Tree structures construction using key densities

Yao, S. Bing

doi:10.1145/800181.810359

Cited by 7 publications

(6 citation statements)

References 7 publications

(10 reference statements)

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“…(1) Mine all frequent sub-sequences from a data set of sequences. A trie tree data structure (De La Briandais, 1959;Yao, 1975) is constructed to identify individual frequent sub-sequences in the whole dataset. Trie, a kind of prefix tree, is a common string indicated by a node, where each node is associated with the list of IDs of the sequences in which the common string appears.…”

Section: Methods Frequent Sub-sequence Tuples Generationmentioning

confidence: 99%

CFSP: a collaborative frequent sequence pattern discovery algorithm for nucleic acid sequence classification

Peng

2020

PeerJ

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Background Conserved nucleic acid sequences play an essential role in transcriptional regulation. The motifs/templates derived from nucleic acid sequence datasets are usually used as biomarkers to predict biochemical properties such as protein binding sites or to identify specific non-coding RNAs. In many cases, template-based nucleic acid sequence classification performs better than some feature extraction methods, such as N-gram and k-spaced pairs classification. The availability of large-scale experimental data provides an unprecedented opportunity to improve motif extraction methods. The process for pattern extraction from large-scale data is crucial for the creation of predictive models. Methods In this article, a Teiresias-like feature extraction algorithm to discover frequent sub-sequences (CFSP) is proposed. Although gaps are allowed in some motif discovery algorithms, the distance and number of gaps are limited. The proposed algorithm can find frequent sequence pairs with a larger gap. The combinations of frequent sub-sequences in given protracted sequences capture the long-distance correlation, which implies a specific molecular biological property. Hence, the proposed algorithm intends to discover the combinations. A set of frequent sub-sequences derived from nucleic acid sequences with order is used as a base frequent sub-sequence array. The mutation information is attached to each sub-sequence array to implement fuzzy matching. Thus, a mutate records a single nucleotide variant or nucleotides insertion/deletion (indel) to encode a slight difference between frequent sequences and a matched subsequence of a sequence under investigation. Conclusions The proposed algorithm has been validated with several nucleic acid sequence prediction case studies. These data demonstrate better results than the recently available feature descriptors based methods based on experimental data sets such as miRNA, piRNA, and Sigma 54 promoters. CFSP is implemented in C++ and shell script; the source code and related data are available at https://github.com/HePeng2016/CFSP.

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Section: Methods Frequent Sub-sequence Tuples Generationmentioning

confidence: 99%

CFSP: a collaborative frequent sequence pattern discovery algorithm for nucleic acid sequence classification

Peng

2020

PeerJ

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“…The file organization produced consists of two parts: a hybrid multidime nsional-tr ie-trce diectory [13) and a data file. The directory obtained from the optimizati on process has two levels.…”

Section: Selection Of File Organizati Onsmentioning

confidence: 99%

Selection of file organization using an analytic model

Yao¹,

Merten

1975

Proceedings of the 1st International Conference on Very Large Data Bases - VLDB '75

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“…Using a different 260 approach, a simple closed form was obtained by Yao in a different context [5). The resulting expression was used in several applications [5,6,7] to estimate the expected number of data blocks accessed. Comparing this to the Cardenas approximation, it is shown that this refinement is significant when the blocking factor n/m is small.…”

mentioning

confidence: 99%

“…approach, a simple closed form was obtained by Yao in a different context [5). The resulting expression was used in several applications [5,6,7] to estimate the expected number of data blocks accessed.…”

mentioning

confidence: 99%

Approximating block accesses in database organizations

Yao

1977

Commun. ACM

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316

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When data records are grouped into blocks in secondary storage, it is frequently necessary to estimate the number of blocks XD accessed for a given query. In a recent paper [Ij, Cardenas gave the expression = m{\ -(I - (1) assuming that there are n records divided into m blocks and that the k records satisfying the query are distributed uniformly among the m blocks. The derivation of the expression was left to the reader as an exercise.Let us take a closer look at the expression.(1 -1/m) gives the probability that a particular block does not contain a particular record. If k records are selected independently, then the probability that a particular block not being "hit" is given by (1 -!/m)*. Hence I -(I -\/mY gives the probability that a particular block is "hit," and the expression follows.The assumption that the k records are selected independently implies selection with replacement. Since a record may be selected more than once, the k records may not be distinct. This is not valid in the case of a query access which retrieves all k distinct records at one time. In fact, Rothnie and Lozano showed that the result of eq. (1) gives the lower bound of the expected number of blocks accessed [2]. A more accurate analysis based on selection without replacement was given by Severance, but the precision problem makes the expression obtained computationally intractable (Appendix D in [3]). A similar approach by Siler results in a rather complicated recursive formula which can be computed (Appendix B in [4]). Another recursive formula was given by [3]. Using a different 260 approach, a simple closed form was obtained by Yao in a different context [5). The resulting expression was used in several applications [5,6,7] to estimate the expected number of data blocks accessed. Comparing this to the Cardenas approximation, it is shown that this refinement is significant when the blocking factor n/m is small. For large blocking factors (e.g. n/m > 10), the error involved in Cardenas' approximation is practically negligible. THEOREM (Yao). Given n records grouped into m blocks {I < m < n), each contains n/m records. If k records {k < n -n/m) are randomly selected from the n records, the expected number of blocks hit {blocks with at least one record selected) is given by m L ,=i n -I -\-\_\

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Tree structures construction using key densities

Cited by 7 publications

References 7 publications

CFSP: a collaborative frequent sequence pattern discovery algorithm for nucleic acid sequence classification

CFSP: a collaborative frequent sequence pattern discovery algorithm for nucleic acid sequence classification

Selection of file organization using an analytic model

Approximating block accesses in database organizations

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