Analysis and visualization of spatial proteomic data for tissue characterization

C. Fuchsberger; H. Hübl; G. Schäfer; A. Pelzer; G. Bartsch; H. Klocker; N. Barbarini; R. Bellazzi; W. Wieder; G. Bonn

doi:10.1109/CBMS.2008.119

Analysis and visualization of spatial proteomic data for tissue characterization

C. Fuchsberger, H. Hübl, G. Schäfer, A. Pelzer, G. Bartsch, H. Klocker, N. Barbarini, R. Bellazzi, W. Wieder, G. Bonn

Istituti Clinici Scientifici Maugeri Spa – Società Benefit

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Spatial proteomic profiling of tissue sections provides in situ molecular analysis of proteins and peptides. Analysis and visualization of these high-dimensional data cubes is challenging. We present a methodology for this task based on a novel developed algorithm for the feature identification and reduction step. To show the validity of our approach, we analyzed prostate cancer tissue sections with an adapted kernel-density based clustering algorithm.

Original language	English
Title of host publication	Proceedings - IEEE Symposium on Computer-Based Medical Systems
Pages	185-190
Number of pages	6
DOIs	https://doi.org/10.1109/CBMS.2008.119
Publication status	Published - 2008
Event	21st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2008 - Jyvaskyla, Finland Duration: Jun 17 2008 → Jun 19 2008

Other

Other	21st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2008
Country/Territory	Finland
City	Jyvaskyla
Period	6/17/08 → 6/19/08

ASJC Scopus subject areas

Engineering(all)

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10.1109/CBMS.2008.119

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Fuchsberger, C, Hübl, H, Schäfer, G, Pelzer, A, Bartsch, G, Klocker, H, Barbarini, N, Bellazzi, R, Wieder, W & Bonn, G 2008, Analysis and visualization of spatial proteomic data for tissue characterization. in Proceedings - IEEE Symposium on Computer-Based Medical Systems., 4561984, pp. 185-190, 21st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2008, Jyvaskyla, Finland, 6/17/08. https://doi.org/10.1109/CBMS.2008.119

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abstract = "Spatial proteomic profiling of tissue sections provides in situ molecular analysis of proteins and peptides. Analysis and visualization of these high-dimensional data cubes is challenging. We present a methodology for this task based on a novel developed algorithm for the feature identification and reduction step. To show the validity of our approach, we analyzed prostate cancer tissue sections with an adapted kernel-density based clustering algorithm.",

author = "C. Fuchsberger and H. H{\"u}bl and G. Sch{\"a}fer and A. Pelzer and G. Bartsch and H. Klocker and N. Barbarini and R. Bellazzi and W. Wieder and G. Bonn",

year = "2008",

doi = "10.1109/CBMS.2008.119",

language = "English",

isbn = "9780769531656",

pages = "185--190",

booktitle = "Proceedings - IEEE Symposium on Computer-Based Medical Systems",

note = "21st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2008 ; Conference date: 17-06-2008 Through 19-06-2008",

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T1 - Analysis and visualization of spatial proteomic data for tissue characterization

AU - Fuchsberger, C.

AU - Hübl, H.

AU - Schäfer, G.

AU - Pelzer, A.

AU - Bartsch, G.

AU - Klocker, H.

AU - Barbarini, N.

AU - Bellazzi, R.

AU - Wieder, W.

AU - Bonn, G.

PY - 2008

Y1 - 2008

N2 - Spatial proteomic profiling of tissue sections provides in situ molecular analysis of proteins and peptides. Analysis and visualization of these high-dimensional data cubes is challenging. We present a methodology for this task based on a novel developed algorithm for the feature identification and reduction step. To show the validity of our approach, we analyzed prostate cancer tissue sections with an adapted kernel-density based clustering algorithm.

AB - Spatial proteomic profiling of tissue sections provides in situ molecular analysis of proteins and peptides. Analysis and visualization of these high-dimensional data cubes is challenging. We present a methodology for this task based on a novel developed algorithm for the feature identification and reduction step. To show the validity of our approach, we analyzed prostate cancer tissue sections with an adapted kernel-density based clustering algorithm.

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U2 - 10.1109/CBMS.2008.119

DO - 10.1109/CBMS.2008.119

M3 - Conference contribution

AN - SCOPUS:51849152033

SN - 9780769531656

SP - 185

EP - 190

BT - Proceedings - IEEE Symposium on Computer-Based Medical Systems

T2 - 21st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2008

Y2 - 17 June 2008 through 19 June 2008

ER -

Analysis and visualization of spatial proteomic data for tissue characterization

Abstract

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