PDBMine: A Reformulation of the Protein Data Bank to Facilitate Structural Data Mining
Large scale initiatives such as the Human Genome Project, Structural Genomics, and individual research teams have provided large deposits of genomic and proteomic data. The transfer of data to knowledge has become one of the existing challenges, which is a consequence of capturing data in databases...
Gespeichert in:
| Veröffentlicht in: | arXiv.org 2019-11 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | arXiv.org |
| container_volume | |
| creator | Cole, Casey A Ott, Christopher Valdes, Diego Valafar, Homayoun |
| description | Large scale initiatives such as the Human Genome Project, Structural Genomics, and individual research teams have provided large deposits of genomic and proteomic data. The transfer of data to knowledge has become one of the existing challenges, which is a consequence of capturing data in databases that are optimally designed for archiving and not mining. In this research, we have targeted the Protein Databank (PDB) and demonstrated a transformation of its content, named PDBMine, that reduces storage space by an order of magnitude, and allows for powerful mining in relation to the topic of protein structure determination. We have demonstrated the utility of PDBMine in exploring the prevalence of dimeric and trimeric amino acid sequences and provided a mechanism of predicting protein structure. |
| format | Article |
| fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2316661049</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2316661049</sourcerecordid><originalsourceid>FETCH-proquest_journals_23166610493</originalsourceid><addsrcrecordid>eNqNjEsKwjAUAIMgWLR3eOC6kCZt_OystbgRRMVtCSXV1Jho-nJ_C3oAV7OYYUYkYpynyTJjbELivu8opUwsWJ7ziFyPZXHQVq1hAyfVOv8MRqJ2FlwLeFdw9A6VtlBKlFBI-wB0UMlGG40SFZzRhwaDl-abDDNtbzMybqXpVfzjlMyr3WW7T17evYPqse5c8HZQNeOpECKl2Yr_V30AMww_ww</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2316661049</pqid></control><display><type>article</type><title>PDBMine: A Reformulation of the Protein Data Bank to Facilitate Structural Data Mining</title><source>Free E- Journals</source><creator>Cole, Casey A ; Ott, Christopher ; Valdes, Diego ; Valafar, Homayoun</creator><creatorcontrib>Cole, Casey A ; Ott, Christopher ; Valdes, Diego ; Valafar, Homayoun</creatorcontrib><description>Large scale initiatives such as the Human Genome Project, Structural Genomics, and individual research teams have provided large deposits of genomic and proteomic data. The transfer of data to knowledge has become one of the existing challenges, which is a consequence of capturing data in databases that are optimally designed for archiving and not mining. In this research, we have targeted the Protein Databank (PDB) and demonstrated a transformation of its content, named PDBMine, that reduces storage space by an order of magnitude, and allows for powerful mining in relation to the topic of protein structure determination. We have demonstrated the utility of PDBMine in exploring the prevalence of dimeric and trimeric amino acid sequences and provided a mechanism of predicting protein structure.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Archiving ; Data banks ; Data mining ; Data transfer (computers) ; Knowledge management ; Proteins</subject><ispartof>arXiv.org, 2019-11</ispartof><rights>2019. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>776,780</link.rule.ids></links><search><creatorcontrib>Cole, Casey A</creatorcontrib><creatorcontrib>Ott, Christopher</creatorcontrib><creatorcontrib>Valdes, Diego</creatorcontrib><creatorcontrib>Valafar, Homayoun</creatorcontrib><title>PDBMine: A Reformulation of the Protein Data Bank to Facilitate Structural Data Mining</title><title>arXiv.org</title><description>Large scale initiatives such as the Human Genome Project, Structural Genomics, and individual research teams have provided large deposits of genomic and proteomic data. The transfer of data to knowledge has become one of the existing challenges, which is a consequence of capturing data in databases that are optimally designed for archiving and not mining. In this research, we have targeted the Protein Databank (PDB) and demonstrated a transformation of its content, named PDBMine, that reduces storage space by an order of magnitude, and allows for powerful mining in relation to the topic of protein structure determination. We have demonstrated the utility of PDBMine in exploring the prevalence of dimeric and trimeric amino acid sequences and provided a mechanism of predicting protein structure.</description><subject>Archiving</subject><subject>Data banks</subject><subject>Data mining</subject><subject>Data transfer (computers)</subject><subject>Knowledge management</subject><subject>Proteins</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNqNjEsKwjAUAIMgWLR3eOC6kCZt_OystbgRRMVtCSXV1Jho-nJ_C3oAV7OYYUYkYpynyTJjbELivu8opUwsWJ7ziFyPZXHQVq1hAyfVOv8MRqJ2FlwLeFdw9A6VtlBKlFBI-wB0UMlGG40SFZzRhwaDl-abDDNtbzMybqXpVfzjlMyr3WW7T17evYPqse5c8HZQNeOpECKl2Yr_V30AMww_ww</recordid><startdate>20191119</startdate><enddate>20191119</enddate><creator>Cole, Casey A</creator><creator>Ott, Christopher</creator><creator>Valdes, Diego</creator><creator>Valafar, Homayoun</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20191119</creationdate><title>PDBMine: A Reformulation of the Protein Data Bank to Facilitate Structural Data Mining</title><author>Cole, Casey A ; Ott, Christopher ; Valdes, Diego ; Valafar, Homayoun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_23166610493</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Archiving</topic><topic>Data banks</topic><topic>Data mining</topic><topic>Data transfer (computers)</topic><topic>Knowledge management</topic><topic>Proteins</topic><toplevel>online_resources</toplevel><creatorcontrib>Cole, Casey A</creatorcontrib><creatorcontrib>Ott, Christopher</creatorcontrib><creatorcontrib>Valdes, Diego</creatorcontrib><creatorcontrib>Valafar, Homayoun</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</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>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cole, Casey A</au><au>Ott, Christopher</au><au>Valdes, Diego</au><au>Valafar, Homayoun</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>PDBMine: A Reformulation of the Protein Data Bank to Facilitate Structural Data Mining</atitle><jtitle>arXiv.org</jtitle><date>2019-11-19</date><risdate>2019</risdate><eissn>2331-8422</eissn><abstract>Large scale initiatives such as the Human Genome Project, Structural Genomics, and individual research teams have provided large deposits of genomic and proteomic data. The transfer of data to knowledge has become one of the existing challenges, which is a consequence of capturing data in databases that are optimally designed for archiving and not mining. In this research, we have targeted the Protein Databank (PDB) and demonstrated a transformation of its content, named PDBMine, that reduces storage space by an order of magnitude, and allows for powerful mining in relation to the topic of protein structure determination. We have demonstrated the utility of PDBMine in exploring the prevalence of dimeric and trimeric amino acid sequences and provided a mechanism of predicting protein structure.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | EISSN: 2331-8422 |
| ispartof | arXiv.org, 2019-11 |
| issn | 2331-8422 |
| language | eng |
| recordid | cdi_proquest_journals_2316661049 |
| source | Free E- Journals |
| subjects | Archiving Data banks Data mining Data transfer (computers) Knowledge management Proteins |
| title | PDBMine: A Reformulation of the Protein Data Bank to Facilitate Structural Data Mining |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T01%3A56%3A05IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=PDBMine:%20A%20Reformulation%20of%20the%20Protein%20Data%20Bank%20to%20Facilitate%20Structural%20Data%20Mining&rft.jtitle=arXiv.org&rft.au=Cole,%20Casey%20A&rft.date=2019-11-19&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2316661049%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2316661049&rft_id=info:pmid/&rfr_iscdi=true |