Photosynthetic algorithm approaches for bioinformatics
Özet
Photosynthetic algorithm (PA) is the newly biosystem-derived heuristic search algorithm that utilizes the dark reaction rules governing the transfer of carbon molecules from one substance into another in the Calvin-Benson cycle and photorespiration. There are only two works using this new algorithm in the literature, one for finite element analysis and one for N-queen problem. This paper presents PA based novel solution techniques in bioinformatics problems such as aligning multiple sequences and discovering association rules within bio-medical data. The simulation results demonstrate the applicability and potential of this algorithm. (C) 2011 Elsevier Ltd. All rights reserved.
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RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/munzur/4696Resmi atıflarda kullanılan kalıcı adresİndeks kayıtları
Kaydın indeks veri tabanlarındaki sayfası (aktarımda alınan kayıt numarasıyla). Erişim, indeksin kendi koşullarına bağlıdır.Atıflar
- WoS
- 6
- OpenAlex
- 7
Tam Kayıt
| Alan | Değer | Dil |
|---|---|---|
| dc.contributor.author | Alatas, Bilal | |
| dc.date.accessioned | 2026-10-28T15:03:37.981747312Z | |
| dc.date.available | 2026-10-28T15:03:37.981747312Z | |
| dc.date.issued | 2011-08 | |
| dc.description.abstract | Photosynthetic algorithm (PA) is the newly biosystem-derived heuristic search algorithm that utilizes the dark reaction rules governing the transfer of carbon molecules from one substance into another in the Calvin-Benson cycle and photorespiration. There are only two works using this new algorithm in the literature, one for finite element analysis and one for N-queen problem. This paper presents PA based novel solution techniques in bioinformatics problems such as aligning multiple sequences and discovering association rules within bio-medical data. The simulation results demonstrate the applicability and potential of this algorithm. (C) 2011 Elsevier Ltd. All rights reserved. | |
| dc.description.sponsorship | Brazilian agency FAPESP [2008/10859-0] | |
| dc.identifier.doi | 10.1016/j.eswa.2011.02.102 | |
| dc.identifier.eissn | 1873-6793 | |
| dc.identifier.endpage | 10553 | |
| dc.identifier.issn | 0957-4174 | |
| dc.identifier.issue | 8 | |
| dc.identifier.startpage | 10541 | |
| dc.identifier.uri | https://rdlopen.rawdatalibrary.net/r/munzur/4696 | |
| dc.identifier.volume | 38 | |
| dc.identifier.wos | WOS:000290237500169 | |
| dc.language.iso | en | |
| dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | |
| dc.relation.ispartof | EXPERT SYSTEMS WITH APPLICATIONS | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.subject | Heuristics | |
| dc.subject | Photosynthetic algorithm | |
| dc.subject | Bioinformatics | |
| dc.subject | Multiple sequence alignment | |
| dc.subject | Association rules mining | |
| dc.subject.classification | Computer Science, Artificial Intelligence | |
| dc.subject.classification | Engineering, Electrical & Electronic | |
| dc.subject.classification | Operations Research & Management Science | |
| dc.subject.classification | Computer Science | |
| dc.subject.classification | Engineering | |
| dc.subject.other | FEATURE-SELECTION | |
| dc.subject.other | NEURAL-NETWORK | |
| dc.subject.other | CLASSIFICATION | |
| dc.subject.other | SIGNALS | |
| dc.title | Photosynthetic algorithm approaches for bioinformatics | |
| dc.type | Article | |
| rdl.citation.openalex | 7 | |
| rdl.citation.wos | 6 | |
| rdl.oa.status | closed | |
| rdl.scopus.quartile | Q1 | |
| rdl.source.index | WOS | |
| rdl.wos.index | Science Citation Index Expanded (SCI-EXPANDED) |
