Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images
Abstract The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. Central to that goal is the determination of the function of each protein that is present in a given cell type, and determining a protein's loc...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Murphy, Robert F. [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2003 |
|---|
| Anmerkung: |
© Kluwer Academic Publishers 2003 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Journal of VLSI signal processing systems for signal, image and video technology - Kluwer Academic Publishers, 1989, 35(2003), 3 vom: 01. Nov., Seite 311-321 |
|---|---|
| Übergeordnetes Werk: |
volume:35 ; year:2003 ; number:3 ; day:01 ; month:11 ; pages:311-321 |
| Links: |
|---|
| DOI / URN: |
10.1023/B:VLSI.0000003028.71666.44 |
|---|
| Katalog-ID: |
OLC2062086288 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2062086288 | ||
| 003 | DE-627 | ||
| 005 | 20230504072307.0 | ||
| 007 | tu | ||
| 008 | 200819s2003 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1023/B:VLSI.0000003028.71666.44 |2 doi | |
| 035 | |a (DE-627)OLC2062086288 | ||
| 035 | |a (DE-He213)B:VLSI.0000003028.71666.44-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 620 |q VZ |
| 100 | 1 | |a Murphy, Robert F. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images |
| 264 | 1 | |c 2003 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Kluwer Academic Publishers 2003 | ||
| 520 | |a Abstract The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. Central to that goal is the determination of the function of each protein that is present in a given cell type, and determining a protein's location within cells is critical to understanding its function. As large amounts of data become available from genome-wide determination of protein subcellular location, automated approaches to categorizing and comparing location patterns are urgently needed. Since subcellular location is most often determined using fluorescence microscopy, we have developed automated systems for interpreting the resulting images. We report here improved numeric features for describing such images that are fairly robust to image intensity binning and spatial resolution. We validate these features by using them to train neural networks that accurately recognize all major subcellular patterns with an accuracy higher than any previously reported. Having validated the features by using them for classification, we also demonstrate using them to create Subcellular Location Trees that group similar proteins and provide a systematic framework for describing subcellular location. | ||
| 700 | 1 | |a Velliste, Meel |4 aut | |
| 700 | 1 | |a Porreca, Gregory |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of VLSI signal processing systems for signal, image and video technology |d Kluwer Academic Publishers, 1989 |g 35(2003), 3 vom: 01. Nov., Seite 311-321 |w (DE-627)130761508 |w (DE-600)1000618-7 |w (DE-576)02508416X |x 0922-5773 |7 nnns |
| 773 | 1 | 8 | |g volume:35 |g year:2003 |g number:3 |g day:01 |g month:11 |g pages:311-321 |
| 856 | 4 | 1 | |u https://doi.org/10.1023/B:VLSI.0000003028.71666.44 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-TEC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_31 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_2006 | ||
| 912 | |a GBV_ILN_2244 | ||
| 912 | |a GBV_ILN_4307 | ||
| 912 | |a GBV_ILN_4319 | ||
| 951 | |a AR | ||
| 952 | |d 35 |j 2003 |e 3 |b 01 |c 11 |h 311-321 | ||
| author_variant |
r f m rf rfm m v mv g p gp |
|---|---|
| matchkey_str |
article:09225773:2003----::outueiafauefrecitoadlsiiainfuclualctopteni |
| hierarchy_sort_str |
2003 |
| publishDate |
2003 |
| allfields |
10.1023/B:VLSI.0000003028.71666.44 doi (DE-627)OLC2062086288 (DE-He213)B:VLSI.0000003028.71666.44-p DE-627 ger DE-627 rakwb eng 620 VZ Murphy, Robert F. verfasserin aut Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2003 Abstract The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. Central to that goal is the determination of the function of each protein that is present in a given cell type, and determining a protein's location within cells is critical to understanding its function. As large amounts of data become available from genome-wide determination of protein subcellular location, automated approaches to categorizing and comparing location patterns are urgently needed. Since subcellular location is most often determined using fluorescence microscopy, we have developed automated systems for interpreting the resulting images. We report here improved numeric features for describing such images that are fairly robust to image intensity binning and spatial resolution. We validate these features by using them to train neural networks that accurately recognize all major subcellular patterns with an accuracy higher than any previously reported. Having validated the features by using them for classification, we also demonstrate using them to create Subcellular Location Trees that group similar proteins and provide a systematic framework for describing subcellular location. Velliste, Meel aut Porreca, Gregory aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Kluwer Academic Publishers, 1989 35(2003), 3 vom: 01. Nov., Seite 311-321 (DE-627)130761508 (DE-600)1000618-7 (DE-576)02508416X 0922-5773 nnns volume:35 year:2003 number:3 day:01 month:11 pages:311-321 https://doi.org/10.1023/B:VLSI.0000003028.71666.44 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_23 GBV_ILN_31 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2244 GBV_ILN_4307 GBV_ILN_4319 AR 35 2003 3 01 11 311-321 |
| spelling |
10.1023/B:VLSI.0000003028.71666.44 doi (DE-627)OLC2062086288 (DE-He213)B:VLSI.0000003028.71666.44-p DE-627 ger DE-627 rakwb eng 620 VZ Murphy, Robert F. verfasserin aut Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2003 Abstract The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. Central to that goal is the determination of the function of each protein that is present in a given cell type, and determining a protein's location within cells is critical to understanding its function. As large amounts of data become available from genome-wide determination of protein subcellular location, automated approaches to categorizing and comparing location patterns are urgently needed. Since subcellular location is most often determined using fluorescence microscopy, we have developed automated systems for interpreting the resulting images. We report here improved numeric features for describing such images that are fairly robust to image intensity binning and spatial resolution. We validate these features by using them to train neural networks that accurately recognize all major subcellular patterns with an accuracy higher than any previously reported. Having validated the features by using them for classification, we also demonstrate using them to create Subcellular Location Trees that group similar proteins and provide a systematic framework for describing subcellular location. Velliste, Meel aut Porreca, Gregory aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Kluwer Academic Publishers, 1989 35(2003), 3 vom: 01. Nov., Seite 311-321 (DE-627)130761508 (DE-600)1000618-7 (DE-576)02508416X 0922-5773 nnns volume:35 year:2003 number:3 day:01 month:11 pages:311-321 https://doi.org/10.1023/B:VLSI.0000003028.71666.44 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_23 GBV_ILN_31 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2244 GBV_ILN_4307 GBV_ILN_4319 AR 35 2003 3 01 11 311-321 |
| allfields_unstemmed |
10.1023/B:VLSI.0000003028.71666.44 doi (DE-627)OLC2062086288 (DE-He213)B:VLSI.0000003028.71666.44-p DE-627 ger DE-627 rakwb eng 620 VZ Murphy, Robert F. verfasserin aut Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2003 Abstract The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. Central to that goal is the determination of the function of each protein that is present in a given cell type, and determining a protein's location within cells is critical to understanding its function. As large amounts of data become available from genome-wide determination of protein subcellular location, automated approaches to categorizing and comparing location patterns are urgently needed. Since subcellular location is most often determined using fluorescence microscopy, we have developed automated systems for interpreting the resulting images. We report here improved numeric features for describing such images that are fairly robust to image intensity binning and spatial resolution. We validate these features by using them to train neural networks that accurately recognize all major subcellular patterns with an accuracy higher than any previously reported. Having validated the features by using them for classification, we also demonstrate using them to create Subcellular Location Trees that group similar proteins and provide a systematic framework for describing subcellular location. Velliste, Meel aut Porreca, Gregory aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Kluwer Academic Publishers, 1989 35(2003), 3 vom: 01. Nov., Seite 311-321 (DE-627)130761508 (DE-600)1000618-7 (DE-576)02508416X 0922-5773 nnns volume:35 year:2003 number:3 day:01 month:11 pages:311-321 https://doi.org/10.1023/B:VLSI.0000003028.71666.44 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_23 GBV_ILN_31 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2244 GBV_ILN_4307 GBV_ILN_4319 AR 35 2003 3 01 11 311-321 |
| allfieldsGer |
10.1023/B:VLSI.0000003028.71666.44 doi (DE-627)OLC2062086288 (DE-He213)B:VLSI.0000003028.71666.44-p DE-627 ger DE-627 rakwb eng 620 VZ Murphy, Robert F. verfasserin aut Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2003 Abstract The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. Central to that goal is the determination of the function of each protein that is present in a given cell type, and determining a protein's location within cells is critical to understanding its function. As large amounts of data become available from genome-wide determination of protein subcellular location, automated approaches to categorizing and comparing location patterns are urgently needed. Since subcellular location is most often determined using fluorescence microscopy, we have developed automated systems for interpreting the resulting images. We report here improved numeric features for describing such images that are fairly robust to image intensity binning and spatial resolution. We validate these features by using them to train neural networks that accurately recognize all major subcellular patterns with an accuracy higher than any previously reported. Having validated the features by using them for classification, we also demonstrate using them to create Subcellular Location Trees that group similar proteins and provide a systematic framework for describing subcellular location. Velliste, Meel aut Porreca, Gregory aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Kluwer Academic Publishers, 1989 35(2003), 3 vom: 01. Nov., Seite 311-321 (DE-627)130761508 (DE-600)1000618-7 (DE-576)02508416X 0922-5773 nnns volume:35 year:2003 number:3 day:01 month:11 pages:311-321 https://doi.org/10.1023/B:VLSI.0000003028.71666.44 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_23 GBV_ILN_31 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2244 GBV_ILN_4307 GBV_ILN_4319 AR 35 2003 3 01 11 311-321 |
| allfieldsSound |
10.1023/B:VLSI.0000003028.71666.44 doi (DE-627)OLC2062086288 (DE-He213)B:VLSI.0000003028.71666.44-p DE-627 ger DE-627 rakwb eng 620 VZ Murphy, Robert F. verfasserin aut Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2003 Abstract The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. Central to that goal is the determination of the function of each protein that is present in a given cell type, and determining a protein's location within cells is critical to understanding its function. As large amounts of data become available from genome-wide determination of protein subcellular location, automated approaches to categorizing and comparing location patterns are urgently needed. Since subcellular location is most often determined using fluorescence microscopy, we have developed automated systems for interpreting the resulting images. We report here improved numeric features for describing such images that are fairly robust to image intensity binning and spatial resolution. We validate these features by using them to train neural networks that accurately recognize all major subcellular patterns with an accuracy higher than any previously reported. Having validated the features by using them for classification, we also demonstrate using them to create Subcellular Location Trees that group similar proteins and provide a systematic framework for describing subcellular location. Velliste, Meel aut Porreca, Gregory aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Kluwer Academic Publishers, 1989 35(2003), 3 vom: 01. Nov., Seite 311-321 (DE-627)130761508 (DE-600)1000618-7 (DE-576)02508416X 0922-5773 nnns volume:35 year:2003 number:3 day:01 month:11 pages:311-321 https://doi.org/10.1023/B:VLSI.0000003028.71666.44 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_23 GBV_ILN_31 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2244 GBV_ILN_4307 GBV_ILN_4319 AR 35 2003 3 01 11 311-321 |
| language |
English |
| source |
Enthalten in Journal of VLSI signal processing systems for signal, image and video technology 35(2003), 3 vom: 01. Nov., Seite 311-321 volume:35 year:2003 number:3 day:01 month:11 pages:311-321 |
| sourceStr |
Enthalten in Journal of VLSI signal processing systems for signal, image and video technology 35(2003), 3 vom: 01. Nov., Seite 311-321 volume:35 year:2003 number:3 day:01 month:11 pages:311-321 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| dewey-raw |
620 |
| isfreeaccess_bool |
false |
| container_title |
Journal of VLSI signal processing systems for signal, image and video technology |
| authorswithroles_txt_mv |
Murphy, Robert F. @@aut@@ Velliste, Meel @@aut@@ Porreca, Gregory @@aut@@ |
| publishDateDaySort_date |
2003-11-01T00:00:00Z |
| hierarchy_top_id |
130761508 |
| dewey-sort |
3620 |
| id |
OLC2062086288 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2062086288</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504072307.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2003 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1023/B:VLSI.0000003028.71666.44</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2062086288</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)B:VLSI.0000003028.71666.44-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Murphy, Robert F.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2003</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Kluwer Academic Publishers 2003</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. Central to that goal is the determination of the function of each protein that is present in a given cell type, and determining a protein's location within cells is critical to understanding its function. As large amounts of data become available from genome-wide determination of protein subcellular location, automated approaches to categorizing and comparing location patterns are urgently needed. Since subcellular location is most often determined using fluorescence microscopy, we have developed automated systems for interpreting the resulting images. We report here improved numeric features for describing such images that are fairly robust to image intensity binning and spatial resolution. We validate these features by using them to train neural networks that accurately recognize all major subcellular patterns with an accuracy higher than any previously reported. Having validated the features by using them for classification, we also demonstrate using them to create Subcellular Location Trees that group similar proteins and provide a systematic framework for describing subcellular location.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Velliste, Meel</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Porreca, Gregory</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of VLSI signal processing systems for signal, image and video technology</subfield><subfield code="d">Kluwer Academic Publishers, 1989</subfield><subfield code="g">35(2003), 3 vom: 01. Nov., Seite 311-321</subfield><subfield code="w">(DE-627)130761508</subfield><subfield code="w">(DE-600)1000618-7</subfield><subfield code="w">(DE-576)02508416X</subfield><subfield code="x">0922-5773</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:35</subfield><subfield code="g">year:2003</subfield><subfield code="g">number:3</subfield><subfield code="g">day:01</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:311-321</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1023/B:VLSI.0000003028.71666.44</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2244</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4319</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">35</subfield><subfield code="j">2003</subfield><subfield code="e">3</subfield><subfield code="b">01</subfield><subfield code="c">11</subfield><subfield code="h">311-321</subfield></datafield></record></collection>
|
| author |
Murphy, Robert F. |
| spellingShingle |
Murphy, Robert F. ddc 620 Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images |
| authorStr |
Murphy, Robert F. |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)130761508 |
| format |
Article |
| dewey-ones |
620 - Engineering & allied operations |
| delete_txt_mv |
keep |
| author_role |
aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0922-5773 |
| topic_title |
620 VZ Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images |
| topic |
ddc 620 |
| topic_unstemmed |
ddc 620 |
| topic_browse |
ddc 620 |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Journal of VLSI signal processing systems for signal, image and video technology |
| hierarchy_parent_id |
130761508 |
| dewey-tens |
620 - Engineering |
| hierarchy_top_title |
Journal of VLSI signal processing systems for signal, image and video technology |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)130761508 (DE-600)1000618-7 (DE-576)02508416X |
| title |
Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images |
| ctrlnum |
(DE-627)OLC2062086288 (DE-He213)B:VLSI.0000003028.71666.44-p |
| title_full |
Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images |
| author_sort |
Murphy, Robert F. |
| journal |
Journal of VLSI signal processing systems for signal, image and video technology |
| journalStr |
Journal of VLSI signal processing systems for signal, image and video technology |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2003 |
| contenttype_str_mv |
txt |
| container_start_page |
311 |
| author_browse |
Murphy, Robert F. Velliste, Meel Porreca, Gregory |
| container_volume |
35 |
| class |
620 VZ |
| format_se |
Aufsätze |
| author-letter |
Murphy, Robert F. |
| doi_str_mv |
10.1023/B:VLSI.0000003028.71666.44 |
| dewey-full |
620 |
| title_sort |
robust numerical features for description and classification of subcellular location patterns in fluorescence microscope images |
| title_auth |
Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images |
| abstract |
Abstract The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. Central to that goal is the determination of the function of each protein that is present in a given cell type, and determining a protein's location within cells is critical to understanding its function. As large amounts of data become available from genome-wide determination of protein subcellular location, automated approaches to categorizing and comparing location patterns are urgently needed. Since subcellular location is most often determined using fluorescence microscopy, we have developed automated systems for interpreting the resulting images. We report here improved numeric features for describing such images that are fairly robust to image intensity binning and spatial resolution. We validate these features by using them to train neural networks that accurately recognize all major subcellular patterns with an accuracy higher than any previously reported. Having validated the features by using them for classification, we also demonstrate using them to create Subcellular Location Trees that group similar proteins and provide a systematic framework for describing subcellular location. © Kluwer Academic Publishers 2003 |
| abstractGer |
Abstract The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. Central to that goal is the determination of the function of each protein that is present in a given cell type, and determining a protein's location within cells is critical to understanding its function. As large amounts of data become available from genome-wide determination of protein subcellular location, automated approaches to categorizing and comparing location patterns are urgently needed. Since subcellular location is most often determined using fluorescence microscopy, we have developed automated systems for interpreting the resulting images. We report here improved numeric features for describing such images that are fairly robust to image intensity binning and spatial resolution. We validate these features by using them to train neural networks that accurately recognize all major subcellular patterns with an accuracy higher than any previously reported. Having validated the features by using them for classification, we also demonstrate using them to create Subcellular Location Trees that group similar proteins and provide a systematic framework for describing subcellular location. © Kluwer Academic Publishers 2003 |
| abstract_unstemmed |
Abstract The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. Central to that goal is the determination of the function of each protein that is present in a given cell type, and determining a protein's location within cells is critical to understanding its function. As large amounts of data become available from genome-wide determination of protein subcellular location, automated approaches to categorizing and comparing location patterns are urgently needed. Since subcellular location is most often determined using fluorescence microscopy, we have developed automated systems for interpreting the resulting images. We report here improved numeric features for describing such images that are fairly robust to image intensity binning and spatial resolution. We validate these features by using them to train neural networks that accurately recognize all major subcellular patterns with an accuracy higher than any previously reported. Having validated the features by using them for classification, we also demonstrate using them to create Subcellular Location Trees that group similar proteins and provide a systematic framework for describing subcellular location. © Kluwer Academic Publishers 2003 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_23 GBV_ILN_31 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2244 GBV_ILN_4307 GBV_ILN_4319 |
| container_issue |
3 |
| title_short |
Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images |
| url |
https://doi.org/10.1023/B:VLSI.0000003028.71666.44 |
| remote_bool |
false |
| author2 |
Velliste, Meel Porreca, Gregory |
| author2Str |
Velliste, Meel Porreca, Gregory |
| ppnlink |
130761508 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1023/B:VLSI.0000003028.71666.44 |
| up_date |
2024-07-03T13:41:11.802Z |
| _version_ |
1803565475354902528 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2062086288</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504072307.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2003 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1023/B:VLSI.0000003028.71666.44</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2062086288</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)B:VLSI.0000003028.71666.44-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Murphy, Robert F.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2003</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Kluwer Academic Publishers 2003</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. Central to that goal is the determination of the function of each protein that is present in a given cell type, and determining a protein's location within cells is critical to understanding its function. As large amounts of data become available from genome-wide determination of protein subcellular location, automated approaches to categorizing and comparing location patterns are urgently needed. Since subcellular location is most often determined using fluorescence microscopy, we have developed automated systems for interpreting the resulting images. We report here improved numeric features for describing such images that are fairly robust to image intensity binning and spatial resolution. We validate these features by using them to train neural networks that accurately recognize all major subcellular patterns with an accuracy higher than any previously reported. Having validated the features by using them for classification, we also demonstrate using them to create Subcellular Location Trees that group similar proteins and provide a systematic framework for describing subcellular location.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Velliste, Meel</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Porreca, Gregory</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of VLSI signal processing systems for signal, image and video technology</subfield><subfield code="d">Kluwer Academic Publishers, 1989</subfield><subfield code="g">35(2003), 3 vom: 01. Nov., Seite 311-321</subfield><subfield code="w">(DE-627)130761508</subfield><subfield code="w">(DE-600)1000618-7</subfield><subfield code="w">(DE-576)02508416X</subfield><subfield code="x">0922-5773</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:35</subfield><subfield code="g">year:2003</subfield><subfield code="g">number:3</subfield><subfield code="g">day:01</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:311-321</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1023/B:VLSI.0000003028.71666.44</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2244</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4319</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">35</subfield><subfield code="j">2003</subfield><subfield code="e">3</subfield><subfield code="b">01</subfield><subfield code="c">11</subfield><subfield code="h">311-321</subfield></datafield></record></collection>
|
| score |
7.3990145 |