Probabilistic logical inference using quantities of DNA strands

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We overview a series of our research on DNA-based supervised learning of Boolean formulae and its application to gene expression analyses. In our previous work, we have presented methods for encoding and evaluating Boolean formulae on DNA strands and supervised learning of Boolean formulae on DNA computers which is known as NP-hard problem in computational learning theory [Kearns and Vazirani 94]. We have also applied those methods to executing logical operations of gene expression profiles in test tube [Sakakibara and Suyama 00]. These proposed methods are discrete (qualitative) algorithms and do not deal with quantitative analysis and are not robust for noise and errors. Recently, we have proposed several methods to execute quantitative inferences using large quantities of DNA strands in test tube and extend the previous algorithms to robust ones for noise and errors in the data. These methods include probabilistic inference and randomized prediction, and weighted majority prediction and learning by am plification in the test tube based on the weighted majority algorithm [Littlestone and Warmuth 94].

Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Evolutionary Computation, ICEC
Pages797-804
Number of pages8
Volume2
Publication statusPublished - 2001
Externally publishedYes
EventCongress on Evolutionary Computation 2001 - Seoul, Korea, Republic of
Duration: 2001 May 272001 May 30

Other

OtherCongress on Evolutionary Computation 2001
CountryKorea, Republic of
CitySeoul
Period01/5/2701/5/30

Fingerprint

DNA
Supervised learning
Gene expression
Computational complexity
Chemical analysis

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

Cite this

Sakakibara, Y. (2001). Probabilistic logical inference using quantities of DNA strands. In Proceedings of the IEEE Conference on Evolutionary Computation, ICEC (Vol. 2, pp. 797-804)

Probabilistic logical inference using quantities of DNA strands. / Sakakibara, Yasubumi.

Proceedings of the IEEE Conference on Evolutionary Computation, ICEC. Vol. 2 2001. p. 797-804.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sakakibara, Y 2001, Probabilistic logical inference using quantities of DNA strands. in Proceedings of the IEEE Conference on Evolutionary Computation, ICEC. vol. 2, pp. 797-804, Congress on Evolutionary Computation 2001, Seoul, Korea, Republic of, 01/5/27.
Sakakibara Y. Probabilistic logical inference using quantities of DNA strands. In Proceedings of the IEEE Conference on Evolutionary Computation, ICEC. Vol. 2. 2001. p. 797-804
Sakakibara, Yasubumi. / Probabilistic logical inference using quantities of DNA strands. Proceedings of the IEEE Conference on Evolutionary Computation, ICEC. Vol. 2 2001. pp. 797-804
@inproceedings{a0814ba50589496a8775391b808a0265,
title = "Probabilistic logical inference using quantities of DNA strands",
abstract = "We overview a series of our research on DNA-based supervised learning of Boolean formulae and its application to gene expression analyses. In our previous work, we have presented methods for encoding and evaluating Boolean formulae on DNA strands and supervised learning of Boolean formulae on DNA computers which is known as NP-hard problem in computational learning theory [Kearns and Vazirani 94]. We have also applied those methods to executing logical operations of gene expression profiles in test tube [Sakakibara and Suyama 00]. These proposed methods are discrete (qualitative) algorithms and do not deal with quantitative analysis and are not robust for noise and errors. Recently, we have proposed several methods to execute quantitative inferences using large quantities of DNA strands in test tube and extend the previous algorithms to robust ones for noise and errors in the data. These methods include probabilistic inference and randomized prediction, and weighted majority prediction and learning by am plification in the test tube based on the weighted majority algorithm [Littlestone and Warmuth 94].",
author = "Yasubumi Sakakibara",
year = "2001",
language = "English",
volume = "2",
pages = "797--804",
booktitle = "Proceedings of the IEEE Conference on Evolutionary Computation, ICEC",

}

TY - GEN

T1 - Probabilistic logical inference using quantities of DNA strands

AU - Sakakibara, Yasubumi

PY - 2001

Y1 - 2001

N2 - We overview a series of our research on DNA-based supervised learning of Boolean formulae and its application to gene expression analyses. In our previous work, we have presented methods for encoding and evaluating Boolean formulae on DNA strands and supervised learning of Boolean formulae on DNA computers which is known as NP-hard problem in computational learning theory [Kearns and Vazirani 94]. We have also applied those methods to executing logical operations of gene expression profiles in test tube [Sakakibara and Suyama 00]. These proposed methods are discrete (qualitative) algorithms and do not deal with quantitative analysis and are not robust for noise and errors. Recently, we have proposed several methods to execute quantitative inferences using large quantities of DNA strands in test tube and extend the previous algorithms to robust ones for noise and errors in the data. These methods include probabilistic inference and randomized prediction, and weighted majority prediction and learning by am plification in the test tube based on the weighted majority algorithm [Littlestone and Warmuth 94].

AB - We overview a series of our research on DNA-based supervised learning of Boolean formulae and its application to gene expression analyses. In our previous work, we have presented methods for encoding and evaluating Boolean formulae on DNA strands and supervised learning of Boolean formulae on DNA computers which is known as NP-hard problem in computational learning theory [Kearns and Vazirani 94]. We have also applied those methods to executing logical operations of gene expression profiles in test tube [Sakakibara and Suyama 00]. These proposed methods are discrete (qualitative) algorithms and do not deal with quantitative analysis and are not robust for noise and errors. Recently, we have proposed several methods to execute quantitative inferences using large quantities of DNA strands in test tube and extend the previous algorithms to robust ones for noise and errors in the data. These methods include probabilistic inference and randomized prediction, and weighted majority prediction and learning by am plification in the test tube based on the weighted majority algorithm [Littlestone and Warmuth 94].

UR - http://www.scopus.com/inward/record.url?scp=0034870276&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034870276&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0034870276

VL - 2

SP - 797

EP - 804

BT - Proceedings of the IEEE Conference on Evolutionary Computation, ICEC

ER -