NightShift: NMR shift inference by general hybrid model training-a framework for NMR chemical shift prediction

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
  • Anna Katharina Dehof
  • Simon Loew
  • Hans-Peter Lenhof
  • Andreas Hildebrandt
Sammlungen
  • metadata
ISSN
1471-2105
Zeitschrift
BMC bioinformatics
Schlüsselwörter
  • 004 Informatik
  • 004 Data processing
Sprache
eng
Paginierung
Art. 98
Datum der Veröffentlichung
2013
Herausgeber
BioMed Central
Herausgeber URL
http://dx.doi.org/10.1186/1471-2105-14-98
Datum der Datenerfassung
2020
Datum, an dem der Datensatz öffentlich gemacht wurde
2020
Zugang
Public
Titel
NightShift: NMR shift inference by general hybrid model training : a framework for NMR chemical shift prediction
Ausgabe der Zeitschrift
14

Datenquelle: METADATA.UB

Andere Metadatenquellen:
Autoren
  • Anna Katharina Dehof
  • Simon Loew
  • Hans-Peter Lenhof
  • Andreas Hildebrandt
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000320309800001&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1186/1471-2105-14-98
Externe Identifier
  • Clarivate Analytics Document Solution ID: 163BH
  • PubMed Identifier: 23496927
ISSN
1471-2105
Zeitschrift
BMC BIOINFORMATICS
Artikelnummer
ARTN 98
Datum der Veröffentlichung
2013
Status
Published
Titel
NightShift: NMR shift inference by general hybrid model training - a framework for NMR chemical shift prediction
Sub types
  • Article
Ausgabe der Zeitschrift
14

Datenquelle: Web of Science (Lite)

Abstract
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>NMR chemical shift prediction plays an important role in various applications in computational biology. Among others, structure determination, structure optimization, and the scoring of docking results can profit from efficient and accurate chemical shift estimation from a three-dimensional model.</jats:p><jats:p>A variety of NMR chemical shift prediction approaches have been presented in the past, but nearly all of these rely on laborious manual data set preparation and the training itself is not automatized, making retraining the model, e.g., if new data is made available, or testing new models a time-consuming manual chore.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>In this work, we present the framework NightShift (NMR Shift Inference by General Hybrid Model Training), which enables automated data set generation as well as model training and evaluation of protein NMR chemical shift prediction.</jats:p><jats:p>In addition to this main result - the NightShift framework itself - we describe the resulting, automatically generated, data set and, as a proof-of-concept, a random forest model called Spinster that was built using the pipeline.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>By demonstrating that the performance of the automatically generated predictors is at least en par with the state of the art, we conclude that automated data set and predictor generation is well-suited for the design of NMR chemical shift estimators.</jats:p><jats:p>The framework can be downloaded from<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://bitbucket.org/akdehof/nightshift" ext-link-type="uri">https://bitbucket.org/akdehof/nightshift</jats:ext-link>. It requires the open source Biochemical Algorithms Library (BALL), and is available under the conditions of the GNU Lesser General Public License (LGPL). We additionally offer a browser-based user interface to our NightShift instance employing the Galaxy framework via<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://ballaxy.bioinf.uni-sb.de/" ext-link-type="uri">https://ballaxy.bioinf.uni-sb.de/</jats:ext-link>.</jats:p></jats:sec>
Autoren
  • Anna Katharina Dehof
  • Simon Loew
  • Hans-Peter Lenhof
  • Andreas Hildebrandt
DOI
10.1186/1471-2105-14-98
eISSN
1471-2105
Ausgabe der Veröffentlichung
1
Zeitschrift
BMC Bioinformatics
Sprache
en
Artikelnummer
98
Online publication date
2013
Datum der Veröffentlichung
2013
Status
Published
Herausgeber
Springer Science and Business Media LLC
Herausgeber URL
http://dx.doi.org/10.1186/1471-2105-14-98
Datum der Datenerfassung
2024
Titel
NightShift: NMR shift inference by general hybrid model training - a framework for NMR chemical shift prediction
Ausgabe der Zeitschrift
14

Datenquelle: Crossref

Abstract
<h4>Background</h4>NMR chemical shift prediction plays an important role in various applications in computational biology. Among others, structure determination, structure optimization, and the scoring of docking results can profit from efficient and accurate chemical shift estimation from a three-dimensional model.A variety of NMR chemical shift prediction approaches have been presented in the past, but nearly all of these rely on laborious manual data set preparation and the training itself is not automatized, making retraining the model, e.g., if new data is made available, or testing new models a time-consuming manual chore.<h4>Results</h4>In this work, we present the framework NightShift (NMR Shift Inference by General Hybrid Model Training), which enables automated data set generation as well as model training and evaluation of protein NMR chemical shift prediction.In addition to this main result - the NightShift framework itself - we describe the resulting, automatically generated, data set and, as a proof-of-concept, a random forest model called Spinster that was built using the pipeline.<h4>Conclusion</h4>By demonstrating that the performance of the automatically generated predictors is at least en par with the state of the art, we conclude that automated data set and predictor generation is well-suited for the design of NMR chemical shift estimators.The framework can be downloaded from https://bitbucket.org/akdehof/nightshift. It requires the open source Biochemical Algorithms Library (BALL), and is available under the conditions of the GNU Lesser General Public License (LGPL). We additionally offer a browser-based user interface to our NightShift instance employing the Galaxy framework via https://ballaxy.bioinf.uni-sb.de/.
Addresses
  • Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany.
Autoren
  • Anna Katharina Dehof
  • Simon Loew
  • Hans-Peter Lenhof
  • Andreas Hildebrandt
DOI
10.1186/1471-2105-14-98
eISSN
1471-2105
Externe Identifier
  • PubMed Identifier: 23496927
  • PubMed Central ID: PMC3682865
Open access
true
ISSN
1471-2105
Zeitschrift
BMC bioinformatics
Schlüsselwörter
  • Proteins
  • Nuclear Magnetic Resonance, Biomolecular
  • Models, Statistical
  • Algorithms
  • Software
  • Databases, Protein
Sprache
eng
Medium
Electronic
Online publication date
2013
Open access status
Open Access
Paginierung
98
Datum der Veröffentlichung
2013
Status
Published
Publisher licence
CC BY
Datum der Datenerfassung
2013
Titel
NightShift: NMR shift inference by general hybrid model training--a framework for NMR chemical shift prediction.
Sub types
  • Research Support, Non-U.S. Gov't
  • research-article
  • Journal Article
Ausgabe der Zeitschrift
14

Files

https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/1471-2105-14-98 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23496927/pdf/?tool=EBI https://europepmc.org/articles/PMC3682865?pdf=render

Datenquelle: Europe PubMed Central

Abstract
BACKGROUND: NMR chemical shift prediction plays an important role in various applications in computational biology. Among others, structure determination, structure optimization, and the scoring of docking results can profit from efficient and accurate chemical shift estimation from a three-dimensional model.A variety of NMR chemical shift prediction approaches have been presented in the past, but nearly all of these rely on laborious manual data set preparation and the training itself is not automatized, making retraining the model, e.g., if new data is made available, or testing new models a time-consuming manual chore. RESULTS: In this work, we present the framework NightShift (NMR Shift Inference by General Hybrid Model Training), which enables automated data set generation as well as model training and evaluation of protein NMR chemical shift prediction.In addition to this main result - the NightShift framework itself - we describe the resulting, automatically generated, data set and, as a proof-of-concept, a random forest model called Spinster that was built using the pipeline. CONCLUSION: By demonstrating that the performance of the automatically generated predictors is at least en par with the state of the art, we conclude that automated data set and predictor generation is well-suited for the design of NMR chemical shift estimators.The framework can be downloaded from https://bitbucket.org/akdehof/nightshift. It requires the open source Biochemical Algorithms Library (BALL), and is available under the conditions of the GNU Lesser General Public License (LGPL). We additionally offer a browser-based user interface to our NightShift instance employing the Galaxy framework via https://ballaxy.bioinf.uni-sb.de/.
Date of acceptance
2013
Autoren
  • Anna Katharina Dehof
  • Simon Loew
  • Hans-Peter Lenhof
  • Andreas Hildebrandt
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/23496927
DOI
10.1186/1471-2105-14-98
eISSN
1471-2105
Externe Identifier
  • PubMed Central ID: PMC3682865
Zeitschrift
BMC Bioinformatics
Schlüsselwörter
  • Algorithms
  • Databases, Protein
  • Models, Statistical
  • Nuclear Magnetic Resonance, Biomolecular
  • Proteins
  • Software
Sprache
eng
Country
England
Paginierung
98
PII
1471-2105-14-98
Datum der Veröffentlichung
2013
Status
Published online
Datum, an dem der Datensatz öffentlich gemacht wurde
2014
Titel
NightShift: NMR shift inference by general hybrid model training--a framework for NMR chemical shift prediction.
Sub types
  • Journal Article
  • Research Support, Non-U.S. Gov't
Ausgabe der Zeitschrift
14

Datenquelle: PubMed

Author's licence
CC-BY
Autoren
  • Anna Katharina Dehof
  • Simon Loew
  • Hans-Peter Lenhof
  • Andreas Hildebrandt
Hosting institution
Universitätsbibliothek Mainz
Sammlungen
  • DFG-OA-Publizieren (2012 - 2017)
Resource version
Published version
DOI
10.1186/1471-2105-14-98
Funding acknowledgements
  • DFG, Open Access-Publizieren Universität Mainz / Universitätsmedizin
File(s) embargoed
false
Open access
true
ISSN
1471-2105
Zeitschrift
BMC bioinformatics
Schlüsselwörter
  • 004 Informatik
  • 004 Data processing
Sprache
eng
Open access status
Open Access
Paginierung
Art. 98
Datum der Veröffentlichung
2013
Public URL
https://openscience.ub.uni-mainz.de/handle/20.500.12030/7784
Herausgeber
BioMed Central
Herausgeber URL
http://dx.doi.org/10.1186/1471-2105-14-98
Datum der Datenerfassung
2022
Datum, an dem der Datensatz öffentlich gemacht wurde
2022
Zugang
Public
Titel
NightShift: NMR shift inference by general hybrid model training : a framework for NMR chemical shift prediction
Ausgabe der Zeitschrift
14

Files

nightshift__nmr_shift_inferen-20220913201246106.pdf

Datenquelle: OPENSCIENCE.UB

Autoren
  • Anna Katharina Dehof
  • Simon Loew
  • Hans-Peter Lenhof
  • Andreas Hildebrandt
Zeitschrift
BMC bioinformatics
Artikelnummer
1
Paginierung
98 - 98
Datum der Veröffentlichung
2013
Herausgeber
BioMed Central
Datum der Datenerfassung
2020
Titel
NightShift: NMR shift inference by general hybrid model training-a framework for NMR chemical shift prediction
Sub types
  • article
Ausgabe der Zeitschrift
14

Datenquelle: Manual

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