Characterizing the shape patterns of dimorphic yeast pseudohyphae

Pseudohyphal growth of the dimorphic yeast Saccharomyces cerevisiae is analysed using two-dimensional top-down binary images. The colony morphology is characterized using clustered shape primitives (CSPs), which are learned automatically from the data and thus do not require a list of predefined fea...
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Autor*in:	Amelia Gontar [verfasserIn] Murk J. Bottema [verfasserIn] Benjamin J. Binder [verfasserIn] Hayden Tronnolone [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	clustered shape primitives shape characterization pseudohyphal growth dimorphic yeast

Übergeordnetes Werk:	In: Royal Society Open Science - The Royal Society, 2015, 5(2018), 10
Übergeordnetes Werk:	volume:5 ; year:2018 ; number:10

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1098/rsos.180820

Katalog-ID:	DOAJ017299160

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520			\|a Pseudohyphal growth of the dimorphic yeast Saccharomyces cerevisiae is analysed using two-dimensional top-down binary images. The colony morphology is characterized using clustered shape primitives (CSPs), which are learned automatically from the data and thus do not require a list of predefined features or a priori knowledge of the shape. The power of CSPs is demonstrated through the classification of pseudohyphal yeast colonies known to produce different morphologies. The classifier categorizes the yeast colonies considered with an accuracy of 0.969 and standard deviation 0.041, demonstrating that CSPs capture differences in morphology, while CSPs are found to provide greater discriminatory power than spatial indices previously used to quantify pseudohyphal growth. The analysis demonstrates that CSPs provide a promising avenue for analysing morphology in high-throughput assays.
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allfieldsSound	10.1098/rsos.180820 doi (DE-627)DOAJ017299160 (DE-599)DOAJ427234a477254326820feb7883cbf0e3 DE-627 ger DE-627 rakwb eng Amelia Gontar verfasserin aut Characterizing the shape patterns of dimorphic yeast pseudohyphae 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pseudohyphal growth of the dimorphic yeast Saccharomyces cerevisiae is analysed using two-dimensional top-down binary images. The colony morphology is characterized using clustered shape primitives (CSPs), which are learned automatically from the data and thus do not require a list of predefined features or a priori knowledge of the shape. The power of CSPs is demonstrated through the classification of pseudohyphal yeast colonies known to produce different morphologies. The classifier categorizes the yeast colonies considered with an accuracy of 0.969 and standard deviation 0.041, demonstrating that CSPs capture differences in morphology, while CSPs are found to provide greater discriminatory power than spatial indices previously used to quantify pseudohyphal growth. The analysis demonstrates that CSPs provide a promising avenue for analysing morphology in high-throughput assays. clustered shape primitives shape characterization pseudohyphal growth dimorphic yeast Science Q Murk J. Bottema verfasserin aut Benjamin J. Binder verfasserin aut Hayden Tronnolone verfasserin aut In Royal Society Open Science The Royal Society, 2015 5(2018), 10 (DE-627)798561173 (DE-600)2787755-3 20545703 nnns volume:5 year:2018 number:10 https://doi.org/10.1098/rsos.180820 kostenfrei https://doaj.org/article/427234a477254326820feb7883cbf0e3 kostenfrei https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.180820 kostenfrei https://doaj.org/toc/2054-5703 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2018 10
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author	Amelia Gontar
spellingShingle	Amelia Gontar misc clustered shape primitives misc shape characterization misc pseudohyphal growth misc dimorphic yeast misc Science misc Q Characterizing the shape patterns of dimorphic yeast pseudohyphae
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topic_title	Characterizing the shape patterns of dimorphic yeast pseudohyphae clustered shape primitives shape characterization pseudohyphal growth dimorphic yeast
topic	misc clustered shape primitives misc shape characterization misc pseudohyphal growth misc dimorphic yeast misc Science misc Q
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title	Characterizing the shape patterns of dimorphic yeast pseudohyphae
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title_full	Characterizing the shape patterns of dimorphic yeast pseudohyphae
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title_sort	characterizing the shape patterns of dimorphic yeast pseudohyphae
title_auth	Characterizing the shape patterns of dimorphic yeast pseudohyphae
abstract	Pseudohyphal growth of the dimorphic yeast Saccharomyces cerevisiae is analysed using two-dimensional top-down binary images. The colony morphology is characterized using clustered shape primitives (CSPs), which are learned automatically from the data and thus do not require a list of predefined features or a priori knowledge of the shape. The power of CSPs is demonstrated through the classification of pseudohyphal yeast colonies known to produce different morphologies. The classifier categorizes the yeast colonies considered with an accuracy of 0.969 and standard deviation 0.041, demonstrating that CSPs capture differences in morphology, while CSPs are found to provide greater discriminatory power than spatial indices previously used to quantify pseudohyphal growth. The analysis demonstrates that CSPs provide a promising avenue for analysing morphology in high-throughput assays.
abstractGer	Pseudohyphal growth of the dimorphic yeast Saccharomyces cerevisiae is analysed using two-dimensional top-down binary images. The colony morphology is characterized using clustered shape primitives (CSPs), which are learned automatically from the data and thus do not require a list of predefined features or a priori knowledge of the shape. The power of CSPs is demonstrated through the classification of pseudohyphal yeast colonies known to produce different morphologies. The classifier categorizes the yeast colonies considered with an accuracy of 0.969 and standard deviation 0.041, demonstrating that CSPs capture differences in morphology, while CSPs are found to provide greater discriminatory power than spatial indices previously used to quantify pseudohyphal growth. The analysis demonstrates that CSPs provide a promising avenue for analysing morphology in high-throughput assays.
abstract_unstemmed	Pseudohyphal growth of the dimorphic yeast Saccharomyces cerevisiae is analysed using two-dimensional top-down binary images. The colony morphology is characterized using clustered shape primitives (CSPs), which are learned automatically from the data and thus do not require a list of predefined features or a priori knowledge of the shape. The power of CSPs is demonstrated through the classification of pseudohyphal yeast colonies known to produce different morphologies. The classifier categorizes the yeast colonies considered with an accuracy of 0.969 and standard deviation 0.041, demonstrating that CSPs capture differences in morphology, while CSPs are found to provide greater discriminatory power than spatial indices previously used to quantify pseudohyphal growth. The analysis demonstrates that CSPs provide a promising avenue for analysing morphology in high-throughput assays.
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title_short	Characterizing the shape patterns of dimorphic yeast pseudohyphae
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Characterizing the shape patterns of dimorphic yeast pseudohyphae

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