Exploiting blind matrix decomposition techniques to identify diagnostic marker genes

Reinhard Schachtner; Dominik Lutter; Fabian J. Theis; Elmar W. Lang; Ana Maria Tomé; Gerd Schmitz

doi:10.1007/978-3-540-74695-9_9

Exploiting blind matrix decomposition techniques to identify diagnostic marker genes

Reinhard Schachtner, Dominik Lutter, Fabian J. Theis, Elmar W. Lang, Ana Maria Tomé, Gerd Schmitz

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

Abstract

Exploratory matrix factorization methods like ICA and LNMF are applied to identify marker genes and classify gene expression data sets into different categories for diagnostic purposes or group genes into functional categories for further investigation of related regulatory pathways. Gene expression levels of either human breast cancer (HBC) cell lines [5] mediating bone metastasis or cell lines from Niemann Pick C patients monitoring monocyte - macrophage differentiation are considered.

Original language	English
Title of host publication	Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings
Publisher	Springer Verlag
Pages	80-89
Number of pages	10
Edition	PART 2
ISBN (Print)	9783540746935
DOIs	https://doi.org/10.1007/978-3-540-74695-9_9
State	Published - 2007
Externally published	Yes
Event	17th International Conference on Artificial Neural Networks, ICANN 2007 - Porto, Portugal Duration: 9 Sep 2007 → 13 Sep 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	4669 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Artificial Neural Networks, ICANN 2007
Country/Territory	Portugal
City	Porto
Period	9/09/07 → 13/09/07

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-540-74695-9_9

Cite this

Schachtner, R., Lutter, D., Theis, F. J., Lang, E. W., Tomé, A. M., & Schmitz, G. (2007). Exploiting blind matrix decomposition techniques to identify diagnostic marker genes. In Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings (PART 2 ed., pp. 80-89). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4669 LNCS, No. PART 2). Springer Verlag. https://doi.org/10.1007/978-3-540-74695-9_9

Schachtner, Reinhard ; Lutter, Dominik ; Theis, Fabian J. et al. / Exploiting blind matrix decomposition techniques to identify diagnostic marker genes. Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings. PART 2. ed. Springer Verlag, 2007. pp. 80-89 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "Exploiting blind matrix decomposition techniques to identify diagnostic marker genes",

abstract = "Exploratory matrix factorization methods like ICA and LNMF are applied to identify marker genes and classify gene expression data sets into different categories for diagnostic purposes or group genes into functional categories for further investigation of related regulatory pathways. Gene expression levels of either human breast cancer (HBC) cell lines [5] mediating bone metastasis or cell lines from Niemann Pick C patients monitoring monocyte - macrophage differentiation are considered.",

author = "Reinhard Schachtner and Dominik Lutter and Theis, {Fabian J.} and Lang, {Elmar W.} and Tom{\'e}, {Ana Maria} and Gerd Schmitz",

year = "2007",

doi = "10.1007/978-3-540-74695-9_9",

language = "English",

isbn = "9783540746935",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

number = "PART 2",

pages = "80--89",

booktitle = "Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings",

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note = "17th International Conference on Artificial Neural Networks, ICANN 2007 ; Conference date: 09-09-2007 Through 13-09-2007",

}

Schachtner, R, Lutter, D, Theis, FJ, Lang, EW, Tomé, AM & Schmitz, G 2007, Exploiting blind matrix decomposition techniques to identify diagnostic marker genes. in Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 4669 LNCS, Springer Verlag, pp. 80-89, 17th International Conference on Artificial Neural Networks, ICANN 2007, Porto, Portugal, 9/09/07. https://doi.org/10.1007/978-3-540-74695-9_9

Exploiting blind matrix decomposition techniques to identify diagnostic marker genes. / Schachtner, Reinhard; Lutter, Dominik; Theis, Fabian J. et al.
Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings. PART 2. ed. Springer Verlag, 2007. p. 80-89 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4669 LNCS, No. PART 2).

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

TY - GEN

T1 - Exploiting blind matrix decomposition techniques to identify diagnostic marker genes

AU - Schachtner, Reinhard

AU - Lutter, Dominik

AU - Theis, Fabian J.

AU - Lang, Elmar W.

AU - Tomé, Ana Maria

AU - Schmitz, Gerd

PY - 2007

Y1 - 2007

N2 - Exploratory matrix factorization methods like ICA and LNMF are applied to identify marker genes and classify gene expression data sets into different categories for diagnostic purposes or group genes into functional categories for further investigation of related regulatory pathways. Gene expression levels of either human breast cancer (HBC) cell lines [5] mediating bone metastasis or cell lines from Niemann Pick C patients monitoring monocyte - macrophage differentiation are considered.

AB - Exploratory matrix factorization methods like ICA and LNMF are applied to identify marker genes and classify gene expression data sets into different categories for diagnostic purposes or group genes into functional categories for further investigation of related regulatory pathways. Gene expression levels of either human breast cancer (HBC) cell lines [5] mediating bone metastasis or cell lines from Niemann Pick C patients monitoring monocyte - macrophage differentiation are considered.

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U2 - 10.1007/978-3-540-74695-9_9

DO - 10.1007/978-3-540-74695-9_9

M3 - Conference contribution

AN - SCOPUS:38149066889

SN - 9783540746935

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 80

EP - 89

BT - Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings

PB - Springer Verlag

T2 - 17th International Conference on Artificial Neural Networks, ICANN 2007

Y2 - 9 September 2007 through 13 September 2007

ER -

Schachtner R, Lutter D, Theis FJ, Lang EW, Tomé AM, Schmitz G. Exploiting blind matrix decomposition techniques to identify diagnostic marker genes. In Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings. PART 2 ed. Springer Verlag. 2007. p. 80-89. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-540-74695-9_9

Exploiting blind matrix decomposition techniques to identify diagnostic marker genes

Abstract

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UN SDGs

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