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Machine Learning Framework for DNA Computing

Machine Learning Framework for DNA Computing. 3rd MEC Workshop 2001.11.30 신수용. DNAC vs. Lego. Consider each DNA base as “LEGO block” A, C, G, T. DNA Lego. DNAC vs. Lego. Solution Sequence of blocks AACTG  A + A + C + T + G Fixed length Computing

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Machine Learning Framework for DNA Computing

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  1. Machine Learning Framework for DNA Computing 3rd MEC Workshop 2001.11.30 신수용

  2. DNAC vs. Lego • Consider each DNA base as “LEGO block” • A, C, G, T DNA Lego (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  3. DNAC vs. Lego • Solution • Sequence of blocks • AACTG  A + A + C + T + G • Fixed length • Computing • Decompose the solution to each block • Rebuild the solution from those blocks (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  4. Lego operation • Hybridization • Combining the lego blocks each other. • Ligation • Given a lego blocks of length n, append a block to it and make a lego blocks of length n+1. • Electrophoresis • Check a lego length. • PCR • Amply (put into) lego blocks. • Selection • Extract a lego which we want to do exactly (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  5. DNAC vs. Lego Computing Process (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  6. Solution Initial Search Space • Most machine learning algorithms can be considered as the process of seeking for the optimal solution in the huge search space. (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  7. Search Space in DNAC • Do parallel search Solution Initial (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  8. Conventional ML algorithmsvs. DNAC algorithms • For n nodes Graph problems • Conventional ML algorithms • DNAC algorithms (ideally) • We can provide blocks unlimitedly Search for solution in the ONLYn! space. Search for solution in the space. (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  9. Conventional ML algorithmsvs. DNAC algorithms n! space space (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/ Exhaustive Search

  10. DNA Computing • It is important to limit search space. • Do not exhaustive search. • We need more intelligent search process. • Evolutionary process! (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  11. Molecular Evolutionary Computing • Combining DNA computing with evolutionary computation. • Use huge parallelism of DNA computing and smart techniques of evolutionary computation. • We can think that molecular evolutionary computing as evolutionary computation with unlimited population size (ideally). (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  12. MEC • Search  Selection  Search  Selection … Select proper search process (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  13. MEC • Abstract flow Search Selection Solution (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  14. Theoretical problem in DNAC • Hybridization is not wholly controlled, can we make a solution exactly? • Case by case approach (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  15. Theoretical problem in DNAC • Case (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  16. Source (Limited or Unlimited) Case (Tree view) Reaction stage A Stage 1 Step 4 Stage 2 AA AT AG AC Step 1, 2, 3 Stage 3 AAT AAC Stage 4 . . . . . . Hybridization probability P (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  17. Case1 • Constant hybridization success ratio, unlimited blocks, experiments and step by step search • Select best string at each step (each depth in tree) • hybridize only selected string for next step • Then the number of experiment X follows the negative binomial distribution • Thus, in this case, DNAC guarantees the success of experiments (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  18. Case 2 CASE 2, 3, 4 CASE 1 Add existing pool select expand select (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  19. Case 2, 3, 4 • Case2 • Uniform (?) • Formulation • Case3, 4 • Verify solutions • Probability • Verify assumptions in real life experiments (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

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