A survey of exact motif finding algorithms

Yousif, Ali Basim; Khafaji, Hussein K.; Abbas, Thekra

doi:10.11591/ijeecs.v27.i2.pp1109-1118

Cited by 2 publications

(2 citation statements)

References 38 publications

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“…Bioinformatics involves the analysis of huge amounts of genetic data. In order to analyze the genome, sequence data, and anticipate the structure and function of biological macromolecules that are utilized in the construction of the genome, the multidisciplinary area of bioinformatics collaborates with the biological sciences, statistics, and information technology [8]. Finding suitable methods to determine the similarities between DNA sequences is one of the most difficult tasks.…”

Section: Bioinformaticsmentioning

confidence: 99%

A survey for the methods of detection and classification of genetic mutations

Saloom

Khafaji

2022

IJEECS

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<span>Research into pathogenic mutations is vital and useful for understanding illness progression, prognosis, and gene-disease connections. Furthermore, pathogenic mutations might have negative implications, such as the development of illnesses or medical problems. In this article, we critically review published studies that are concerned with the detection of genetic mutations and their types using genomic data. Using the reporting items for systematic reviews and meta-analysis criteria, a complete search was conducted on IEEE, Scopus, the Web of Science, Google Scholar, and Elsevier. In our review, we included 73 papers out of a total of more than 150 that were initially discovered. The most common data types used to detect and predict models are deoxyribose nucleic acid (DNA) and protein sequencing data. The examined models have a good level of accuracy. We devised a methodology for developing a detective and predictive model that takes into account all stages of mutation identification and classification.</span>

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Section: Bioinformaticsmentioning

confidence: 99%

A survey for the methods of detection and classification of genetic mutations

Saloom

Khafaji

2022

IJEECS

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“…Typically, single-base representation is used to code databases. ese bases are adenine (A), thymine (T), cytosine (C), guanine (G), isoleucine (I), phenylalanine (F), serine (S), glutamine (Q), histidine (H), asparagine (N), aspartic acid (D), arginine (R), glutamic acid(E), lysine (K), leucine (L), valine (V), tryptophan (W), tyrosine (Y), methionine (M), and proline (P) [21,22]. In this paper, the bases of protein motifs will be used to hide data in specified bits of binary representation of a base.…”

Section: Protein In Bioinformaticsmentioning

confidence: 99%

Protein Motifs to Hide GA-Based Encrypted Data

Mawla

Khafaji

2022

Scientific Programming

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The arms of the Internet octopus have reached the ends of the planet. As it has become indispensable in our daily lives, huge amounts of information are transmitted through this network, and it is growing momentarily, which has led to an increase in the number of attacks on this information. Keeping the security of this information has become a necessity today. Therefore, the scientists of cryptography and steganography have seen a great and rapid development in the previous years to the present day, where various security and protection techniques have been used in these two technologies. In this research, it was emphasized to secure the confidentiality and security of the transmitted data between the sending and receiving parties by using both techniques of encryption and steganography. In contrast, where genetic algorithms and logic gates are exploited in an encryption process, in an unprecedented approach, protein motifs are used to mask the encoded message, gaining more dispersion because there are 20 bases used to represent the protein. The real payload gained ranges between 0.8 and 2.666, which outperforms the algorithms that depend on DNA sequences.

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A survey of exact motif finding algorithms

Cited by 2 publications

References 38 publications

A survey for the methods of detection and classification of genetic mutations

A survey for the methods of detection and classification of genetic mutations

Protein Motifs to Hide GA-Based Encrypted Data

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