Identification of gene variant associated with Parkinson’s disease using genomic databases
Parkinson’s disease (PD) is the second-most common neurodegenerative disorder, which is still not established as the exact explanation of the illness. Identifying the target genes associated with the disorder plays a crucial part in managing PD. Different genetic experiments have established the imp...
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Veröffentlicht in: | Journal of ambient intelligence and humanized computing 2022-11, Vol.13 (11), p.5211-5224 |
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description | Parkinson’s disease (PD) is the second-most common neurodegenerative disorder, which is still not established as the exact explanation of the illness. Identifying the target genes associated with the disorder plays a crucial part in managing PD. Different genetic experiments have established the important target genes for disease development, but this remains difficult in the drug designing area. In this research, we suggested a novel approach to disease diagnosis that classifies variant genes for PD using gene mutation, gene expression and analysis of gene deletion. The protein sequence of PD genes was retrieved from genomic databases like NCBI, Ensemble, and UniProtKB and revealed the clinical relevance of various genes mis-sense mutation and amino acid codons. Here the targeted variant genes were identified using sequence matching. Set of PARK genes were identified as target genes by integrating gene mutation and expression data. Gene deletion analysis was carried out to determine the significant target for the Parkinson’s disease. The findings from the suggested mechanism will provide additional insight for understanding the disease mechanism of PD. This changes help drug designer for specific treatment. Future enhancement of this study may help in predicting disease biomarkers as well as designing novel compounds in rational drug designing. |
doi_str_mv | 10.1007/s12652-021-02994-4 |
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Identifying the target genes associated with the disorder plays a crucial part in managing PD. Different genetic experiments have established the important target genes for disease development, but this remains difficult in the drug designing area. In this research, we suggested a novel approach to disease diagnosis that classifies variant genes for PD using gene mutation, gene expression and analysis of gene deletion. The protein sequence of PD genes was retrieved from genomic databases like NCBI, Ensemble, and UniProtKB and revealed the clinical relevance of various genes mis-sense mutation and amino acid codons. Here the targeted variant genes were identified using sequence matching. Set of PARK genes were identified as target genes by integrating gene mutation and expression data. Gene deletion analysis was carried out to determine the significant target for the Parkinson’s disease. The findings from the suggested mechanism will provide additional insight for understanding the disease mechanism of PD. This changes help drug designer for specific treatment. Future enhancement of this study may help in predicting disease biomarkers as well as designing novel compounds in rational drug designing.</description><identifier>ISSN: 1868-5137</identifier><identifier>EISSN: 1868-5145</identifier><identifier>DOI: 10.1007/s12652-021-02994-4</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Amino acids ; Artificial Intelligence ; Behavior ; Biomarkers ; Brain research ; Chromosomes ; Computational Intelligence ; Dynamic programming ; Engineering ; Gait ; Gene expression ; Genes ; Genetics ; Genomes ; Medical imaging ; Mutation ; Original Research ; Parkinson's disease ; Proteins ; Robotics and Automation ; User Interfaces and Human Computer Interaction</subject><ispartof>Journal of ambient intelligence and humanized computing, 2022-11, Vol.13 (11), p.5211-5224</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c270t-a008c25da753c36cdca3e8aa7f6a21821213f66592bb3af635665c9de89505473</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12652-021-02994-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2919490632?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,21368,27903,27904,33723,41467,42536,43784,51297</link.rule.ids></links><search><creatorcontrib>Anusha, B.</creatorcontrib><creatorcontrib>Geetha, P.</creatorcontrib><title>Identification of gene variant associated with Parkinson’s disease using genomic databases</title><title>Journal of ambient intelligence and humanized computing</title><addtitle>J Ambient Intell Human Comput</addtitle><description>Parkinson’s disease (PD) is the second-most common neurodegenerative disorder, which is still not established as the exact explanation of the illness. Identifying the target genes associated with the disorder plays a crucial part in managing PD. Different genetic experiments have established the important target genes for disease development, but this remains difficult in the drug designing area. In this research, we suggested a novel approach to disease diagnosis that classifies variant genes for PD using gene mutation, gene expression and analysis of gene deletion. The protein sequence of PD genes was retrieved from genomic databases like NCBI, Ensemble, and UniProtKB and revealed the clinical relevance of various genes mis-sense mutation and amino acid codons. Here the targeted variant genes were identified using sequence matching. Set of PARK genes were identified as target genes by integrating gene mutation and expression data. Gene deletion analysis was carried out to determine the significant target for the Parkinson’s disease. The findings from the suggested mechanism will provide additional insight for understanding the disease mechanism of PD. This changes help drug designer for specific treatment. Future enhancement of this study may help in predicting disease biomarkers as well as designing novel compounds in rational drug designing.</description><subject>Algorithms</subject><subject>Amino acids</subject><subject>Artificial Intelligence</subject><subject>Behavior</subject><subject>Biomarkers</subject><subject>Brain research</subject><subject>Chromosomes</subject><subject>Computational Intelligence</subject><subject>Dynamic programming</subject><subject>Engineering</subject><subject>Gait</subject><subject>Gene expression</subject><subject>Genes</subject><subject>Genetics</subject><subject>Genomes</subject><subject>Medical imaging</subject><subject>Mutation</subject><subject>Original Research</subject><subject>Parkinson's disease</subject><subject>Proteins</subject><subject>Robotics and Automation</subject><subject>User Interfaces and Human Computer Interaction</subject><issn>1868-5137</issn><issn>1868-5145</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kMtKAzEUhoMoWGpfwFXA9Wguk8xkKcVLQdCF7oRwmsnUVJvUJFXc-Rq-nk9i6ojuPHA4F_7vHPgROqTkmBLSnCTKpGAVYbSkUnVV76ARbWVbCVqL3d-eN_toktKSlOCKU0pH6H7WWZ9d7wxkFzwOPV5Yb_ELRAc-Y0gpGAfZdvjV5Qd8A_HR-RT85_tHwp1LFpLFm-T8YguGlTO4gwzzsk4HaK-Hp2QnP3WM7s7PbqeX1dX1xWx6elUZ1pBcASGtYaKDRnDDpekMcNsCNL0ERltGGeW9lEKx-ZxDL7kog1GdbZUgom74GB0Nd9cxPG9synoZNtGXl5opqmpFJGdFxQaViSGlaHu9jm4F8U1TordG6sFIXYzU30bqukB8gFIR-4WNf6f_ob4A8PN3HQ</recordid><startdate>20221101</startdate><enddate>20221101</enddate><creator>Anusha, B.</creator><creator>Geetha, P.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20221101</creationdate><title>Identification of gene variant associated with Parkinson’s disease using genomic databases</title><author>Anusha, B. ; Geetha, P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c270t-a008c25da753c36cdca3e8aa7f6a21821213f66592bb3af635665c9de89505473</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Amino acids</topic><topic>Artificial Intelligence</topic><topic>Behavior</topic><topic>Biomarkers</topic><topic>Brain research</topic><topic>Chromosomes</topic><topic>Computational Intelligence</topic><topic>Dynamic programming</topic><topic>Engineering</topic><topic>Gait</topic><topic>Gene expression</topic><topic>Genes</topic><topic>Genetics</topic><topic>Genomes</topic><topic>Medical imaging</topic><topic>Mutation</topic><topic>Original Research</topic><topic>Parkinson's disease</topic><topic>Proteins</topic><topic>Robotics and Automation</topic><topic>User Interfaces and Human Computer Interaction</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Anusha, B.</creatorcontrib><creatorcontrib>Geetha, P.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Journal of ambient intelligence and humanized computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Anusha, B.</au><au>Geetha, P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of gene variant associated with Parkinson’s disease using genomic databases</atitle><jtitle>Journal of ambient intelligence and humanized computing</jtitle><stitle>J Ambient Intell Human Comput</stitle><date>2022-11-01</date><risdate>2022</risdate><volume>13</volume><issue>11</issue><spage>5211</spage><epage>5224</epage><pages>5211-5224</pages><issn>1868-5137</issn><eissn>1868-5145</eissn><abstract>Parkinson’s disease (PD) is the second-most common neurodegenerative disorder, which is still not established as the exact explanation of the illness. Identifying the target genes associated with the disorder plays a crucial part in managing PD. Different genetic experiments have established the important target genes for disease development, but this remains difficult in the drug designing area. In this research, we suggested a novel approach to disease diagnosis that classifies variant genes for PD using gene mutation, gene expression and analysis of gene deletion. The protein sequence of PD genes was retrieved from genomic databases like NCBI, Ensemble, and UniProtKB and revealed the clinical relevance of various genes mis-sense mutation and amino acid codons. Here the targeted variant genes were identified using sequence matching. Set of PARK genes were identified as target genes by integrating gene mutation and expression data. Gene deletion analysis was carried out to determine the significant target for the Parkinson’s disease. The findings from the suggested mechanism will provide additional insight for understanding the disease mechanism of PD. This changes help drug designer for specific treatment. Future enhancement of this study may help in predicting disease biomarkers as well as designing novel compounds in rational drug designing.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12652-021-02994-4</doi><tpages>14</tpages></addata></record> |
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subjects | Algorithms Amino acids Artificial Intelligence Behavior Biomarkers Brain research Chromosomes Computational Intelligence Dynamic programming Engineering Gait Gene expression Genes Genetics Genomes Medical imaging Mutation Original Research Parkinson's disease Proteins Robotics and Automation User Interfaces and Human Computer Interaction |
title | Identification of gene variant associated with Parkinson’s disease using genomic databases |
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