1. AI and Bioinformatics From Database Mining to the Robot Scientist

2. 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

3. 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.

4. 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

5. 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.

6. History of Bioinformatics YearSubject NameMBP (Millions of base pairs) 1995 Haemophilus Influenza 1.8 1996Bakers Yeast12.1 1997E.Coli4.7 2000 Pseudomonas aeruginosa A. Thaliana D. Melonagaster 6.3 100 180 2001Human Genome3,000 2002House Mouse2,500

7. 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

8. 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

9. The Robot Scientist Source: BBC News

10. Yeast Metabolic Pathways

11. Hypothesis Generation and Experimentation Loop Ross D. King, et al., Nature, January 2004

12. 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

13. 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 random A third the cost of naive

14. Major Challenges and Research Issues Requires individuals with knowledge of both disciplines Requires collaboration of individuals from diverse disciplines

15. 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

16. References and Additional Resources Ross 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.htmlhttp://www.netsci.org/Science/Bioinform/feature06.html History of Bioinformatics - http://www.geocities.com/bioinformaticsweb/his.htmlhttp://www.geocities.com/bioinformaticsweb/his.html National Center for Biotechnology Information - http://www.ncbi.nih.govhttp://www.ncbi.nih.gov Pubmed - http://www.pubmed.govhttp://www.pubmed.gov