algorithms ranging from
Enumeration, Iterative, Neural Networks, Kernel based etc.
Best Precision and with statistical significant improvements on different datasets

9. Why Human Competitive ?
F) The result >= than a result that was considered an achievement when was first discovered
On Promoter Identification Problem
What was considered achievement
Where we stand
Uday Kamath, Kenneth A De Jong, and Amarda Shehu. "An Evolutionary-based Approach for Feature Generation: Eukaryotic Promoter Recognition." IEEE Congress on Evolutionary Computation (IEEE CEC), New Orleans, LA, pg , 2011

10. Why Human Competitive ?
F) The result >= than a result that was considered an achievement when was first discovered
On Splice site Identification Problem
What was considered achievement
Where we stand
Uday Kamath, Jack Compton, Rezarta Islamaj Dogan, Kenneth A. De Jong, and Amarda Shehu. An Evolutionary Algorithm Approach for Feature Generation from Sequence Data and its Application to DNA Splice-Site Prediction. Trans Comp Biol and Bioinf 2012

11. Why Human Competitive ?
E) The result >= than the most recent human-created solution to a long-standing problem
Long Standing Problem(s)
Genome Sequence prediction and annotation of Splice sites and Promoters
Computational Results >=
Around 7 datasets and 10 algorithms compared
Advancing Understanding in Genomics
Our top features do contain signals painstakingly determined by biologists through decades
of wet-lab research.
More importantly, new features are found that may help biologists further advance their
understanding of DNA architecture
All our features are available online for experts to analyze and spur further wet-lab research

12. Why Human Competitive?
G) The result solves a problem of indisputable difficulty in its field
Estimated 10-25K human protein-coding genes (only 1.5% of entire genome)
Wet-lab models of discovery costly and prone to errors
Cannot keep pace with growing genomic sequences
Computational models good complements, but
Black Box Models – No or Little help to Biologists
White Box Models- Lower precision/accuracy and reliant on manual steps
Decades of research into DNA function and architecture
“Gene finding” on pubmed returns > 80,000 research articles
Progress crucial to speed up our understanding of disease and development of
targeted treatments

13. Why is this the Best Entry
Addresses central problems to molecular biology and health research
Finding functional signals in genome sequences is complex and NP-Hard
Improvements over state of the art are statistically significant
Extensive statistical analysis validates usefulness of GP features
F-score and Information gain techniques
Advances understanding to motivate further research
Features found by GP reproduce results of decades of research by biologists
Novel interesting features also reported
Features, data sets, and software publicly available for community
Far reaching implications, spurring research beyond genomics
Example: finding what features determine anti-microbial activity for the purpose of generating novel peptides to combat drug resistance.