AI and Bioinformatics

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
Year 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
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.c
  om/bioinformaticsweb/his.html
• National Center for Biotechnology Information -
  http//www.ncbi.nih.gov
• Pubmed - http//www.pubmed.gov