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PEITH(Θ): perfecting experiments with information theory in Python with GPU support
Abstract Motivation Different experiments provide differing levels of information about a biological system. This makes it difficult, a priori, to select one of them beyond mere speculation and/or belief, especially when resources are limited. With the increasing diversity of experimental approaches...
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| Published in: | Bioinformatics 2018-04, Vol.34 (7), p.1249-1250 |
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| Main Authors: | , , , , , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c452t-94bef78ec3ba9e86d2ac4d197ba9288c7edbfa7672d25e99669e648ed4f3b7a3 |
| container_end_page | 1250 |
| container_issue | 7 |
| container_start_page | 1249 |
| container_title | Bioinformatics |
| container_volume | 34 |
| creator | Dony, Leander Mackerodt, Jonas Ward, Scott Filippi, Sarah Stumpf, Michael P H Liepe, Juliane |
| description | Abstract
Motivation
Different experiments provide differing levels of information about a biological system. This makes it difficult, a priori, to select one of them beyond mere speculation and/or belief, especially when resources are limited. With the increasing diversity of experimental approaches and general advances in quantitative systems biology, methods that inform us about the information content that a given experiment carries about the question we want to answer, become crucial.
Results
PEITH(Θ) is a general purpose, Python framework for experimental design in systems biology. PEITH(Θ) uses Bayesian inference and information theory in order to derive which experiments are most informative in order to estimate all model parameters and/or perform model predictions.
Availability and implementation
https://github.com/MichaelPHStumpf/Peitho |
| doi_str_mv | 10.1093/bioinformatics/btx776 |
| format | article |
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Motivation
Different experiments provide differing levels of information about a biological system. This makes it difficult, a priori, to select one of them beyond mere speculation and/or belief, especially when resources are limited. With the increasing diversity of experimental approaches and general advances in quantitative systems biology, methods that inform us about the information content that a given experiment carries about the question we want to answer, become crucial.
Results
PEITH(Θ) is a general purpose, Python framework for experimental design in systems biology. PEITH(Θ) uses Bayesian inference and information theory in order to derive which experiments are most informative in order to estimate all model parameters and/or perform model predictions.
Availability and implementation
https://github.com/MichaelPHStumpf/Peitho</description><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1460-2059</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btx776</identifier><identifier>PMID: 29228182</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Applications Notes ; Bayes Theorem ; Information Theory ; Software ; Systems Biology - methods</subject><ispartof>Bioinformatics, 2018-04, Vol.34 (7), p.1249-1250</ispartof><rights>The Author 2017. Published by Oxford University Press. 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c452t-94bef78ec3ba9e86d2ac4d197ba9288c7edbfa7672d25e99669e648ed4f3b7a3</citedby><cites>FETCH-LOGICAL-c452t-94bef78ec3ba9e86d2ac4d197ba9288c7edbfa7672d25e99669e648ed4f3b7a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5998942/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5998942/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,1604,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29228182$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Wren, Jonathan</contributor><creatorcontrib>Dony, Leander</creatorcontrib><creatorcontrib>Mackerodt, Jonas</creatorcontrib><creatorcontrib>Ward, Scott</creatorcontrib><creatorcontrib>Filippi, Sarah</creatorcontrib><creatorcontrib>Stumpf, Michael P H</creatorcontrib><creatorcontrib>Liepe, Juliane</creatorcontrib><title>PEITH(Θ): perfecting experiments with information theory in Python with GPU support</title><title>Bioinformatics</title><addtitle>Bioinformatics</addtitle><description>Abstract
Motivation
Different experiments provide differing levels of information about a biological system. This makes it difficult, a priori, to select one of them beyond mere speculation and/or belief, especially when resources are limited. With the increasing diversity of experimental approaches and general advances in quantitative systems biology, methods that inform us about the information content that a given experiment carries about the question we want to answer, become crucial.
Results
PEITH(Θ) is a general purpose, Python framework for experimental design in systems biology. PEITH(Θ) uses Bayesian inference and information theory in order to derive which experiments are most informative in order to estimate all model parameters and/or perform model predictions.
Availability and implementation
https://github.com/MichaelPHStumpf/Peitho</description><subject>Applications Notes</subject><subject>Bayes Theorem</subject><subject>Information Theory</subject><subject>Software</subject><subject>Systems Biology - methods</subject><issn>1367-4803</issn><issn>1460-2059</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><recordid>eNqNUctO4zAUtUYg6HT4hEFZwiLgOI4fs0AaoQ4gVaKLztpynBtq1MYZ22HaP-Gr-CYMhQp2rO7r3HOP7kHoZ4HPCizL89o627XOr3S0JpzXcc05-4ZGBWU4J7iSeykvGc-pwOUh-h7CPcZVQSk9QIdEEiIKQUZoPpvczK9Pnh5Pf2U9-BZMtN1dButU2BV0MWT_bVxku1uuy-ICnN-kVjbbxEVqvCKuZn-zMPS98_EH2m_1MsDRWxyj-Z_J_PI6n95e3Vz-nuaGViTmktbQcgGmrLUEwRqiDW0KyVNJhDAcmrrVnHHSkAqkZEwCowIa2pY11-UYXWxp-6FeQWOSWq-Xqk_Ctd8op636POnsQt25B1VJKSQlieDkjcC7fwOEqFY2GFgudQduCCpJYRgTmv49RtUWarwLwUO7O1Ng9WKI-myI2hqS9o4_atxtvTuQAHgLcEP_Rc5n6q2hlQ</recordid><startdate>20180401</startdate><enddate>20180401</enddate><creator>Dony, Leander</creator><creator>Mackerodt, Jonas</creator><creator>Ward, Scott</creator><creator>Filippi, Sarah</creator><creator>Stumpf, Michael P H</creator><creator>Liepe, Juliane</creator><general>Oxford University Press</general><scope>TOX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20180401</creationdate><title>PEITH(Θ): perfecting experiments with information theory in Python with GPU support</title><author>Dony, Leander ; Mackerodt, Jonas ; Ward, Scott ; Filippi, Sarah ; Stumpf, Michael P H ; Liepe, Juliane</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c452t-94bef78ec3ba9e86d2ac4d197ba9288c7edbfa7672d25e99669e648ed4f3b7a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Applications Notes</topic><topic>Bayes Theorem</topic><topic>Information Theory</topic><topic>Software</topic><topic>Systems Biology - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dony, Leander</creatorcontrib><creatorcontrib>Mackerodt, Jonas</creatorcontrib><creatorcontrib>Ward, Scott</creatorcontrib><creatorcontrib>Filippi, Sarah</creatorcontrib><creatorcontrib>Stumpf, Michael P H</creatorcontrib><creatorcontrib>Liepe, Juliane</creatorcontrib><collection>Oxford Open</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dony, Leander</au><au>Mackerodt, Jonas</au><au>Ward, Scott</au><au>Filippi, Sarah</au><au>Stumpf, Michael P H</au><au>Liepe, Juliane</au><au>Wren, Jonathan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>PEITH(Θ): perfecting experiments with information theory in Python with GPU support</atitle><jtitle>Bioinformatics</jtitle><addtitle>Bioinformatics</addtitle><date>2018-04-01</date><risdate>2018</risdate><volume>34</volume><issue>7</issue><spage>1249</spage><epage>1250</epage><pages>1249-1250</pages><issn>1367-4803</issn><eissn>1460-2059</eissn><eissn>1367-4811</eissn><abstract>Abstract
Motivation
Different experiments provide differing levels of information about a biological system. This makes it difficult, a priori, to select one of them beyond mere speculation and/or belief, especially when resources are limited. With the increasing diversity of experimental approaches and general advances in quantitative systems biology, methods that inform us about the information content that a given experiment carries about the question we want to answer, become crucial.
Results
PEITH(Θ) is a general purpose, Python framework for experimental design in systems biology. PEITH(Θ) uses Bayesian inference and information theory in order to derive which experiments are most informative in order to estimate all model parameters and/or perform model predictions.
Availability and implementation
https://github.com/MichaelPHStumpf/Peitho</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>29228182</pmid><doi>10.1093/bioinformatics/btx776</doi><tpages>2</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1367-4803 |
| ispartof | Bioinformatics, 2018-04, Vol.34 (7), p.1249-1250 |
| issn | 1367-4803 1460-2059 1367-4811 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5998942 |
| source | Oxford Open; PubMed Central |
| subjects | Applications Notes Bayes Theorem Information Theory Software Systems Biology - methods |
| title | PEITH(Θ): perfecting experiments with information theory in Python with GPU support |
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