Improving hash-q exact string matching algorithm with perfect hashing for DNA sequences

Exact string matching algorithms involve finding all occurrences of a pattern P in a text T. These algorithms have been extensively studied in computer science, primarily because of their applications in various fields such as text search and computational biology. The main goal of exact string matc...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Computers in biology and medicine Ročník 131; s. 104292
Hlavní autoři:	Karcioglu, Abdullah Ammar, Bulut, Hasan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Ltd 01.04.2021 Elsevier Limited
Témata:	Algorithms Collisions Computer applications Datasets Deoxyribonucleic acid Dictionaries DNA DNA Sequences E coli Gene sequencing Hash based algorithms Hash function Internal Medicine Nucleotide sequence Other Pattern matching Performance measurement Run time (computers) Sequence analysis String matching String matching algorithms Hash function Sequence analysis Pattern matching String matching algorithms DNA Sequences Hash Function Pattern Matching Sequence Analysis String Matching Algorithms
ISSN:	0010-4825, 1879-0534, 1879-0534
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Exact string matching algorithms involve finding all occurrences of a pattern P in a text T. These algorithms have been extensively studied in computer science, primarily because of their applications in various fields such as text search and computational biology. The main goal of exact string matching algorithms is to find all pattern matches correctly within the shortest possible time frame. Although hash-based string matching algorithms run fast, there are shortcomings, such as hash collisions. In this study, a novel hash function has been proposed that eliminates hash collisions for DNA sequences. It provides us perfect hashing and produces hash values in a time-efficient manner. We have proposed two exact string matching algorithms based on the proposed hash function. In the first approach, we replace the traditional Hash-q algorithm's hash function with the proposed one. In the second approach, we improved the first approach by utilizing the shift size indicated at the (m−1)th entry in the good suffix shift table when an exact matching is found. In these approaches, we eliminate the need to compare the last q characters of the pattern and text. We have included six algorithms from the literature in our evaluations. E. Coli and Human Chromosome1 datasets from the literature and a synthetic dataset produced randomly are utilized for comparisons. The results show that the proposed approaches achieve better performance metrics in terms of the average runtime, the average number of character comparisons, and the average number of hash comparisons. [Display omitted] •A novel collision free hash function is proposed for DNA sequences.•Based on the proposed hash function, we propose Hash-q Algorithm with Unique FNG algorithm as a first improvement to the traditional Hash-q algorithm.•Based on the proposed hash function, we propose Hash-q Boyer-Moore Algorithm with UniqueFNG algorithm as a second improvement to the traditional Hash-q algorithm.•The approaches are compared for E. Coli, synthetic dataset and Human Chromosome1 datasets.•Significant improvements have been achieved for the avg. runtime, the avg. # of character and the avg. # of hash comparisons.
AbstractList	Exact string matching algorithms involve finding all occurrences of a pattern P in a text T. These algorithms have been extensively studied in computer science, primarily because of their applications in various fields such as text search and computational biology. The main goal of exact string matching algorithms is to find all pattern matches correctly within the shortest possible time frame. Although hash-based string matching algorithms run fast, there are shortcomings, such as hash collisions. In this study, a novel hash function has been proposed that eliminates hash collisions for DNA sequences. It provides us perfect hashing and produces hash values in a time-efficient manner. We have proposed two exact string matching algorithms based on the proposed hash function. In the first approach, we replace the traditional Hash-q algorithm's hash function with the proposed one. In the second approach, we improved the first approach by utilizing the shift size indicated at the (m−1)th entry in the good suffix shift table when an exact matching is found. In these approaches, we eliminate the need to compare the last q characters of the pattern and text. We have included six algorithms from the literature in our evaluations. E. Coli and Human Chromosome1 datasets from the literature and a synthetic dataset produced randomly are utilized for comparisons. The results show that the proposed approaches achieve better performance metrics in terms of the average runtime, the average number of character comparisons, and the average number of hash comparisons. [Display omitted] •A novel collision free hash function is proposed for DNA sequences.•Based on the proposed hash function, we propose Hash-q Algorithm with Unique FNG algorithm as a first improvement to the traditional Hash-q algorithm.•Based on the proposed hash function, we propose Hash-q Boyer-Moore Algorithm with UniqueFNG algorithm as a second improvement to the traditional Hash-q algorithm.•The approaches are compared for E. Coli, synthetic dataset and Human Chromosome1 datasets.•Significant improvements have been achieved for the avg. runtime, the avg. # of character and the avg. # of hash comparisons. Exact string matching algorithms involve finding all occurrences of a pattern P in a text T. These algorithms have been extensively studied in computer science, primarily because of their applications in various fields such as text search and computational biology. The main goal of exact string matching algorithms is to find all pattern matches correctly within the shortest possible time frame. Although hash-based string matching algorithms run fast, there are shortcomings, such as hash collisions. In this study, a novel hash function has been proposed that eliminates hash collisions for DNA sequences. It provides us perfect hashing and produces hash values in a time-efficient manner. We have proposed two exact string matching algorithms based on the proposed hash function. In the first approach, we replace the traditional Hash-q algorithm's hash function with the proposed one. In the second approach, we improved the first approach by utilizing the shift size indicated at the (m-1)th entry in the good suffix shift table when an exact matching is found. In these approaches, we eliminate the need to compare the last q characters of the pattern and text. We have included six algorithms from the literature in our evaluations. E. Coli and Human Chromosome1 datasets from the literature and a synthetic dataset produced randomly are utilized for comparisons. The results show that the proposed approaches achieve better performance metrics in terms of the average runtime, the average number of character comparisons, and the average number of hash comparisons.Exact string matching algorithms involve finding all occurrences of a pattern P in a text T. These algorithms have been extensively studied in computer science, primarily because of their applications in various fields such as text search and computational biology. The main goal of exact string matching algorithms is to find all pattern matches correctly within the shortest possible time frame. Although hash-based string matching algorithms run fast, there are shortcomings, such as hash collisions. In this study, a novel hash function has been proposed that eliminates hash collisions for DNA sequences. It provides us perfect hashing and produces hash values in a time-efficient manner. We have proposed two exact string matching algorithms based on the proposed hash function. In the first approach, we replace the traditional Hash-q algorithm's hash function with the proposed one. In the second approach, we improved the first approach by utilizing the shift size indicated at the (m-1)th entry in the good suffix shift table when an exact matching is found. In these approaches, we eliminate the need to compare the last q characters of the pattern and text. We have included six algorithms from the literature in our evaluations. E. Coli and Human Chromosome1 datasets from the literature and a synthetic dataset produced randomly are utilized for comparisons. The results show that the proposed approaches achieve better performance metrics in terms of the average runtime, the average number of character comparisons, and the average number of hash comparisons. Exact string matching algorithms involve finding all occurrences of a pattern P in a text T. These algorithms have been extensively studied in computer science, primarily because of their applications in various fields such as text search and computational biology. The main goal of exact string matching algorithms is to find all pattern matches correctly within the shortest possible time frame. Although hash-based string matching algorithms run fast, there are shortcomings, such as hash collisions. In this study, a novel hash function has been proposed that eliminates hash collisions for DNA sequences. It provides us perfect hashing and produces hash values in a time-efficient manner. We have proposed two exact string matching algorithms based on the proposed hash function. In the first approach, we replace the traditional Hash-q algorithm's hash function with the proposed one. In the second approach, we improved the first approach by utilizing the shift size indicated at the (m-1) entry in the good suffix shift table when an exact matching is found. In these approaches, we eliminate the need to compare the last q characters of the pattern and text. We have included six algorithms from the literature in our evaluations. E. Coli and Human Chromosome1 datasets from the literature and a synthetic dataset produced randomly are utilized for comparisons. The results show that the proposed approaches achieve better performance metrics in terms of the average runtime, the average number of character comparisons, and the average number of hash comparisons. Exact string matching algorithms involve finding all occurrences of a pattern P in a text T. These algorithms have been extensively studied in computer science, primarily because of their applications in various fields such as text search and computational biology. The main goal of exact string matching algorithms is to find all pattern matches correctly within the shortest possible time frame. Although hash-based string matching algorithms run fast, there are shortcomings, such as hash collisions. In this study, a novel hash function has been proposed that eliminates hash collisions for DNA sequences. It provides us perfect hashing and produces hash values in a time-efficient manner. We have proposed two exact string matching algorithms based on the proposed hash function. In the first approach, we replace the traditional Hash-q algorithm's hash function with the proposed one. In the second approach, we improved the first approach by utilizing the shift size indicated at the (m−1)th entry in the good suffix shift table when an exact matching is found. In these approaches, we eliminate the need to compare the last q characters of the pattern and text. We have included six algorithms from the literature in our evaluations. E. Coli and Human Chromosome1 datasets from the literature and a synthetic dataset produced randomly are utilized for comparisons. The results show that the proposed approaches achieve better performance metrics in terms of the average runtime, the average number of character comparisons, and the average number of hash comparisons. AbstractExact string matching algorithms involve finding all occurrences of a pattern P in a text T. These algorithms have been extensively studied in computer science, primarily because of their applications in various fields such as text search and computational biology. The main goal of exact string matching algorithms is to find all pattern matches correctly within the shortest possible time frame. Although hash-based string matching algorithms run fast, there are shortcomings, such as hash collisions. In this study, a novel hash function has been proposed that eliminates hash collisions for DNA sequences. It provides us perfect hashing and produces hash values in a time-efficient manner. We have proposed two exact string matching algorithms based on the proposed hash function. In the first approach, we replace the traditional Hash-q algorithm’s hash function with the proposed one. In the second approach, we improved the first approach by utilizing the shift size indicated at the (m−1)th entry in the good suffix shift table when an exact matching is found. In these approaches, we eliminate the need to compare the last q characters of the pattern and text. We have included six algorithms from the literature in our evaluations. E. Coli and Human Chromosome1 datasets from the literature and a synthetic dataset produced randomly are utilized for comparisons. The results show that the proposed approaches achieve better performance metrics in terms of the average runtime, the average number of character comparisons, and the average number of hash comparisons.
ArticleNumber	104292
Author	Bulut, Hasan Karcioglu, Abdullah Ammar
Author_xml	– sequence: 1 givenname: Abdullah Ammar surname: Karcioglu fullname: Karcioglu, Abdullah Ammar email: abdullah.ammar.karcioglu@ege.edu.tr – sequence: 2 givenname: Hasan surname: Bulut fullname: Bulut, Hasan email: hasan.bulut@ege.edu.tr
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/33662682$$D View this record in MEDLINE/PubMed
BookMark	eNqNUk1v1DAQtVAR3Rb-AorEhUu246_UuVSUQmmlqhwAwc1ynHHXSxJv7Wxp_z2OtgVppUp7sa3xmzcz780B2RvCgIQUFOYUaHW0nNvQrxofemznDBjNYcFq9oLMqDquS5Bc7JEZAIVSKCb3yUFKSwAQwOEV2ee8qlil2Iz8vOxXMdz54aZYmLQobwu8N3Ys0hinWG9Gu5geprsJ0Y-LvviTz2KF0WGGTTnTtwux-HR9WiS8XeNgMb0mL53pEr55vA_Jj_PP388uyquvXy7PTq9KK6kYSyeUUbUVKBraCsCWgURZ2eMGpHOSsVq01jkuaxQOG-WowIYLi63kqm0VPyTvN7x5ilw6jbr3yWLXmQHDOmkmaiWzQFRk6Lst6DKs45C700wCVVLyCjLq7SNq3WR19Sr63sQH_SRZBpxsADaGlCI6bf1oRh-GMRrfaQp68kgv9X-P9OSR3niUCdQWwVONHVI_blIxS3rnMepk_aR362O2Q7fB70JyskViOz94a7rf-IDpnyhUJ6ZBf5u2aFoiRvMCqepXJvjwPMFuPfwF9PXchQ
CitedBy_id	crossref_primary_10_1016_j_compbiomed_2022_105534 crossref_primary_10_1155_2021_1716182 crossref_primary_10_21597_jist_1404898 crossref_primary_10_1155_2023_3278505 crossref_primary_10_2478_qic_2025_0016 crossref_primary_10_4018_IJDST_341269 crossref_primary_10_1002_cpe_6505 crossref_primary_10_1016_j_compbiomed_2021_104656 crossref_primary_10_1016_j_jksuci_2024_102089 crossref_primary_10_1155_2022_6521905
Cites_doi	10.1145/79173.79184 10.1016/j.imu.2020.100356 10.1137/0206024 10.1016/j.compbiomed.2004.06.002 10.1016/j.compbiolchem.2010.03.007 10.1016/j.compbiomed.2003.11.003 10.1002/spe.4380100608 10.1155/2019/7074387 10.1002/(SICI)1097-024X(199707)27:7<851::AID-SPE108>3.0.CO;2-D 10.1109/ACCESS.2019.2914071 10.1016/j.ipl.2007.01.002 10.1145/2431211.2431212 10.1145/359842.359859 10.1186/1748-7188-8-25 10.1016/j.imu.2020.100323 10.1016/j.compbiomed.2019.04.002
ContentType	Journal Article
Copyright	2021 Elsevier Ltd Elsevier Ltd Copyright © 2021 Elsevier Ltd. All rights reserved. 2021. Elsevier Ltd
Copyright_xml	– notice: 2021 Elsevier Ltd – notice: Elsevier Ltd – notice: Copyright © 2021 Elsevier Ltd. All rights reserved. – notice: 2021. Elsevier Ltd
DBID	AAYXX CITATION NPM 3V. 7RV 7X7 7XB 88E 8AL 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK 8G5 ABUWG AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ GUQSH HCIFZ JQ2 K7- K9. KB0 LK8 M0N M0S M1P M2O M7P M7Z MBDVC NAPCQ P5Z P62 P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8
DOI	10.1016/j.compbiomed.2021.104292
DatabaseName	CrossRef PubMed ProQuest Central (Corporate) Nursing & Allied Health Database Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Research Library (Alumni) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC Biological Science Collection ProQuest Central ProQuest Technology Collection Natural Science Collection ProQuest One ProQuest Central Korea Engineering Research Database ProQuest Health & Medical Collection Health Research Premium Collection (Alumni) ProQuest Central Student ProQuest Research Library SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Database (Alumni Edition) ProQuest Biological Science Collection Computing Database Health & Medical Collection (Alumni) Medical Database Research Library Biological Science Database Biochemistry Abstracts 1 Research Library (Corporate) Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic
DatabaseTitle	CrossRef PubMed Research Library Prep Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection SciTech Premium Collection ProQuest Central China ProQuest One Applied & Life Sciences Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing Research Library (Alumni Edition) ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea ProQuest Research Library ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest Nursing & Allied Health Source ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest Medical Library Biochemistry Abstracts 1 ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic PubMed Research Library Prep
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine
EISSN	1879-0534
EndPage	104292
ExternalDocumentID	33662682 10_1016_j_compbiomed_2021_104292 S001048252100086X 1_s2_0_S001048252100086X
Genre	Journal Article
GroupedDBID	--- --K --M --Z -~X .1- .55 .DC .FO .GJ .~1 0R~ 1B1 1P~ 1RT 1~. 1~5 29F 4.4 457 4G. 53G 5GY 5VS 7-5 71M 77I 7RV 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ 8G5 8P~ 9JN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYFN AAYWO ABBOA ABFNM ABJNI ABMAC ABMZM ABOCM ABUWG ABWVN ABXDB ACDAQ ACGFS ACIEU ACIUM ACIWK ACLOT ACNNM ACPRK ACRLP ACRPL ACVFH ACZNC ADBBV ADCNI ADEZE ADJOM ADMUD ADNMO AEBSH AEIPS AEKER AENEX AEUPX AEVXI AFJKZ AFKRA AFPUW AFRAH AFRHN AFTJW AFXIZ AGHFR AGQPQ AGUBO AGYEJ AHHHB AHMBA AHZHX AIALX AIEXJ AIGII AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX AOUOD APXCP ARAPS ASPBG AVWKF AXJTR AZFZN AZQEC BBNVY BENPR BGLVJ BHPHI BKEYQ BKOJK BLXMC BNPGV BPHCQ BVXVI CCPQU CS3 DU5 DWQXO EBS EFJIC EFKBS EFLBG EJD EMOBN EO8 EO9 EP2 EP3 EX3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN FYUFA G-2 G-Q GBLVA GBOLZ GNUQQ GUQSH HCIFZ HLZ HMCUK HMK HMO HVGLF HZ~ IHE J1W K6V K7- KOM LK8 LX9 M1P M29 M2O M41 M7P MO0 N9A NAPCQ O-L O9- OAUVE OZT P-8 P-9 P2P P62 PC. PHGZM PHGZT PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO Q38 R2- ROL RPZ RXW SAE SBC SCC SDF SDG SDP SEL SES SEW SPC SPCBC SSH SSV SSZ SV3 T5K TAE UAP UKHRP WOW WUQ X7M XPP Z5R ZGI ~G- ~HD 3V. AACTN AFCTW AFKWA AJOXV ALIPV AMFUW M0N RIG AAIAV ABLVK ABYKQ AHPSJ AJBFU LCYCR 9DU AAYXX AFFHD CITATION AGCQF AGRNS NPM 7XB 8AL 8FD 8FK FR3 JQ2 K9. M7Z MBDVC P64 PKEHL PQEST PQUKI PRINS Q9U 7X8 PUEGO
ID	FETCH-LOGICAL-c514t-f48a89c4e4b1d40ed205e56c7b05ff52294dcff359e4feb8f14eb34ced538dd83
IEDL.DBID	M7P
ISICitedReferencesCount	11
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000631619900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0010-4825 1879-0534
IngestDate	Sat Sep 27 17:59:21 EDT 2025 Sat Nov 29 14:44:16 EST 2025 Mon Jul 21 05:20:46 EDT 2025 Sat Nov 29 07:31:44 EST 2025 Tue Nov 18 21:48:56 EST 2025 Fri Feb 23 02:45:19 EST 2024 Tue Feb 25 20:03:27 EST 2025 Tue Oct 14 19:33:06 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Keywords	Hash function Sequence analysis Pattern matching String matching algorithms DNA Sequences Hash Function Pattern Matching Sequence Analysis String Matching Algorithms
Language	English
License	Copyright © 2021 Elsevier Ltd. All rights reserved.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c514t-f48a89c4e4b1d40ed205e56c7b05ff52294dcff359e4feb8f14eb34ced538dd83
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
PMID	33662682
PQID	2501855360
PQPubID	1226355
PageCount	1
ParticipantIDs	proquest_miscellaneous_2498502114 proquest_journals_2501855360 pubmed_primary_33662682 crossref_citationtrail_10_1016_j_compbiomed_2021_104292 crossref_primary_10_1016_j_compbiomed_2021_104292 elsevier_sciencedirect_doi_10_1016_j_compbiomed_2021_104292 elsevier_clinicalkeyesjournals_1_s2_0_S001048252100086X elsevier_clinicalkey_doi_10_1016_j_compbiomed_2021_104292
PublicationCentury	2000
PublicationDate	2021-04-01
PublicationDateYYYYMMDD	2021-04-01
PublicationDate_xml	– month: 04 year: 2021 text: 2021-04-01 day: 01
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: Oxford
PublicationTitle	Computers in biology and medicine
PublicationTitleAlternate	Comput Biol Med
PublicationYear	2021
Publisher	Elsevier Ltd Elsevier Limited
Publisher_xml	– name: Elsevier Ltd – name: Elsevier Limited
References	Horspool (bib21) 1980; 10 Deorowicz, Grabowski (bib4) 2013; 8 Boyer, Moore (bib20) 1977; 20 Kim, Kim (bib16) 1999, August Faro (bib15) 2016 Upama, Khan, Zemim, Yasmin, Sakib (bib2) 2015, December Lecroq (bib13) 2007; 102 Hakak, Kamsin, Shivakumara, Gilkar, Khan, M Imran (bib17) 2019; 7 Wandelt, Bux, Leser (bib3) 2014; 3 Kanuga (bib10) 2015, March Wu, Manber (bib12) 1994 Chen, Lu, Ram (bib8) 2004, August Karp (bib11) 1987 Mane, Pangu (bib1) 2016 Tarhio, Peltola (bib14) 1997; 27 Lander (bib29) 2004 Faro, Lecroq (bib18) 2013; 45 Sunday (bib22) 1990; 33 Plunkett (bib28) 2008 Najam, Rasool, Ahmad, Ashraf, Malik (bib9) 2019 Naghibzadeh, Savari, Savadi, Saadati, Mehrazin (bib27) 2020 Berry, Ravindran (bib23) 1999 Wang, Li, Cai, Chen (bib25) 2005; 35 Bakr, Sharawi (bib5) 2013; 3 Jeong, Park, Kang, Lim (bib6) 2010; 34 Hyyrö, Juhola, Vihinen (bib24) 2005; 35 Xu, Yang, Kang, Wang, Liu (bib26) 2019; 108 Khitmoh, Smanchat, Tongsima (bib7) 2020 Knuth, Morris, Pratt (bib19) 1977; 6 Faro (10.1016/j.compbiomed.2021.104292_bib15) 2016 Horspool (10.1016/j.compbiomed.2021.104292_bib21) 1980; 10 Sunday (10.1016/j.compbiomed.2021.104292_bib22) 1990; 33 Bakr (10.1016/j.compbiomed.2021.104292_bib5) 2013; 3 Karp (10.1016/j.compbiomed.2021.104292_bib11) Wang (10.1016/j.compbiomed.2021.104292_bib25) 2005; 35 Hakak (10.1016/j.compbiomed.2021.104292_bib17) 2019; 7 Berry (10.1016/j.compbiomed.2021.104292_bib23) 1999 Wu (10.1016/j.compbiomed.2021.104292_bib12) 1994 Najam (10.1016/j.compbiomed.2021.104292_bib9) 2019 Lecroq (10.1016/j.compbiomed.2021.104292_bib13) 2007; 102 Upama (10.1016/j.compbiomed.2021.104292_bib2) 2015 Deorowicz (10.1016/j.compbiomed.2021.104292_bib4) 2013; 8 Chen (10.1016/j.compbiomed.2021.104292_bib8) 2004 Kim (10.1016/j.compbiomed.2021.104292_bib16) 1999 Naghibzadeh (10.1016/j.compbiomed.2021.104292_bib27) 2020 Jeong (10.1016/j.compbiomed.2021.104292_bib6) 2010; 34 Khitmoh (10.1016/j.compbiomed.2021.104292_bib7) 2020 Xu (10.1016/j.compbiomed.2021.104292_bib26) 2019; 108 Lander (10.1016/j.compbiomed.2021.104292_bib29) Boyer (10.1016/j.compbiomed.2021.104292_bib20) 1977; 20 Hyyrö (10.1016/j.compbiomed.2021.104292_bib24) 2005; 35 Kanuga (10.1016/j.compbiomed.2021.104292_bib10) 2015 Mane (10.1016/j.compbiomed.2021.104292_bib1) 2016 Faro (10.1016/j.compbiomed.2021.104292_bib18) 2013; 45 Knuth (10.1016/j.compbiomed.2021.104292_bib19) 1977; 6 Wandelt (10.1016/j.compbiomed.2021.104292_bib3) 2014; 3 Plunkett (10.1016/j.compbiomed.2021.104292_bib28) Tarhio (10.1016/j.compbiomed.2021.104292_bib14) 1997; 27
References_xml	– volume: 3 start-page: 315 year: 2014 end-page: 326 ident: bib3 article-title: Trends in genome compression, Current Bioinformatics, vol publication-title: 9, no – year: 2004 ident: bib29 article-title: Homo sapiens chromosome, GRCh38.p13 Primary Assembly, NCBI data – volume: 33 start-page: 132 year: 1990 end-page: 142 ident: bib22 article-title: A very fast substring search algorithm publication-title: Commun. ACM – year: 2008 ident: bib28 article-title: Escherichia coli str. K-12 substr. DH10B, complete sequence dataset, NCBI data – volume: 45 start-page: 1 year: 2013 end-page: 42 ident: bib18 article-title: The exact online string matching problem: a review of the most recent results publication-title: ACM Comput. Surv. – start-page: 1 year: 2016 end-page: 5 ident: bib1 article-title: Disease diagnosis using pattern matching algorithm from DNA sequencing: a sequential and GPGPU based approach publication-title: Proceedings of the International Conference on Informatics and Analytics – start-page: 62 year: 2004, August end-page: 68 ident: bib8 article-title: Compressed pattern matching in dna sequences publication-title: Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004 – start-page: 1 year: 1994 end-page: 11 ident: bib12 article-title: A Fast Algorithm for Multi-Pattern Searching – start-page: 16 year: 1999 end-page: 28 ident: bib23 article-title: A fast string matching algorithm and experimental results publication-title: Stringology – start-page: 44 year: 1999, August end-page: 49 ident: bib16 article-title: A fast multiple string-pattern matching algorithm publication-title: Proceedings of 17th AoM/IAoM Conference on Computer Science – start-page: 172 year: 2015, December end-page: 177 ident: bib2 article-title: A new approach in pattern matching: codon Detection in DNA and RNA using hash function (CDDRHF) publication-title: 2015 18th International Conference on Computer and Information Technology (ICCIT) – start-page: 65 year: 2016 end-page: 76 ident: bib15 article-title: A very fast string matching algorithm based on condensed alphabets publication-title: International Conference on Algorithmic Applications in Management – volume: 3 start-page: 72 year: 2013 end-page: 81 ident: bib5 article-title: DNA lossless compression algorithms, American Journal of Bioinformatics Research, vol publication-title: 3, no – volume: 8 year: 2013 ident: bib4 article-title: Data compression for sequencing data publication-title: Algorithm Mol. Biol. – volume: 6 start-page: 323 year: 1977 end-page: 350 ident: bib19 article-title: Fast pattern matching in strings publication-title: SIAM J. Comput. – volume: 10 start-page: 501 year: 1980 end-page: 506 ident: bib21 article-title: Practical fast searching in strings publication-title: Software Pract. Ex. – volume: 34 start-page: 131 year: 2010 end-page: 136 ident: bib6 article-title: An efficient similarity search based on indexing in large DNA databases publication-title: Comput. Biol. Chem. – year: 2020 ident: bib7 article-title: Stretch Profile: a pruning technique to accelerate DNA sequence search publication-title: Informatics in Medicine Unlocked – volume: 27 start-page: 851 year: 1997 end-page: 861 ident: bib14 article-title: String matching in the DNA alphabet publication-title: Software Pract. Ex. – volume: 35 start-page: 173 year: 2005 end-page: 181 ident: bib24 article-title: On exact string matching of unique oligonucleotides publication-title: Comput. Biol. Med. – year: 2019 ident: bib9 article-title: Pattern matching for dna sequencing data using multiple bloom filters publication-title: BioMed Res. Int. – start-page: 100356 year: 2020 ident: bib27 article-title: Developing an ultra-efficient microsatellite discoverer to find structural differences between SARS-CoV-1 and Covid-19 publication-title: Informatics in Medicine Unlocked – volume: 7 start-page: 69614 year: 2019 end-page: 69637 ident: bib17 article-title: Exact string matching algorithms: survey, issues, and future research directions publication-title: IEEE Access – start-page: 1 year: 2015, March end-page: 6 ident: bib10 article-title: Clustering Oriented Hashing Based Multiple String Pattern Matching Algorithm. In 2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015] – volume: 108 start-page: 122 year: 2019 end-page: 132 ident: bib26 article-title: Document-level attention-based BiLSTM-CRF incorporating disease dictionary for disease named entity recognition publication-title: Comput. Biol. Med. – volume: 20 start-page: 762 year: 1977 end-page: 772 ident: bib20 article-title: A fast string searching algorithm publication-title: Commun. ACM – volume: 102 start-page: 229 year: 2007 end-page: 235 ident: bib13 article-title: Fast exact string matching algorithms publication-title: Inf. Process. Lett. – year: 1987 ident: bib11 article-title: Efficient randomized pattern-matching algorithms, the IBM journal of research and development – volume: 35 start-page: 717 year: 2005 end-page: 724 ident: bib25 article-title: Assessment of approximate string matching in a biomedical text retrieval problem publication-title: Comput. Biol. Med. – start-page: 62 year: 2004 ident: 10.1016/j.compbiomed.2021.104292_bib8 article-title: Compressed pattern matching in dna sequences – start-page: 44 year: 1999 ident: 10.1016/j.compbiomed.2021.104292_bib16 article-title: A fast multiple string-pattern matching algorithm – volume: 33 start-page: 132 issue: 8 year: 1990 ident: 10.1016/j.compbiomed.2021.104292_bib22 article-title: A very fast substring search algorithm publication-title: Commun. ACM doi: 10.1145/79173.79184 – volume: 3 start-page: 315 year: 2014 ident: 10.1016/j.compbiomed.2021.104292_bib3 article-title: Trends in genome compression, Current Bioinformatics, vol publication-title: 9, no – volume: 3 start-page: 72 year: 2013 ident: 10.1016/j.compbiomed.2021.104292_bib5 article-title: DNA lossless compression algorithms, American Journal of Bioinformatics Research, vol publication-title: 3, no – start-page: 100356 year: 2020 ident: 10.1016/j.compbiomed.2021.104292_bib27 article-title: Developing an ultra-efficient microsatellite discoverer to find structural differences between SARS-CoV-1 and Covid-19 publication-title: Informatics in Medicine Unlocked doi: 10.1016/j.imu.2020.100356 – ident: 10.1016/j.compbiomed.2021.104292_bib11 – volume: 6 start-page: 323 issue: 2 year: 1977 ident: 10.1016/j.compbiomed.2021.104292_bib19 article-title: Fast pattern matching in strings publication-title: SIAM J. Comput. doi: 10.1137/0206024 – volume: 35 start-page: 717 issue: 8 year: 2005 ident: 10.1016/j.compbiomed.2021.104292_bib25 article-title: Assessment of approximate string matching in a biomedical text retrieval problem publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2004.06.002 – volume: 34 start-page: 131 issue: 2 year: 2010 ident: 10.1016/j.compbiomed.2021.104292_bib6 article-title: An efficient similarity search based on indexing in large DNA databases publication-title: Comput. Biol. Chem. doi: 10.1016/j.compbiolchem.2010.03.007 – volume: 35 start-page: 173 issue: 2 year: 2005 ident: 10.1016/j.compbiomed.2021.104292_bib24 article-title: On exact string matching of unique oligonucleotides publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2003.11.003 – ident: 10.1016/j.compbiomed.2021.104292_bib29 – start-page: 1 year: 2016 ident: 10.1016/j.compbiomed.2021.104292_bib1 article-title: Disease diagnosis using pattern matching algorithm from DNA sequencing: a sequential and GPGPU based approach – start-page: 1 year: 2015 ident: 10.1016/j.compbiomed.2021.104292_bib10 – volume: 10 start-page: 501 issue: 6 year: 1980 ident: 10.1016/j.compbiomed.2021.104292_bib21 article-title: Practical fast searching in strings publication-title: Software Pract. Ex. doi: 10.1002/spe.4380100608 – year: 2019 ident: 10.1016/j.compbiomed.2021.104292_bib9 article-title: Pattern matching for dna sequencing data using multiple bloom filters publication-title: BioMed Res. Int. doi: 10.1155/2019/7074387 – start-page: 1 year: 1994 ident: 10.1016/j.compbiomed.2021.104292_bib12 – volume: 27 start-page: 851 issue: 7 year: 1997 ident: 10.1016/j.compbiomed.2021.104292_bib14 article-title: String matching in the DNA alphabet publication-title: Software Pract. Ex. doi: 10.1002/(SICI)1097-024X(199707)27:7<851::AID-SPE108>3.0.CO;2-D – volume: 7 start-page: 69614 year: 2019 ident: 10.1016/j.compbiomed.2021.104292_bib17 article-title: Exact string matching algorithms: survey, issues, and future research directions publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2914071 – volume: 102 start-page: 229 issue: 6 year: 2007 ident: 10.1016/j.compbiomed.2021.104292_bib13 article-title: Fast exact string matching algorithms publication-title: Inf. Process. Lett. doi: 10.1016/j.ipl.2007.01.002 – start-page: 65 year: 2016 ident: 10.1016/j.compbiomed.2021.104292_bib15 article-title: A very fast string matching algorithm based on condensed alphabets – volume: 45 start-page: 1 issue: 2 year: 2013 ident: 10.1016/j.compbiomed.2021.104292_bib18 article-title: The exact online string matching problem: a review of the most recent results publication-title: ACM Comput. Surv. doi: 10.1145/2431211.2431212 – volume: 20 start-page: 762 issue: 10 year: 1977 ident: 10.1016/j.compbiomed.2021.104292_bib20 article-title: A fast string searching algorithm publication-title: Commun. ACM doi: 10.1145/359842.359859 – ident: 10.1016/j.compbiomed.2021.104292_bib28 – volume: 8 issue: no. 1 year: 2013 ident: 10.1016/j.compbiomed.2021.104292_bib4 article-title: Data compression for sequencing data publication-title: Algorithm Mol. Biol. doi: 10.1186/1748-7188-8-25 – year: 2020 ident: 10.1016/j.compbiomed.2021.104292_bib7 article-title: Stretch Profile: a pruning technique to accelerate DNA sequence search publication-title: Informatics in Medicine Unlocked doi: 10.1016/j.imu.2020.100323 – start-page: 16 year: 1999 ident: 10.1016/j.compbiomed.2021.104292_bib23 article-title: A fast string matching algorithm and experimental results – volume: 108 start-page: 122 year: 2019 ident: 10.1016/j.compbiomed.2021.104292_bib26 article-title: Document-level attention-based BiLSTM-CRF incorporating disease dictionary for disease named entity recognition publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2019.04.002 – start-page: 172 year: 2015 ident: 10.1016/j.compbiomed.2021.104292_bib2 article-title: A new approach in pattern matching: codon Detection in DNA and RNA using hash function (CDDRHF)
SSID	ssj0004030
Score	2.3904524
Snippet	Exact string matching algorithms involve finding all occurrences of a pattern P in a text T. These algorithms have been extensively studied in computer... AbstractExact string matching algorithms involve finding all occurrences of a pattern P in a text T. These algorithms have been extensively studied in computer...
SourceID	proquest pubmed crossref elsevier
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	104292
SubjectTerms	Algorithms Collisions Computer applications Datasets Deoxyribonucleic acid Dictionaries DNA DNA Sequences E coli Gene sequencing Hash based algorithms Hash function Internal Medicine Nucleotide sequence Other Pattern matching Performance measurement Run time (computers) Sequence analysis String matching String matching algorithms
Title	Improving hash-q exact string matching algorithm with perfect hashing for DNA sequences
URI	https://www.clinicalkey.com/#!/content/1-s2.0-S001048252100086X https://www.clinicalkey.es/playcontent/1-s2.0-S001048252100086X https://dx.doi.org/10.1016/j.compbiomed.2021.104292 https://www.ncbi.nlm.nih.gov/pubmed/33662682 https://www.proquest.com/docview/2501855360 https://www.proquest.com/docview/2498502114
Volume	131
WOSCitedRecordID	wos000631619900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1879-0534 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004030 issn: 0010-4825 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1879-0534 dateEnd: 20231231 omitProxy: false ssIdentifier: ssj0004030 issn: 0010-4825 databaseCode: P5Z dateStart: 20030101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1879-0534 dateEnd: 20231231 omitProxy: false ssIdentifier: ssj0004030 issn: 0010-4825 databaseCode: M7P dateStart: 20030101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1879-0534 dateEnd: 20231231 omitProxy: false ssIdentifier: ssj0004030 issn: 0010-4825 databaseCode: K7- dateStart: 20030101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1879-0534 dateEnd: 20231231 omitProxy: false ssIdentifier: ssj0004030 issn: 0010-4825 databaseCode: 7X7 dateStart: 20030101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Nursing & Allied Health Database customDbUrl: eissn: 1879-0534 dateEnd: 20231231 omitProxy: false ssIdentifier: ssj0004030 issn: 0010-4825 databaseCode: 7RV dateStart: 20030101 isFulltext: true titleUrlDefault: https://search.proquest.com/nahs providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1879-0534 dateEnd: 20231231 omitProxy: false ssIdentifier: ssj0004030 issn: 0010-4825 databaseCode: BENPR dateStart: 20030101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Research Library customDbUrl: eissn: 1879-0534 dateEnd: 20231231 omitProxy: false ssIdentifier: ssj0004030 issn: 0010-4825 databaseCode: M2O dateStart: 20030101 isFulltext: true titleUrlDefault: https://search.proquest.com/pqrl providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELZoixAX3o9AWRmJq0Xix9oRB1SgFRJ0WRUoKy5W4gcLKrvbTYr4-YwdJ3uhaCUulvIYO8lMxjOezzMIPZNWOOuVI8YYSrhQntSly0mtfG4ULVwVa0aevpeTiZrNymlacGsSrLLXiVFR26UJa-TPacg8JwQb5y9X5yRUjQrR1VRCYwfthSwJNEL3ppt9kTnrtqCAruHgCiUkT4fvCpDtbos7eIm0CMFOWtLLpqfLzM84DR3d_N8XuIVuJAMUH3QScxtdcYs76NpxCrHfRV-GZQY8r5o5Ocfud2VaHOp7wDkwcCP6Eldn36D3dv4Th6VcvHLrAAyJNOEy2ML4zeQAD1Dte-jz0eGn129Jqr5ADBhRLfFcVao03PG6sDx3lubCibGRdS68B7Ot5NZ4z0TpuHfA24KDY86Ns6BDrVXsPtpdLBfuIcKVVYqFCK7ljDvvy7pmVrJaWAnykNMMyf6ja5NSk4cKGWe6x6D90Bt26cAu3bErQ8VAuerSc2xBU_Z81f32U1CYGuaQLWjl32hdk_78Rhe6oTrXH2PiI5A5WkS3cZahFwNlMm46o2XLcfd7odLDUBuJytDT4TKohxDzqRZueQH38FIJ6KfgGXrQCe7woRgbgzur6KN_d_4YXQ9P0sGV9tFuu75wT9BV86v93qxHaEeenIZ2JmOrRmjv1eFkegJH7ySB9ph-GMX_Etqp-PoHEWA8ig
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VgoAL70eggJHgGJH4sXGEEKooVavdrpAosDc38YMFld3tJuXxp_iNjOMke6FoLz1wjTPOw9-MZ-xvPADPMiOscdLGWmsacyFdXOY2iUvpEi1paoumZuTHUTYey8kkf7cBv7tcGE-r7GxiY6jNXPs18hfUnzwnBBskrxcnsa8a5XdXuxIaARZD--sHhmzVq_0dHN_nlO6-PXyzF7dVBWKNzkEdOy4LmWtueZkanlhDE2HFQGdlIpxDdyTnRjvHRG65s_jOKceAk2tr0DYYIxn2ewEuciYzr1fDLF7lYSYspLygbeMYerXMocAn8xTxkFKPUSlN_eYqzelZ0-FZ7m4z7e1e_99-2A241jrYZDtoxE3YsLNbcPmgpRDchk_9MgqZFtU0PiH2Z6Fr4uuX4DV04Bt2KSmOP-PX1NNvxC9Vk4VdeuJLI-Ob0dcnO-Nt0lPR78CHc_msu7A5m8_sfSCFkZL5HWrDGbfO5WXJTMZKYTLEe0IjyLpBVro9et1XADlWHcfuq1rBQ3l4qACPCNJechGOH1lDJu9wpLr0WpwQFM6Ra8hmf5O1VWvZKpWqiqpEvW8OdkKM07QJiycRvOwlW-ctOGVrPnerA7HqH7VCcARP-2Y0f35Pq5jZ-Snew3MpsJ-UR3AvKEr_oxgbYLgu6YN_d_4EruwdHozUaH88fAhX_VsFatYWbNbLU_sILunv9Zdq-bjRdgJH560tfwBUd5TF
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Zb9NAEB6VgipeuI9AgUWCR6v2HvFaCKGKEFG1RJG4Il629h6kVUnS2OX4a_w6Zr2280JRXvrAq9ez58zszO43OwDPUiOscdJGWmsacSFdVGQ2jgrpYi1pYvM6Z-Sng3Q0kpNJNt6A320sjIdVtjqxVtRmrv0Z-Q71L88JwfrxjmtgEePB8NXiNPIZpPxNa5tOI7DIvv31A9238uXeANf6OaXDNx9ev42aDAORRkOhihyXucw0t7xIDI-tobGwoq_TIhbOoWmScaOdYyKz3Fnsf8LR-eTaGtQTxkiG9V6Cyyn6mB5OOBZfVjGZMQvhL6jnOLphDYooYMs8XDyE16OHShN_0Uozet7WeJ7pW2-Bw-v_8-TdgGuN4U12g6TchA07uwVb7xpowW343B2vkGleTqNTYn_muiI-rwl-Q8O-Rp2S_OQrjqaafiP-CJss7NIDYmoaX4w-ABmMdkkHUb8DHy9kWHdhczaf2ftAciMl8zfXhjNuncuKgpmUFcKkKAcx7UHaLrjSzZPsPjPIiWqxd8dqxSrKs4oKrNKDpKNchGdJ1qDJWp5SbdgtbhQK9841aNO_0dqy0XilSlRJVaze1w8-Ib_TpHaXJz140VE2Rl0w1tZsd7tlaNU1teLmHjztilEt-ruufGbnZ_gPz6TAehLeg3tBaLqJYqyPbrykD_5d-RPYQiFRB3uj_Ydw1XcqILa2YbNantlHcEV_r47K5eNa8AkcXrSw_AHJtp3p
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Improving+hash-q+exact+string+matching+algorithm+with+perfect+hashing+for+DNA+sequences&rft.jtitle=Computers+in+biology+and+medicine&rft.au=Karcioglu%2C+Abdullah+Ammar&rft.au=Bulut%2C+Hasan&rft.date=2021-04-01&rft.issn=0010-4825&rft.volume=131&rft.spage=104292&rft_id=info:doi/10.1016%2Fj.compbiomed.2021.104292&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_compbiomed_2021_104292
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0010-4825&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0010-4825&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0010-4825&client=summon