Pairwise sequence alignment algorithms

Haque, Waqar; Aravind, Alex; Reddy, B. S. R.

doi:10.1145/1551950.1551980

Cited by 44 publications

(22 citation statements)

References 23 publications

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“…Moreover, this upper bound can be obtained from the string alignment distance between S 1 and S 2 (

T_{1}^{'}

and

T_{2}^{'}

) that can be computed using a dynamic programming algorithm, Levenshtein distance (Levenshtein, ). We focus on Levenshtein distance because of its importance among all the string matching algorithms (Navarro, ; Haque, Aravind, & Reddy, ). The binary sequence matching method based on Levenshtein () has been adopted in dynamic programming algorithm for computing the string edit distance.…”

Section: Research Methodology and Datamentioning

confidence: 99%

A Graph Mining Approach to Identify Financial Reporting Patterns: An Empirical Examination of Industry Classifications

et al. 2018

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This study proposes a quantitative method using the eXtensible Business Reporting Language financial accounting taxonomies to identify firms' common business characteristics and demonstrates that this graph mining approach can effectively identify industry boundaries. The premise of this method is based on the previous findings that financial accounts and the structural semantic information represented in financial statements reveal firms' general business operations and common characteristics if they have similar business models. Specifically, we introduce a graph similarity metric combined with spectral clustering algorithm to quantify the similarity of financial disclosures. Through industry classification comparison with the traditional classification schemes, the Standard Industrial Classification and the North American Industry Classification System, we show that the proposed method consistently clusters firms into their respective industries based on financial disclosures with significantly lower variance in a time‐varying fashion. This novel graph mining method provides an automated way for decision makers to identify common business operations as well as detecting potential financial fraud and uncovering accounting information misrepresentation.

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“…Moreover, this upper bound can be obtained from the string alignment distance between S 1 and S 2 (

T_{1}^{'}

and

T_{2}^{'}

Section: Research Methodology and Datamentioning

confidence: 99%

A Graph Mining Approach to Identify Financial Reporting Patterns: An Empirical Examination of Industry Classifications

et al. 2018

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“…Sequence alignment is essential in solving such issues as prediction of the secondary and tertiary structures of proteins, prediction of the ancestral sequence or tracing the common genes in two organisms [28], prediction of gene function, sequence divergence, sequence assembly, database searching and so on [29]. However, sequence alignment is a highly complicated task because of the high number of possible combinations and searches.…”

Section: Sequence Miningmentioning

confidence: 99%

“…This complexity rises exponentially along with the size of the sequence. Therefore, sequence alignment is considered a highly computationally intensive problem [28]. Thus, both software and hardware advancements have the potential to improve the accuracy and speed.…”

Section: Sequence Miningmentioning

confidence: 99%

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Intelligent mining of large-scale bio-data: Bioinformatics applications

Hashemi

Ismail

Rafii

et al. 2017

Biotechnology & Biotechnological Equipment

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Today, there is a collection of a tremendous amount of bio-data because of the computerized applications worldwide. Therefore, scholars have been encouraged to develop effective methods to extract the hidden knowledge in these data. Consequently, a challenging and valuable area for research in artificial intelligence has been created. Bioinformatics creates heuristic approaches and complex algorithms using artificial intelligence and information technology in order to solve biological problems. Intelligent implication of the data can accelerate biological knowledge discovery. Data mining, as biology intelligence, attempts to find reliable, new, useful and meaningful patterns in huge amounts of data. Hence, there is a high potential to raise the interaction between artificial intelligence and bio-data mining. The present paper argues how artificial intelligence can assist bio-data analysis and gives an up-to-date review of different applications of bio-data mining. It also highlights some future perspectives of data mining in bioinformatics that can inspire further developments of data mining instruments. Important and new techniques are critically discussed for intelligent knowledge discovery of different types of row datasets with applicable examples in human, plant and animal sciences. Finally, a broad perception of this hot topic in data science is given.

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“…BLAST, FASTA, CLUSTALW, etc. [3][4][5][6] Among the first techniques for sequence comparison was The Diagram, a method proposed by Gibbs and Mcintyre. [7] Later heuristic approaches were developed for faster comparison.…”

Section: Introductionmentioning

confidence: 99%

Extended pairwise local alignment of wild card DNA/RNA sequences using dynamic programming

Fürstberger

Maucher

Kestler

2014

Journal of Statistical Computation and Simulation

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Optimal string alignment is used to discover evolutionary relationships or mutations in DNA/RNA or protein sequences. Errors, missing parts or uncertainty in such a sequence can be covered with wild cards, so-called wild bases. This makes an alignment possible even when the data are corrupted or incomplete. The extended pairwise local alignment of wild card DNA/RNA sequences requires additional calculations in the dynamic programming algorithm and necessitates a subsequent best-and worst-case analysis for the wild card positions. In this paper, we propose an algorithm which solves the problem of input data wild cards, offers a highly flexible set of parameters and displays a detailed alignment output and a compact representation of the mutated positions of the alignment. An implementation of the algorithm can be obtained at https://github.com/sysbio-bioinf/swat+ and http://sysbio.uni-ulm.de/?Software:Swat+.

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Pairwise sequence alignment algorithms

Cited by 44 publications

References 23 publications

A Graph Mining Approach to Identify Financial Reporting Patterns: An Empirical Examination of Industry Classifications

A Graph Mining Approach to Identify Financial Reporting Patterns: An Empirical Examination of Industry Classifications

Intelligent mining of large-scale bio-data: Bioinformatics applications

Extended pairwise local alignment of wild card DNA/RNA sequences using dynamic programming

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