AI and Bioinformatics

From Database Mining to the Robot Scientist

History of Bioinformatics

Definition of Bioinformatics is debated In 1973, Herbert Boyer and Stanely Cohen

invented DNA cloning. By 1977, a method for sequencing DNA was

discovered In 1981 The Smith-Waterman algorithm for

sequence alignment is published

History of Bioinformatics

By 1981, 579 human genes had been mapped

In 1985 the FASTP algorithm is published.

In 1988, the Human Genome organization (HUGO) was founded.

History of Bioinformatics Bioinformatics was fuelled by the need to create

huge databases. AI and heuristic methods can provide key solutions

for the new challenges posed by the progressive transformation of biology into a data-massive science. Data Mining

1990, the BLAST program is implemented. BLAST: Basic Local Alignment Search Tool. A program for searching biosequence databases

History of Bioinformatics

Scientists use Computer scripting languages such as Perl and Python

By 1991, a total of 1879 human genes had been mapped.

In 1996, Genethon published the final version of the Human Genetic Map. This concluded the end of the first phase of the Human Genome Project.

History of BioinformaticsYear Subject Name MBP

(Millions of base pairs)

1995 Haemophilus Influenza 1.8

1996 Bakers Yeast 12.1

1997 E.Coli 4.7

2000 Pseudomonas aeruginosa A. Thaliana

D. Melonagaster

6.3

100

180

2001 Human Genome 3,000

2002 House Mouse 2,500

Bioinformatics Today

There are several important problems where AI approaches are particularly promising Prediction of Protein Structure Semiautomatic drug design Knowledge acquisition from genetic data

Functional Genomics and the Robot Scientist Robot scientist developed by University of

Wales researchers Designed for the study of functional genomics Tested on yeast metabolic pathways Utilizes logical and associationist knowledge

representation schemes

Ross D. King, et al., Nature, January 2004

The Robot Scientist

Source: BBC News

Yeast Metabolic Pathways

Hypothesis Generation and Experimentation Loop

Ross D. King, et al., Nature, January 2004

Integration of Artificial Intelligence Utilizes a Prolog database to store

background biological information Prolog can inspect biological information,

infer knowledge, and make predictions Optimal hypothesis is determined using

machine learning, which looks at probabilities and associated cost

Experimental Results Performance similar to humans Performance significantly better than “naïve” or

“random” selection of experiments

Ross D. King, et al., Nature, January 2004

For 70% classification accuracy:A hundredth the cost of randomA third the cost of naive

Major Challenges and Research Issues

Requires individuals with knowledge of both disciplines

Requires collaboration of individuals from diverse disciplines

Major Challenges and Research Issues Data generation in biology/bioinformatics is

outpacing methods of data analysis Data interpretation and generation of hypotheses

requires intelligence AI offers established methods for knowledge

representation and “intelligent” data interpretation Predict utilization of AI in bioinformatics to increase

References and Additional ResourcesRoss D. King, Kenneth E. Whelan, Ffion M. Jones, Philip G. K. Reiser, Christopher H.

Bryant, Stephen H. Muggleton, Douglas B. Kell & Stephen G. Oliver. Functional Genomic Hypothesis Generation and Experimentation by a Robot Scientist. Nature 427 (15), 2004.

A Short History of Bioinformatics - http://www.netsci.org/Science/Bioinform/feature06.html

History of Bioinformatics - http://www.geocities.com/bioinformaticsweb/his.html

National Center for Biotechnology Information - http://www.ncbi.nih.gov

Pubmed - http://www.pubmed.gov