Inference of biochemical network models in S-system using multiobjective optimization approach
Motivation: The inference of biochemical networks, such as gene regulatory networks, protein–protein interaction networks, and metabolic pathway networks, from time-course data is one of the main challenges in systems biology. The ultimate goal of inferred modeling is to obtain expressions that quan...
Uložené v:
| Vydané v: | Bioinformatics Ročník 24; číslo 8; s. 1085 - 1092 |
|---|---|
| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Oxford
Oxford University Press
15.04.2008
Oxford Publishing Limited (England) |
| Predmet: | |
| ISSN: | 1367-4803, 1367-4811, 1460-2059, 1367-4811 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Motivation: The inference of biochemical networks, such as gene regulatory networks, protein–protein interaction networks, and metabolic pathway networks, from time-course data is one of the main challenges in systems biology. The ultimate goal of inferred modeling is to obtain expressions that quantitatively understand every detail and principle of biological systems. To infer a realizable S-system structure, most articles have applied sums of magnitude of kinetic orders as a penalty term in the fitness evaluation. How to tune a penalty weight to yield a realizable model structure is the main issue for the inverse problem. No guideline has been published for tuning a suitable penalty weight to infer a suitable model structure of biochemical networks. Results: We introduce an interactive inference algorithm to infer a realizable S-system structure for biochemical networks. The inference problem is formulated as a multiobjective optimization problem to minimize simultaneously the concentration error, slope error and interaction measure in order to find a suitable S-system model structure and its corresponding model parameters. The multiobjective optimization problem is solved by the ε-constraint method to minimize the interaction measure subject to the expectation constraints for the concentration and slope error criteria. The theorems serve to guarantee the minimum solution for the ε-constrained problem to achieve the minimum interaction network for the inference problem. The approach could avoid assigning a penalty weight for sums of magnitude of kinetic orders. Contact: chmfsw@ccu.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online. |
|---|---|
| AbstractList | Motivation: The inference of biochemical networks, such as gene regulatory networks, protein–protein interaction networks, and metabolic pathway networks, from time-course data is one of the main challenges in systems biology. The ultimate goal of inferred modeling is to obtain expressions that quantitatively understand every detail and principle of biological systems. To infer a realizable S-system structure, most articles have applied sums of magnitude of kinetic orders as a penalty term in the fitness evaluation. How to tune a penalty weight to yield a realizable model structure is the main issue for the inverse problem. No guideline has been published for tuning a suitable penalty weight to infer a suitable model structure of biochemical networks. Results: We introduce an interactive inference algorithm to infer a realizable S-system structure for biochemical networks. The inference problem is formulated as a multiobjective optimization problem to minimize simultaneously the concentration error, slope error and interaction measure in order to find a suitable S-system model structure and its corresponding model parameters. The multiobjective optimization problem is solved by the ε-constraint method to minimize the interaction measure subject to the expectation constraints for the concentration and slope error criteria. The theorems serve to guarantee the minimum solution for the ε-constrained problem to achieve the minimum interaction network for the inference problem. The approach could avoid assigning a penalty weight for sums of magnitude of kinetic orders. Contact: chmfsw@ccu.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online. The inference of biochemical networks, such as gene regulatory networks, protein-protein interaction networks, and metabolic pathway networks, from time-course data is one of the main challenges in systems biology. The ultimate goal of inferred modeling is to obtain expressions that quantitatively understand every detail and principle of biological systems. To infer a realizable S-system structure, most articles have applied sums of magnitude of kinetic orders as a penalty term in the fitness evaluation. How to tune a penalty weight to yield a realizable model structure is the main issue for the inverse problem. No guideline has been published for tuning a suitable penalty weight to infer a suitable model structure of biochemical networks.MOTIVATIONThe inference of biochemical networks, such as gene regulatory networks, protein-protein interaction networks, and metabolic pathway networks, from time-course data is one of the main challenges in systems biology. The ultimate goal of inferred modeling is to obtain expressions that quantitatively understand every detail and principle of biological systems. To infer a realizable S-system structure, most articles have applied sums of magnitude of kinetic orders as a penalty term in the fitness evaluation. How to tune a penalty weight to yield a realizable model structure is the main issue for the inverse problem. No guideline has been published for tuning a suitable penalty weight to infer a suitable model structure of biochemical networks.We introduce an interactive inference algorithm to infer a realizable S-system structure for biochemical networks. The inference problem is formulated as a multiobjective optimization problem to minimize simultaneously the concentration error, slope error and interaction measure in order to find a suitable S-system model structure and its corresponding model parameters. The multiobjective optimization problem is solved by the epsilon-constraint method to minimize the interaction measure subject to the expectation constraints for the concentration and slope error criteria. The theorems serve to guarantee the minimum solution for the epsilon-constrained problem to achieve the minimum interaction network for the inference problem. The approach could avoid assigning a penalty weight for sums of magnitude of kinetic orders.RESULTSWe introduce an interactive inference algorithm to infer a realizable S-system structure for biochemical networks. The inference problem is formulated as a multiobjective optimization problem to minimize simultaneously the concentration error, slope error and interaction measure in order to find a suitable S-system model structure and its corresponding model parameters. The multiobjective optimization problem is solved by the epsilon-constraint method to minimize the interaction measure subject to the expectation constraints for the concentration and slope error criteria. The theorems serve to guarantee the minimum solution for the epsilon-constrained problem to achieve the minimum interaction network for the inference problem. The approach could avoid assigning a penalty weight for sums of magnitude of kinetic orders. Motivation: The inference of biochemical networks, such as gene regulatory networks, protein–protein interaction networks, and metabolic pathway networks, from time-course data is one of the main challenges in systems biology. The ultimate goal of inferred modeling is to obtain expressions that quantitatively understand every detail and principle of biological systems. To infer a realizable S-system structure, most articles have applied sums of magnitude of kinetic orders as a penalty term in the fitness evaluation. How to tune a penalty weight to yield a realizable model structure is the main issue for the inverse problem. No guideline has been published for tuning a suitable penalty weight to infer a suitable model structure of biochemical networks. Results: We introduce an interactive inference algorithm to infer a realizable S-system structure for biochemical networks. The inference problem is formulated as a multiobjective optimization problem to minimize simultaneously the concentration error, slope error and interaction measure in order to find a suitable S-system model structure and its corresponding model parameters. The multiobjective optimization problem is solved by the ε-constraint method to minimize the interaction measure subject to the expectation constraints for the concentration and slope error criteria. The theorems serve to guarantee the minimum solution for the ε-constrained problem to achieve the minimum interaction network for the inference problem. The approach could avoid assigning a penalty weight for sums of magnitude of kinetic orders. Contact: chmfsw@ccu.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online. Motivation: The inference of biochemical networks, such as gene regulatory networks, protein-protein interaction networks, and metabolic pathway networks, from time-course data is one of the main challenges in systems biology. The ultimate goal of inferred modeling is to obtain expressions that quantitatively understand every detail and principle of biological systems. To infer a realizable S-system structure, most articles have applied sums of magnitude of kinetic orders as a penalty term in the fitness evaluation. How to tune a penalty weight to yield a realizable model structure is the main issue for the inverse problem. No guideline has been published for tuning a suitable penalty weight to infer a suitable model structure of biochemical networks. Results: We introduce an interactive inference algorithm to infer a realizable S-system structure for biochemical networks. The inference problem is formulated as a multiobjective optimization problem to minimize simultaneously the concentration error, slope error and interaction measure in order to find a suitable S-system model structure and its corresponding model parameters. The multiobjective optimization problem is solved by the ε-constraint method to minimize the interaction measure subject to the expectation constraints for the concentration and slope error criteria. The theorems serve to guarantee the minimum solution for the ε-constrained problem to achieve the minimum interaction network for the inference problem. The approach could avoid assigning a penalty weight for sums of magnitude of kinetic orders. Contact: chmfsw@ccu.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online. The inference of biochemical networks, such as gene regulatory networks, protein-protein interaction networks, and metabolic pathway networks, from time-course data is one of the main challenges in systems biology. The ultimate goal of inferred modeling is to obtain expressions that quantitatively understand every detail and principle of biological systems. To infer a realizable S-system structure, most articles have applied sums of magnitude of kinetic orders as a penalty term in the fitness evaluation. How to tune a penalty weight to yield a realizable model structure is the main issue for the inverse problem. No guideline has been published for tuning a suitable penalty weight to infer a suitable model structure of biochemical networks. We introduce an interactive inference algorithm to infer a realizable S-system structure for biochemical networks. The inference problem is formulated as a multiobjective optimization problem to minimize simultaneously the concentration error, slope error and interaction measure in order to find a suitable S-system model structure and its corresponding model parameters. The multiobjective optimization problem is solved by the epsilon-constraint method to minimize the interaction measure subject to the expectation constraints for the concentration and slope error criteria. The theorems serve to guarantee the minimum solution for the epsilon-constrained problem to achieve the minimum interaction network for the inference problem. The approach could avoid assigning a penalty weight for sums of magnitude of kinetic orders. Motivation: The inference of biochemical networks, such as gene regulatory networks, protein-protein interaction networks, and metabolic pathway networks, from time-course data is one of the main challenges in systems biology. The ultimate goal of inferred modeling is to obtain expressions that quantitatively understand every detail and principle of biological systems. To infer a realizable S-system structure, most articles have applied sums of magnitude of kinetic orders as a penalty term in the fitness evaluation. How to tune a penalty weight to yield a realizable model structure is the main issue for the inverse problem. No guideline has been published for tuning a suitable penalty weight to infer a suitable model structure of biochemical networks. Results: We introduce an interactive inference algorithm to infer a realizable S-system structure for biochemical networks. The inference problem is formulated as a multiobjective optimization problem to minimize simultaneously the concentration error, slope error and interaction measure in order to find a suitable S-system model structure and its corresponding model parameters. The multiobjective optimization problem is solved by the [straight epsilon]-constraint method to minimize the interaction measure subject to the expectation constraints for the concentration and slope error criteria. The theorems serve to guarantee the minimum solution for the [straight epsilon]-constrained problem to achieve the minimum interaction network for the inference problem. The approach could avoid assigning a penalty weight for sums of magnitude of kinetic orders. Contact: chmfsw@ccu.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online. |
| Author | Wang, Feng-Sheng Liu, Pang-Kai |
| Author_xml | – sequence: 1 givenname: Pang-Kai surname: Liu fullname: Liu, Pang-Kai organization: Department of Chemical Engineering, National Chung Cheng University, Chiayi 621-02, Taiwan, ROC – sequence: 2 givenname: Feng-Sheng surname: Wang fullname: Wang, Feng-Sheng organization: To whom correspondence should be addressed |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=20281556$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/18321886$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkc1u1DAURi3UirYDjwCKkMourR3HsSNWqAKm0kgsAAmxwPJfqKexHWynUJ4etxlGohtY2Ytz7v10vxNw4IM3ADxD8AzBHp9LG6wfQnQiW5XOZfaQkkfgGLUdrBtI-oPyxx2tWwbxEThJaQshQW3bPgZHiOEGMdYdg6-XfjDReGWqMFRlqLoyzioxVt7kHyFeVy5oM6bK-upDnW5TNq6ak_XfKjeP2Qa5NSrbm6JP2Tr7q8QJvhLTFINQV0_A4SDGZJ7u3hX49PbNx4t1vXn_7vLi9aZWpEG57jAehOypRi0ZmBaUluQMY611I7UwhCGIpFZSwYZp3EqBNJPUiL7TkrEGr8DLZW5Z-302KXNnkzLjKLwJc-IUEoibsmYFXjwAt2GOvmTjqGdd2_b3057voFk6o_kUrRPxlv85WwFOd4BI5VZDFF7ZtOeakhIRcse9WjgVQ0rRDFzZfH-hHIUdOYL8rkz-d5l8KbPY5IG9D_IPDy5emKf_VupFsaXgn3tJxGveUUwJX3_-wjekkJSteYd_A0XXzIY |
| CODEN | BOINFP |
| CitedBy_id | crossref_primary_10_1016_j_mbs_2009_03_002 crossref_primary_10_1016_j_ymeth_2016_08_001 crossref_primary_10_1109_TEVC_2012_2218610 crossref_primary_10_1002_wsbm_1391 crossref_primary_10_1016_j_jtice_2017_10_015 crossref_primary_10_4137_EBO_S8123 crossref_primary_10_3389_fbioe_2014_00062 crossref_primary_10_3389_fgene_2019_00549 crossref_primary_10_1155_2013_897658 crossref_primary_10_1186_1471_2105_15_S6_S1 crossref_primary_10_1186_1752_0509_3_5 crossref_primary_10_7717_peerj_9065 crossref_primary_10_1109_TCBB_2012_56 crossref_primary_10_1016_j_compbiolchem_2014_09_003 crossref_primary_10_1186_1752_0509_5_52 crossref_primary_10_1186_1471_2105_10_140 crossref_primary_10_1186_1752_0509_6_119 crossref_primary_10_1016_j_mbs_2013_07_019 crossref_primary_10_1093_bioinformatics_btp050 crossref_primary_10_1093_bioinformatics_btp072 crossref_primary_10_1186_1752_0509_3_94 crossref_primary_10_1016_j_mbs_2013_11_002 crossref_primary_10_1109_TCBB_2011_63 crossref_primary_10_1016_j_jtice_2011_06_007 crossref_primary_10_1016_j_jtice_2009_05_010 crossref_primary_10_1109_TCBB_2019_2892450 crossref_primary_10_1007_s40295_015_0059_8 crossref_primary_10_1186_1471_2105_15_256 crossref_primary_10_1016_j_compchemeng_2014_04_003 crossref_primary_10_1186_1752_0509_4_16 crossref_primary_10_1186_1752_0509_3_47 crossref_primary_10_1371_journal_pone_0083308 crossref_primary_10_1016_j_mbs_2011_11_008 crossref_primary_10_3389_fgene_2022_888786 crossref_primary_10_1002_ceat_200900513 crossref_primary_10_1016_j_compbiolchem_2019_05_003 crossref_primary_10_1007_s10489_020_01891_1 crossref_primary_10_1109_TCBB_2013_19 crossref_primary_10_1016_j_mbs_2013_01_004 crossref_primary_10_1016_j_ymben_2019_08_005 crossref_primary_10_1016_j_ymeth_2013_05_013 crossref_primary_10_1016_j_compchemeng_2009_05_008 crossref_primary_10_1016_j_eswa_2014_11_039 crossref_primary_10_1155_2017_3020326 crossref_primary_10_1109_TFUZZ_2012_2187212 crossref_primary_10_1089_cmb_2011_0269 crossref_primary_10_1109_TCBB_2015_2459686 crossref_primary_10_1016_j_mbs_2016_02_014 |
| Cites_doi | 10.1016/S0009-2509(00)00038-5 10.1016/S0098-1354(96)00362-6 10.1093/bioinformatics/bti099 10.1093/bioinformatics/btg027 10.1186/1742-4682-3-4 10.1093/bioinformatics/bti071 10.1007/978-1-4899-1633-4_4 10.1186/1471-2105-6-44 10.1016/S0098-1354(99)00290-2 10.1109/TCBB.2007.070203 10.1093/bioinformatics/btl522 10.1006/jmbi.1996.0011 10.1093/bioinformatics/bth140 10.1186/1742-4682-3-25 10.1049/sb:20045005 10.1101/gr.1262503 10.1109/CEC.2005.1554750 10.1093/bioinformatics/14.10.869 |
| ContentType | Journal Article |
| Copyright | 2008 The Author(s) 2008 2008 INIST-CNRS 2008 The Author(s) |
| Copyright_xml | – notice: 2008 The Author(s) 2008 – notice: 2008 INIST-CNRS – notice: 2008 The Author(s) |
| DBID | BSCLL TOX AAYXX CITATION IQODW CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TM 7TO 7U5 8BQ 8FD F28 FR3 H8D H8G H94 JG9 JQ2 K9. KR7 L7M L~C L~D P64 7X8 |
| DOI | 10.1093/bioinformatics/btn075 |
| DatabaseName | Istex Oxford Journals Open Access Collection CrossRef Pascal-Francis Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Nucleic Acids Abstracts Oncogenes and Growth Factors Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Copper Technical Reference Library AIDS and Cancer Research Abstracts Materials Research Database ProQuest Computer Science Collection ProQuest Health & Medical Complete (Alumni) Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Materials Research Database Oncogenes and Growth Factors Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Nucleic Acids Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Health & Medical Complete (Alumni) Materials Business File Aerospace Database Copper Technical Reference Library Engineered Materials Abstracts Biotechnology Research Abstracts AIDS and Cancer Research Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Civil Engineering Abstracts Aluminium Industry Abstracts Electronics & Communications Abstracts Ceramic Abstracts METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Solid State and Superconductivity Abstracts Engineering Research Database Corrosion Abstracts MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic CrossRef MEDLINE Materials Research Database |
| 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: TOX name: Oxford Journals Open Access Collection url: https://academic.oup.com/journals/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1460-2059 1367-4811 |
| EndPage | 1092 |
| ExternalDocumentID | 1464265511 18321886 20281556 10_1093_bioinformatics_btn075 10.1093/bioinformatics/btn075 ark_67375_HXZ_L575378H_6 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GroupedDBID | -~X .2P .I3 482 48X 5GY AAMVS ABGNP ABJNI ABPTD ACGFS ACUFI ADZXQ ALMA_UNASSIGNED_HOLDINGS BSCLL CZ4 EE~ F5P F9B H5~ HAR HW0 IOX KSI KSN NGC Q5Y RD5 ROZ RXO TLC TN5 TOX WH7 ~91 ADRIX BCRHZ KOP ROX --- -E4 .DC 0R~ 1TH 23N 2WC 4.4 53G 5WA 70D AAIJN AAIMJ AAJKP AAJQQ AAKPC AAMDB AAOGV AAPQZ AAPXW AAUQX AAVAP AAVLN AAYXX ABEJV ABEUO ABIXL ABNGD ABNKS ABPQP ABQLI ABWST ABXVV ABZBJ ACIWK ACPRK ACUKT ACUXJ ACYTK ADBBV ADEYI ADEZT ADFTL ADGKP ADGZP ADHKW ADHZD ADMLS ADOCK ADPDF ADRDM ADRTK ADVEK ADYVW ADZTZ AECKG AEGPL AEJOX AEKKA AEKSI AELWJ AEMDU AENEX AENZO AEPUE AETBJ AEWNT AFFNX AFFZL AFGWE AFIYH AFOFC AFRAH AGINJ AGKEF AGQPQ AGQXC AGSYK AHMBA AHXPO AIJHB AJEEA AJEUX AKHUL AKWXX ALTZX ALUQC AMNDL APIBT APWMN ARIXL ASPBG AVWKF AXUDD AYOIW AZFZN AZVOD BAWUL BAYMD BHONS BQDIO BQUQU BSWAC BTQHN C1A C45 CAG CDBKE CITATION COF CS3 DAKXR DIK DILTD DU5 D~K EBD EBS EJD EMOBN FEDTE FHSFR FLIZI FLUFQ FOEOM FQBLK GAUVT GJXCC GROUPED_DOAJ GX1 H13 HVGLF HZ~ J21 JXSIZ KAQDR KQ8 M-Z MK~ ML0 N9A NLBLG NMDNZ NOMLY NU- NVLIB O0~ O9- OAWHX ODMLO OJQWA OK1 OVD OVEED P2P PAFKI PB- PEELM PQQKQ Q1. R44 RNS ROL RPM RUSNO RW1 SV3 TEORI TJP TR2 W8F WOQ X7H YAYTL YKOAZ YXANX ZKX ~KM .-4 .GJ ABEFU AI. AQDSO ATTQO ELUNK IQODW NTWIH O~Y RIG RNI RZF RZO VH1 ZGI CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TM 7TO 7U5 8BQ 8FD F28 FR3 H8D H8G H94 JG9 JQ2 K9. KR7 L7M L~C L~D P64 7X8 |
| ID | FETCH-LOGICAL-c521t-633fab97d145f8da77367833ddd2bdae58101bdcbc028d34ba1d8b7ea96db8823 |
| IEDL.DBID | TOX |
| ISICitedReferencesCount | 66 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000254878500008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1367-4803 1367-4811 |
| IngestDate | Thu Oct 02 07:48:41 EDT 2025 Fri Oct 03 09:31:43 EDT 2025 Mon Jul 21 05:43:20 EDT 2025 Mon Jul 21 09:11:48 EDT 2025 Sat Nov 29 05:33:35 EST 2025 Tue Nov 18 21:52:33 EST 2025 Wed Aug 28 03:24:15 EDT 2024 Sat Sep 20 11:01:52 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 8 |
| Keywords | Inference Models Bioinformatics Optimization Network |
| Language | English |
| License | http://creativecommons.org/licenses/by-nc/2.0/uk CC BY 4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c521t-633fab97d145f8da77367833ddd2bdae58101bdcbc028d34ba1d8b7ea96db8823 |
| Notes | istex:4F808BF6F597ED9C5A817F32CBDA8068203D192D To whom correspondence should be addressed. ArticleID:btn075 ark:/67375/HXZ-L575378H-6 Associate Editor: John Quackenbush ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://dx.doi.org/10.1093/bioinformatics/btn075 |
| PMID | 18321886 |
| PQID | 198644982 |
| PQPubID | 36124 |
| PageCount | 8 |
| ParticipantIDs | proquest_miscellaneous_70503263 proquest_journals_198644982 pubmed_primary_18321886 pascalfrancis_primary_20281556 crossref_citationtrail_10_1093_bioinformatics_btn075 crossref_primary_10_1093_bioinformatics_btn075 oup_primary_10_1093_bioinformatics_btn075 istex_primary_ark_67375_HXZ_L575378H_6 |
| PublicationCentury | 2000 |
| PublicationDate | 2008-04-15 |
| PublicationDateYYYYMMDD | 2008-04-15 |
| PublicationDate_xml | – month: 04 year: 2008 text: 2008-04-15 day: 15 |
| PublicationDecade | 2000 |
| PublicationPlace | Oxford |
| PublicationPlace_xml | – name: Oxford – name: England |
| PublicationTitle | Bioinformatics |
| PublicationTitleAlternate | Bioinformatics |
| PublicationYear | 2008 |
| Publisher | Oxford University Press Oxford Publishing Limited (England) |
| Publisher_xml | – name: Oxford University Press – name: Oxford Publishing Limited (England) |
| References | Almeida (2023020210015678600_B1) 2003; 14 Ingalls (2023020210015678600_B12) 2004; 1 Bazaraa (2023020210015678600_B2) 1979 Mendes (2023020210015678600_B18) 1998; 14 Voit (2023020210015678600_B26) 2004; 20 Wang (2023020210015678600_B28) 2000; 55 Kimura (2023020210015678600_B14) 2004; 4 Wang (2023020210015678600_B27) 2000; 116 Noman (2023020210015678600_B20) 2005; 16 Gonzalez (2023020210015678600_B7) 2007; 23 Sakawa (2023020210015678600_B22) 1993 Ho (2023020210015678600_B10) 2005; 1 Chang (2023020210015678600_B3) 2005; 6 Polisetty (2023020210015678600_B21) 2006; 3 Voit (2023020210015678600_B24) 2000 Huang (2023020210015678600_B11) 2006 Moles (2023020210015678600_B19) 2003; 13 Hlavacek (2023020210015678600_B9) 1996; 255 Tsai (2023020210015678600_B23) 2005; 21 Edwards (2023020210015678600_B6) 1998; 22 Wang (2023020210015678600_B29) 2001; 40 Chiou (2023020210015678600_B4) 1999; 23 Handl (2023020210015678600_B8) 2007; 4 Kikuchi (2023020210015678600_B13) 2003; 19 Maki (2023020210015678600_B16) 2001; 6 Chou (2023020210015678600_B5) 2006; 3 Kimura (2023020210015678600_B15) 2005; 21 Maki (2023020210015678600_B17) 2002; 13 Voit (2023020210015678600_B25) 2003 |
| References_xml | – volume: 14 start-page: 114 year: 2003 ident: 2023020210015678600_B1 article-title: Neural-network-based parameter estimation in S-system models of biological networks publication-title: Genome Inform. – volume: 55 start-page: 3685 year: 2000 ident: 2023020210015678600_B28 article-title: Multiobjective parameter estimation problems of fermentation processes using a high ethanol tolerance yeast publication-title: Chem. Eng. Sci. doi: 10.1016/S0009-2509(00)00038-5 – volume: 22 start-page: 239 year: 1998 ident: 2023020210015678600_B6 article-title: Kinetic model reduction using genetic algorithms publication-title: Comput. Chem. Eng. doi: 10.1016/S0098-1354(96)00362-6 – volume: 21 start-page: 1180 year: 2005 ident: 2023020210015678600_B23 article-title: Evolutionary optimization with data collocation for reverse engineering of biological networks, publication-title: Bioinformatics doi: 10.1093/bioinformatics/bti099 – start-page: 9 year: 2006 ident: 2023020210015678600_B11 article-title: Reverse engineering for embryonic gene regulatory network in zebrafish via evolutionary optimization with data collocation – volume: 19 start-page: 643 year: 2003 ident: 2023020210015678600_B13 article-title: Dynamic modeling of genetic algorithm and S-system publication-title: Bioinformatics doi: 10.1093/bioinformatics/btg027 – volume: 3 start-page: 1 year: 2006 ident: 2023020210015678600_B21 article-title: Identification of metabolic system parameters using global optimization methods, publication-title: Theor. Biol. Med. Model. doi: 10.1186/1742-4682-3-4 – volume: 4 start-page: 1 year: 2004 ident: 2023020210015678600_B14 article-title: Inference of S-system models of genetic networks from noisy time-series data publication-title: Chem-BioInformatics J. – volume: 21 start-page: 1154 year: 2005 ident: 2023020210015678600_B15 article-title: Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm publication-title: Bioinformatics doi: 10.1093/bioinformatics/bti071 – start-page: 91 volume-title: Fuzzy Sets and Interactive Multiobjective Optimization year: 1993 ident: 2023020210015678600_B22 doi: 10.1007/978-1-4899-1633-4_4 – volume: 6 start-page: 1 year: 2005 ident: 2023020210015678600_B3 article-title: Quantitative inference of dynamic regulatory pathways via microarray data publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-6-44 – volume: 40 start-page: 2876 year: 2001 ident: 2023020210015678600_B29 article-title: Hybrid differential evolution for problems of kinetic parameter estimation and dynamic optimization of an ethanol fermentation process publication-title: Chem. Eng. Sci. – volume: 23 start-page: 1277 year: 1999 ident: 2023020210015678600_B4 article-title: Hybrid method of evolution algorithms for static and dynamic optimization problems with application to a fedbatch fermentation process publication-title: Comput. Chem. Eng. doi: 10.1016/S0098-1354(99)00290-2 – volume: 116 start-page: 257 year: 2000 ident: 2023020210015678600_B27 article-title: A modified collocation method for solving differential-algebraic equations publication-title: Appl. Math. Comput. – start-page: 37 volume-title: Computational analysis of biochemical systems year: 2000 ident: 2023020210015678600_B24 – volume: 4 start-page: 279 year: 2007 ident: 2023020210015678600_B8 article-title: Multiobjective optimization in bioinformatics and computational biology publication-title: IEEE/ACM Trans. Comput. Biol. Bioinform. doi: 10.1109/TCBB.2007.070203 – volume: 13 start-page: 382 year: 2002 ident: 2023020210015678600_B17 article-title: Inference of genetic network using the expression profile time course data of mouse P19 cells publication-title: Chem-BioInformatics J. – volume: 23 start-page: 480 year: 2007 ident: 2023020210015678600_B7 article-title: Parameter estimation using simulated annealing for S-system models of biochemical networks publication-title: Bioinformatics doi: 10.1093/bioinformatics/btl522 – volume: 255 start-page: 121 year: 1996 ident: 2023020210015678600_B9 article-title: Rules for coupled expression of regulator and effector genes in inducible circuits publication-title: J. Mol. Biol. doi: 10.1006/jmbi.1996.0011 – volume: 20 start-page: 1670 year: 2004 ident: 2023020210015678600_B26 article-title: Decoupling dynamic systems for pathway identification from metabolic profiles publication-title: Bioinformatics doi: 10.1093/bioinformatics/bth140 – start-page: 125 volume-title: Metabolite Profiling: Its Role in Biomarker Discovery and Gene Function Analysis year: 2003 ident: 2023020210015678600_B25 article-title: Dynamic profiling and canonical modeling: powerful partners in metabolic pathway identification – volume: 3 start-page: 1 year: 2006 ident: 2023020210015678600_B5 article-title: Parameter estimation in biochemical systems models with alternating regression publication-title: Theor. Biol. Med. Model. doi: 10.1186/1742-4682-3-25 – start-page: 336 volume-title: Nonlinear Programming. Theory and Algorithms year: 1979 ident: 2023020210015678600_B2 – volume: 1 start-page: 62 year: 2004 ident: 2023020210015678600_B12 article-title: Autonomously oscillating biochemical systems: parametric sensi-tivities of extrema and period publication-title: IEE Syst. Biol. doi: 10.1049/sb:20045005 – volume: 13 start-page: 2467 year: 2003 ident: 2023020210015678600_B19 article-title: Parameter estimation in biochemical pathways: a comparison of global optimization methods publication-title: Genome Res. doi: 10.1101/gr.1262503 – volume: 6 start-page: 446 year: 2001 ident: 2023020210015678600_B16 article-title: Development of a system for the inference of large scale genetic networks publication-title: Pac. Symp. Biocomput. – volume: 1 start-page: 691 year: 2005 ident: 2023020210015678600_B10 article-title: Evolutionary divide-and-conquer approach to inferring S-system models of genetic networks publication-title: Proceedings of the 2005 IEEE Congress on Evolutionary Computation. doi: 10.1109/CEC.2005.1554750 – volume: 16 start-page: 205 year: 2005 ident: 2023020210015678600_B20 article-title: Reverse engineering genetic networks using evolutionary computation publication-title: Genome Inform. – volume: 14 start-page: 869 year: 1998 ident: 2023020210015678600_B18 article-title: Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation, publication-title: Bioinformatics doi: 10.1093/bioinformatics/14.10.869 |
| SSID | ssj0051444 ssj0005056 |
| Score | 2.2202728 |
| Snippet | Motivation: The inference of biochemical networks, such as gene regulatory networks, protein–protein interaction networks, and metabolic pathway networks, from... Motivation: The inference of biochemical networks, such as gene regulatory networks, protein-protein interaction networks, and metabolic pathway networks, from... The inference of biochemical networks, such as gene regulatory networks, protein-protein interaction networks, and metabolic pathway networks, from time-course... |
| SourceID | proquest pubmed pascalfrancis crossref oup istex |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1085 |
| SubjectTerms | Algorithms Bioinformatics Biological and medical sciences Computer Simulation Fundamental and applied biological sciences. Psychology Gene Expression Profiling - methods General aspects Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Models, Biological Models, Statistical Protein Interaction Mapping - methods Proteome - metabolism Signal Transduction - physiology Software |
| Title | Inference of biochemical network models in S-system using multiobjective optimization approach |
| URI | https://api.istex.fr/ark:/67375/HXZ-L575378H-6/fulltext.pdf https://www.ncbi.nlm.nih.gov/pubmed/18321886 https://www.proquest.com/docview/198644982 https://www.proquest.com/docview/70503263 |
| Volume | 24 |
| WOSCitedRecordID | wos000254878500008&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: PRVASL databaseName: Oxford Journals Open Access Collection customDbUrl: eissn: 1460-2059 dateEnd: 20220930 omitProxy: false ssIdentifier: ssj0005056 issn: 1367-4803 databaseCode: TOX dateStart: 19850101 isFulltext: true titleUrlDefault: https://academic.oup.com/journals/ providerName: Oxford University Press – providerCode: PRVASL databaseName: Oxford Journals Open Access Collection customDbUrl: eissn: 1460-2059 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005056 issn: 1367-4803 databaseCode: TOX dateStart: 19850101 isFulltext: true titleUrlDefault: https://academic.oup.com/journals/ providerName: Oxford University Press |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bi9QwFD6sq4Ig3i91dcyDCD7EnTZpkzyKuIywrIIrFB8MuTQyXtplOiv67z1p2llGWHQfS5tD--Xk5JSc830Az4JQVWkVpyYIR3lTWWosK6m0Bn_EHHMhHxqFD8XRkaxr9X4H5lMvzN9H-Irt22U3kohG4uJ9u25xm8Ogm5cyOvbxu_qspmMemWHSBWYCPEnaRmZvOWdT_855Jrd2pssR5F9T19v1E9MjZCFpXZyfjA6b0sHNi3_OLbgxJqDkVfKY27DTtHfgapKk_H0XPr-dOgBJFwhacSOjAGlTwTgZtHN6smzJB5p4oEksnv9ChtrEzn5NIZR0GIx-jF2eZKIuvwcfD94cv17QUYOBuih1QCvGgrFK-JyXQXojBMIpGfPeF9abpowEYdY76zBR8Yxbk3tpRWNU5S1m7-w-7LZd2zwE4vIi-CqSC8mGB5_Hdi-LZrxTKihmMuAT_tqNBOVRJ-O7TgflTG9DpxN0GbzcDDtJDB3_GvB8mNzN02b1LZa3iVIv6k_6EBNYJuRCVxm8wNn_X6OzLR_ZjCoQF8zZ0Nje5DR6DBO9ziM5PleyyODp5i6u73hoY9qmO-21iIQ9RcUyeJA87ex9osiUlNWjC7zmHlxLBS-c5uVj2F2vTpsncMX9XC_71QwuiVrOhiX1B5GvJ-M |
| linkProvider | Oxford University Press |
| 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=Inference+of+biochemical+network+models+in+S-system+using+multiobjective+optimization+approach&rft.jtitle=Bioinformatics&rft.au=Liu%2C+Pang-Kai&rft.au=Wang%2C+Feng-Sheng&rft.date=2008-04-15&rft.pub=Oxford+University+Press&rft.issn=1367-4803&rft.eissn=1460-2059&rft.volume=24&rft.issue=8&rft.spage=1085&rft.epage=1092&rft_id=info:doi/10.1093%2Fbioinformatics%2Fbtn075&rft.externalDocID=10.1093%2Fbioinformatics%2Fbtn075 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1367-4803&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1367-4803&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1367-4803&client=summon |