Neural computing in discovering RNA interactions

Yoshiyasu Takefuji, Dora Ben-Alon, Arieh Zaritsky

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

High-order RNA structures are involved in regulating many biological processes; various algorithms have been designed to predict them. Experimental methods to probe such structures and to decipher the results are tedious. Artificial intelligence and the neural network approach can support the process of discovering RNA structures. Secondary structures of RNA molecules are probed by autoradiographing gels, separating end-labeled fragments generated by base-specific RNases. This process is performed in both conditions, denaturing (for sequencing purposes) and native. The resultant autoradiograms are scanned using line-detection techniques to identify the fragments by comparing the lines with those obtained by 'alkaline ladders'. The identified paired bases are treated by either one of two methods to find the foldings which are consistent with the RNases' 'cutting' rules. One exploits the maximum independent set algorithm; the other, the planarization algorithm. They require, respectively, n and n2 processing elements, where n is the number of base pairs. The state of the system usually converges to the near-optimum solution within about 500 iteration steps, where each processing element implements the McCulloch-Pitts binary neuron. Our simulator, based on the proposed algorithm, discovered a new structure in a sequence of 38 bases, which is more stable than that formerly proposed.

Original languageEnglish
Pages (from-to)85-96
Number of pages12
JournalBioSystems
Volume27
Issue number2
DOIs
Publication statusPublished - 1992
Externally publishedYes

Fingerprint

RNA
Computing
Ribonucleases
Interaction
Fragment
Planarization
Line Detection
Biological Phenomena
Maximum Independent Set
ladders
secondary structure
artificial intelligence
Artificial Intelligence
Ladders
Secondary Structure
Processing
Folding
Base Pairing
biological processes
neural networks

Keywords

  • Artificial intelligence
  • Biological regulation
  • High-order RNA structures
  • Maximum independent set
  • Neural nets
  • planarization algorithms

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Biotechnology
  • Drug Discovery

Cite this

Neural computing in discovering RNA interactions. / Takefuji, Yoshiyasu; Ben-Alon, Dora; Zaritsky, Arieh.

In: BioSystems, Vol. 27, No. 2, 1992, p. 85-96.

Research output: Contribution to journalArticle

Takefuji, Yoshiyasu ; Ben-Alon, Dora ; Zaritsky, Arieh. / Neural computing in discovering RNA interactions. In: BioSystems. 1992 ; Vol. 27, No. 2. pp. 85-96.
@article{ebfea3278e8243a484a4b0e87d406b2a,
title = "Neural computing in discovering RNA interactions",
abstract = "High-order RNA structures are involved in regulating many biological processes; various algorithms have been designed to predict them. Experimental methods to probe such structures and to decipher the results are tedious. Artificial intelligence and the neural network approach can support the process of discovering RNA structures. Secondary structures of RNA molecules are probed by autoradiographing gels, separating end-labeled fragments generated by base-specific RNases. This process is performed in both conditions, denaturing (for sequencing purposes) and native. The resultant autoradiograms are scanned using line-detection techniques to identify the fragments by comparing the lines with those obtained by 'alkaline ladders'. The identified paired bases are treated by either one of two methods to find the foldings which are consistent with the RNases' 'cutting' rules. One exploits the maximum independent set algorithm; the other, the planarization algorithm. They require, respectively, n and n2 processing elements, where n is the number of base pairs. The state of the system usually converges to the near-optimum solution within about 500 iteration steps, where each processing element implements the McCulloch-Pitts binary neuron. Our simulator, based on the proposed algorithm, discovered a new structure in a sequence of 38 bases, which is more stable than that formerly proposed.",
keywords = "Artificial intelligence, Biological regulation, High-order RNA structures, Maximum independent set, Neural nets, planarization algorithms",
author = "Yoshiyasu Takefuji and Dora Ben-Alon and Arieh Zaritsky",
year = "1992",
doi = "10.1016/0303-2647(92)90049-5",
language = "English",
volume = "27",
pages = "85--96",
journal = "BioSystems",
issn = "0303-2647",
publisher = "Elsevier Ireland Ltd",
number = "2",

}

TY - JOUR

T1 - Neural computing in discovering RNA interactions

AU - Takefuji, Yoshiyasu

AU - Ben-Alon, Dora

AU - Zaritsky, Arieh

PY - 1992

Y1 - 1992

N2 - High-order RNA structures are involved in regulating many biological processes; various algorithms have been designed to predict them. Experimental methods to probe such structures and to decipher the results are tedious. Artificial intelligence and the neural network approach can support the process of discovering RNA structures. Secondary structures of RNA molecules are probed by autoradiographing gels, separating end-labeled fragments generated by base-specific RNases. This process is performed in both conditions, denaturing (for sequencing purposes) and native. The resultant autoradiograms are scanned using line-detection techniques to identify the fragments by comparing the lines with those obtained by 'alkaline ladders'. The identified paired bases are treated by either one of two methods to find the foldings which are consistent with the RNases' 'cutting' rules. One exploits the maximum independent set algorithm; the other, the planarization algorithm. They require, respectively, n and n2 processing elements, where n is the number of base pairs. The state of the system usually converges to the near-optimum solution within about 500 iteration steps, where each processing element implements the McCulloch-Pitts binary neuron. Our simulator, based on the proposed algorithm, discovered a new structure in a sequence of 38 bases, which is more stable than that formerly proposed.

AB - High-order RNA structures are involved in regulating many biological processes; various algorithms have been designed to predict them. Experimental methods to probe such structures and to decipher the results are tedious. Artificial intelligence and the neural network approach can support the process of discovering RNA structures. Secondary structures of RNA molecules are probed by autoradiographing gels, separating end-labeled fragments generated by base-specific RNases. This process is performed in both conditions, denaturing (for sequencing purposes) and native. The resultant autoradiograms are scanned using line-detection techniques to identify the fragments by comparing the lines with those obtained by 'alkaline ladders'. The identified paired bases are treated by either one of two methods to find the foldings which are consistent with the RNases' 'cutting' rules. One exploits the maximum independent set algorithm; the other, the planarization algorithm. They require, respectively, n and n2 processing elements, where n is the number of base pairs. The state of the system usually converges to the near-optimum solution within about 500 iteration steps, where each processing element implements the McCulloch-Pitts binary neuron. Our simulator, based on the proposed algorithm, discovered a new structure in a sequence of 38 bases, which is more stable than that formerly proposed.

KW - Artificial intelligence

KW - Biological regulation

KW - High-order RNA structures

KW - Maximum independent set

KW - Neural nets

KW - planarization algorithms

UR - http://www.scopus.com/inward/record.url?scp=0026466820&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0026466820&partnerID=8YFLogxK

U2 - 10.1016/0303-2647(92)90049-5

DO - 10.1016/0303-2647(92)90049-5

M3 - Article

VL - 27

SP - 85

EP - 96

JO - BioSystems

JF - BioSystems

SN - 0303-2647

IS - 2

ER -