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AI and Bioinformatics

AI and Bioinformatics

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AI and Bioinformatics

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  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

  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 For 70% classification accuracy: A hundredth the cost of random A third the cost of naive Ross D. King, et al., Nature, January 2004

  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.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