Languages of DNA Based Code Words

Jonoska, Nataša; Mahalingam, Kalpana

doi:10.1007/978-3-540-24628-2_8

Cited by 33 publications

(25 citation statements)

References 13 publications

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“…DNA strands consist of four types of bases: thymine (T), adenine (A), cytosine (C), and guanine (G), as shown in Figure 1b. Not all of the bases on the DNA strands have direct influence on lives, which means only a few pieces of DNA strands, i.e., genes, play a critical role in biological difference [25]. Genes consist of four types of original bases.…”

Section: The Basic Theory Of Dnamentioning

confidence: 99%

Multi-Probe Based Artificial DNA Encoding and Matching Classifier for Hyperspectral Remote Sensing Imagery

Zhao

Zhong

et al. 2016

Remote Sensing

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Abstract:In recent years, a novel matching classification strategy inspired by the artificial deoxyribonucleic acid (DNA) technology has been proposed for hyperspectral remote sensing imagery. Such a method can describe brightness and shape information of a spectrum by encoding the spectral curve into a DNA strand, providing a more comprehensive way for spectral similarity comparison. However, it suffers from two problems: data volume is amplified when all of the bands participate in the encoding procedure and full-band comparison degrades the importance of bands carrying key information. In this paper, a new multi-probe based artificial DNA encoding and matching (MADEM) method is proposed. In this method, spectral signatures are first transformed into DNA code words with a spectral feature encoding operation. After that, multiple probes for interesting classes are extracted to represent the specific fragments of DNA strands. During the course of spectral matching, the different probes are compared to obtain the similarity of different types of land covers. By computing the absolute vector distance (AVD) between different probes of an unclassified spectrum and the typical DNA code words from the database, the class property of each pixel is set as the minimum distance class. The main benefit of this strategy is that the risk of redundant bands can be deeply reduced and critical spectral discrepancies can be enlarged. Two hyperspectral image datasets were tested. Comparing with the other classification methods, the overall accuracy can be improved from 1.22% to 10.09% and 1.19% to 15.87%, respectively. Furthermore, the kappa coefficient can be improved from 2.05% to 15.29% and 1.35% to 19.59%, respectively. This demonstrated that the proposed algorithm outperformed other traditional classification methods.

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Section: The Basic Theory Of Dnamentioning

confidence: 99%

Multi-Probe Based Artificial DNA Encoding and Matching Classifier for Hyperspectral Remote Sensing Imagery

Zhao

Zhong

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In [6], a θ-non-overlapping language is called to be strictly θ. Generally, if any other property holds in conjunction with (A), we add the qualifier strictly.…”

Section: Undesirable Bonds In Dna Languagesmentioning

confidence: 99%

“…Generally, if any other property holds in conjunction with (A), we add the qualifier strictly. Further properties have been defined in [6]. We denote by Sub k (L) the set of all subwords of L of the length k. The following property corresponds to Fig.…”

Section: Undesirable Bonds In Dna Languagesmentioning

confidence: 99%

“…Solid languages are also used in [10] as a tool for constructing error-detecting DNA languages that are invariant under bio-operations. We refer to [6,10,11] for further examples and for mutual relations between classes of a DNA languages satisfying these properties.…”

Section: Undesirable Bonds In Dna Languagesmentioning

confidence: 99%

“…See e.g. [1,3,4,5,6,8,10,13] for studies of properties of such sets (called also DNA languages) and methods of their construction.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Preventing Undesirable Bonds Between DNA Codewords

2005

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Abstract. The input data for DNA computing must be encoded into the form of single or double DNA strands. As complementary parts of single strands can bind together forming a double-stranded DNA sequence, one has to impose restrictions on these sets of DNA words (=languages) to prevent them from interacting in undesirable ways. We recall a list of known properties of DNA languages which are free of certain types of undesirable bonds. Then we introduce a general framework in which we can characterize each of these properties by a solution of a uniform formal language inequation. This characterization allows us among others to construct (i) a uniform algorithm deciding in polynomial time whether a given DNA language possesses any of the studied properties, and (ii) in many cases also an algorithm deciding whether a given DNA language is maximal with respect to the desired property.

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Bond-Free Languages: Formalizations, Maximality and Construction Methods

2005

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Abstract. The problem of negative design of DNA languages is addressed, that is, properties and construction methods of large sets of words that prevent undesired bonds when used in DNA computations. We recall a few existing formalizations of the problem and then define the property of sim-bond-freedom, where sim is a similarity relation between words. We show that this property is decidable for context-free languages and polynomial-time decidable for regular languages. The maximality of this property also turns out to be decidable for regular languages and polynomial-time decidable for an important case of the Hamming similarity. Then we consider various construction methods for Hamming bond-free languages, including the recently introduced method of templates, and obtain a complete structural characterization of all maximal Hamming bond-free languages. This result is applicable to the θ-k-code property introduced by Jonoska and Mahalingam.

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Languages of DNA Based Code Words

Cited by 33 publications

References 13 publications

Multi-Probe Based Artificial DNA Encoding and Matching Classifier for Hyperspectral Remote Sensing Imagery

Multi-Probe Based Artificial DNA Encoding and Matching Classifier for Hyperspectral Remote Sensing Imagery

Preventing Undesirable Bonds Between DNA Codewords

Bond-Free Languages: Formalizations, Maximality and Construction Methods

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