Combination of Neuro-Fuzzy Network Models with Biological Knowledge for Reconstructing Gene Regulatory Networks
Inferring gene regulatory networks from large-scale expression data is an important topic in both cellular systems and computational biology. The inference of regulators might be the core factor for understanding actual regulatory conditions in gene regulatory networks, especially when strong regula...
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| Veröffentlicht in: | Journal of Bionic Engineering 2011-03, Vol.8 (1), p.98-106 |
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| creator | Liu, Guixia Liu, Lei Liu, Chunyu Zheng, Ming Su, Lanying Zhou, Chunguang |
| description | Inferring gene regulatory networks from large-scale expression data is an important topic in both cellular systems and computational biology. The inference of regulators might be the core factor for understanding actual regulatory conditions in gene regulatory networks, especially when strong regulators do work significantly. In this paper, we propose a novel approach based on combining neuro-fuzzy network models with biological knowledge to infer strong regulators and interrelated fuzzy rules. The hybrid neuro-fuzzy architecture can not only infer the fuzzy rules, which are suitable for describing the regulatory conditions in regulatory networks, but also explain the meaning of nodes and weight value in the neural network. It can get useful rules automatically without factitious judgments. At the same time, it does not add recursive layers to the model, and the model can also strengthen the relationships among genes and reduce calculation. We use the proposed approach to reconstruct a partial gene regulatory network of yeast. The results show that this approach can work effectively. |
| doi_str_mv | 10.1016/S1672-6529(11)60008-5 |
| format | Article |
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The inference of regulators might be the core factor for understanding actual regulatory conditions in gene regulatory networks, especially when strong regulators do work significantly. In this paper, we propose a novel approach based on combining neuro-fuzzy network models with biological knowledge to infer strong regulators and interrelated fuzzy rules. The hybrid neuro-fuzzy architecture can not only infer the fuzzy rules, which are suitable for describing the regulatory conditions in regulatory networks, but also explain the meaning of nodes and weight value in the neural network. It can get useful rules automatically without factitious judgments. At the same time, it does not add recursive layers to the model, and the model can also strengthen the relationships among genes and reduce calculation. We use the proposed approach to reconstruct a partial gene regulatory network of yeast. The results show that this approach can work effectively.</description><identifier>ISSN: 1672-6529</identifier><identifier>EISSN: 2543-2141</identifier><identifier>DOI: 10.1016/S1672-6529(11)60008-5</identifier><language>eng</language><publisher>Singapore: Elsevier Ltd</publisher><subject>Analysis ; Artificial Intelligence ; Biochemical Engineering ; Bioinformatics ; biological knowledge ; Biomaterials ; Biomedical Engineering and Bioengineering ; Biomedical Engineering/Biotechnology ; Engineering ; gene regulatory networks ; Genes ; Neural networks ; neuro-fuzzy network ; regulators ; 基因表达数据 ; 基因调控网络 ; 模糊神经网络模型 ; 模糊规则 ; 生物学知识 ; 监管机构 ; 蜂窝系统 ; 计算生物学</subject><ispartof>Journal of Bionic Engineering, 2011-03, Vol.8 (1), p.98-106</ispartof><rights>2011 Jilin University</rights><rights>Jilin University 2011</rights><rights>COPYRIGHT 2011 Springer</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c454t-88f87209f13d26c4b1e95e57a3483dea0eb81d63bb04e1e5a9793609163ff0303</citedby><cites>FETCH-LOGICAL-c454t-88f87209f13d26c4b1e95e57a3483dea0eb81d63bb04e1e5a9793609163ff0303</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://image.cqvip.com/vip1000/qk/87903X/87903X.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1016/S1672-6529(11)60008-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1672652911600085$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,41464,42533,51294,65306</link.rule.ids></links><search><creatorcontrib>Liu, Guixia</creatorcontrib><creatorcontrib>Liu, Lei</creatorcontrib><creatorcontrib>Liu, Chunyu</creatorcontrib><creatorcontrib>Zheng, Ming</creatorcontrib><creatorcontrib>Su, Lanying</creatorcontrib><creatorcontrib>Zhou, Chunguang</creatorcontrib><title>Combination of Neuro-Fuzzy Network Models with Biological Knowledge for Reconstructing Gene Regulatory Networks</title><title>Journal of Bionic Engineering</title><addtitle>J Bionic Eng</addtitle><addtitle>Journal of Bionics Engineering</addtitle><description>Inferring gene regulatory networks from large-scale expression data is an important topic in both cellular systems and computational biology. The inference of regulators might be the core factor for understanding actual regulatory conditions in gene regulatory networks, especially when strong regulators do work significantly. In this paper, we propose a novel approach based on combining neuro-fuzzy network models with biological knowledge to infer strong regulators and interrelated fuzzy rules. The hybrid neuro-fuzzy architecture can not only infer the fuzzy rules, which are suitable for describing the regulatory conditions in regulatory networks, but also explain the meaning of nodes and weight value in the neural network. It can get useful rules automatically without factitious judgments. At the same time, it does not add recursive layers to the model, and the model can also strengthen the relationships among genes and reduce calculation. We use the proposed approach to reconstruct a partial gene regulatory network of yeast. The results show that this approach can work effectively.</description><subject>Analysis</subject><subject>Artificial Intelligence</subject><subject>Biochemical Engineering</subject><subject>Bioinformatics</subject><subject>biological knowledge</subject><subject>Biomaterials</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Biomedical Engineering/Biotechnology</subject><subject>Engineering</subject><subject>gene regulatory networks</subject><subject>Genes</subject><subject>Neural networks</subject><subject>neuro-fuzzy network</subject><subject>regulators</subject><subject>基因表达数据</subject><subject>基因调控网络</subject><subject>模糊神经网络模型</subject><subject>模糊规则</subject><subject>生物学知识</subject><subject>监管机构</subject><subject>蜂窝系统</subject><subject>计算生物学</subject><issn>1672-6529</issn><issn>2543-2141</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNqFkU1v1DAQhiMEEkvhJyBFXIBDih1_xDmhsqIFUUDi42w5zjh1m_W0tsOq_fV4d6teax88Gr3PjGfeqnpNyTElVH74TWXXNlK0_TtK30tCiGrEk2rVCs6alnL6tFo9SJ5XL1K6JET0rWKrCte4GXww2WOo0dU_YInYnC53d7clzluMV_V3HGFO9dbni_qTxxknb81cfwu4nWGcoHYY619gMaQcF5t9mOozCFBy0zKbjPGhVnpZPXNmTvDq_j2q_p5-_rP-0pz_PPu6PjlvLBc8N0o51bWkd5SNrbR8oNALEJ1hXLERDIFB0VGyYSAcKAjTdz2TpKeSOUcYYUfV20Pd64g3C6SsNz5ZmGcTAJeklewVp1KIojw-KCczg_bBYY7GljvCxpeZwPmSP-moKEcpXgBxAGzElCI4fR39xsRbTYneGaL3hujdtjWlem-I3jWSBy4VfZgg6ktcYihbeBT8eACLC_DPFzBZD8HC6CPYrEf0j1Z4c__lCwzTTemuB2Ov9pOxru05VYT9B7fHr2Q</recordid><startdate>20110301</startdate><enddate>20110301</enddate><creator>Liu, Guixia</creator><creator>Liu, Lei</creator><creator>Liu, Chunyu</creator><creator>Zheng, Ming</creator><creator>Su, Lanying</creator><creator>Zhou, Chunguang</creator><general>Elsevier Ltd</general><general>Springer Singapore</general><general>Springer</general><scope>2RA</scope><scope>92L</scope><scope>CQIGP</scope><scope>W92</scope><scope>~WA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IAO</scope><scope>7QO</scope><scope>7TM</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope></search><sort><creationdate>20110301</creationdate><title>Combination of Neuro-Fuzzy Network Models with Biological Knowledge for Reconstructing Gene Regulatory Networks</title><author>Liu, Guixia ; Liu, Lei ; Liu, Chunyu ; Zheng, Ming ; Su, Lanying ; Zhou, Chunguang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c454t-88f87209f13d26c4b1e95e57a3483dea0eb81d63bb04e1e5a9793609163ff0303</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Analysis</topic><topic>Artificial Intelligence</topic><topic>Biochemical Engineering</topic><topic>Bioinformatics</topic><topic>biological knowledge</topic><topic>Biomaterials</topic><topic>Biomedical Engineering and Bioengineering</topic><topic>Biomedical Engineering/Biotechnology</topic><topic>Engineering</topic><topic>gene regulatory networks</topic><topic>Genes</topic><topic>Neural networks</topic><topic>neuro-fuzzy network</topic><topic>regulators</topic><topic>基因表达数据</topic><topic>基因调控网络</topic><topic>模糊神经网络模型</topic><topic>模糊规则</topic><topic>生物学知识</topic><topic>监管机构</topic><topic>蜂窝系统</topic><topic>计算生物学</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Guixia</creatorcontrib><creatorcontrib>Liu, Lei</creatorcontrib><creatorcontrib>Liu, Chunyu</creatorcontrib><creatorcontrib>Zheng, Ming</creatorcontrib><creatorcontrib>Su, Lanying</creatorcontrib><creatorcontrib>Zhou, Chunguang</creatorcontrib><collection>中文科技期刊数据库</collection><collection>中文科技期刊数据库-CALIS站点</collection><collection>中文科技期刊数据库-7.0平台</collection><collection>中文科技期刊数据库-工程技术</collection><collection>中文科技期刊数据库- 镜像站点</collection><collection>CrossRef</collection><collection>Gale Academic OneFile</collection><collection>Biotechnology Research Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><jtitle>Journal of Bionic Engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Guixia</au><au>Liu, Lei</au><au>Liu, Chunyu</au><au>Zheng, Ming</au><au>Su, Lanying</au><au>Zhou, Chunguang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Combination of Neuro-Fuzzy Network Models with Biological Knowledge for Reconstructing Gene Regulatory Networks</atitle><jtitle>Journal of Bionic Engineering</jtitle><stitle>J Bionic Eng</stitle><addtitle>Journal of Bionics Engineering</addtitle><date>2011-03-01</date><risdate>2011</risdate><volume>8</volume><issue>1</issue><spage>98</spage><epage>106</epage><pages>98-106</pages><issn>1672-6529</issn><eissn>2543-2141</eissn><abstract>Inferring gene regulatory networks from large-scale expression data is an important topic in both cellular systems and computational biology. The inference of regulators might be the core factor for understanding actual regulatory conditions in gene regulatory networks, especially when strong regulators do work significantly. In this paper, we propose a novel approach based on combining neuro-fuzzy network models with biological knowledge to infer strong regulators and interrelated fuzzy rules. The hybrid neuro-fuzzy architecture can not only infer the fuzzy rules, which are suitable for describing the regulatory conditions in regulatory networks, but also explain the meaning of nodes and weight value in the neural network. It can get useful rules automatically without factitious judgments. At the same time, it does not add recursive layers to the model, and the model can also strengthen the relationships among genes and reduce calculation. We use the proposed approach to reconstruct a partial gene regulatory network of yeast. 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| subjects | Analysis Artificial Intelligence Biochemical Engineering Bioinformatics biological knowledge Biomaterials Biomedical Engineering and Bioengineering Biomedical Engineering/Biotechnology Engineering gene regulatory networks Genes Neural networks neuro-fuzzy network regulators 基因表达数据 基因调控网络 模糊神经网络模型 模糊规则 生物学知识 监管机构 蜂窝系统 计算生物学 |
| title | Combination of Neuro-Fuzzy Network Models with Biological Knowledge for Reconstructing Gene Regulatory Networks |
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