Analysis of protein profiles using fuzzy clustering methods: case of malignant and normal cervical tissues

Gopal Raghunath Karemore; Sujatha Ukendt; Lavanya Rai; V.B Kartha; Santhosh C

Analysis of protein profiles using fuzzy clustering methods: case of malignant and normal cervical tissues

Gopal Raghunath Karemore, Sujatha Ukendt, Lavanya Rai, V.B Kartha, Santhosh C

Department of Computer Science

Abstract

The tissue protein profiles of healthy volunteers and volunteers with cervical cancer were

recorded using High Performance Liquid Chromatography combined with Laser Induced

Fluorescence technique (HPLC-LIF) developed in our lab. We analyzed the protein

profile data using different clustering methods for their classification followed by various

validation measures. The clustering algorithms used for the study were K- means, K-

medoid, Fuzzy C-means, Gustafson-Kessel, and Gath-Geva. The results presented in this

study conclude that the protein profiles of tissue samples recorded by using the HPLC-

LIF system and the data analyzed by clustering algorithms quite successfully classifies

them as belonging from normal and malignant conditions.

Original language	English
Publication date	2008
Number of pages	1
Publication status	Published - 2008
Event	The 27 Annual Convention of the IACR Networking Research to Applications & International Symposium on Frontiers in Functional Genomics - Ahmadabad, India Duration: 7 Feb 2008 → 9 Feb 2008 Conference number: 27

Conference

Conference	The 27 Annual Convention of the IACR Networking Research to Applications & International Symposium on Frontiers in Functional Genomics
Number	27
Country/Territory	India
City	Ahmadabad
Period	07/02/2008 → 09/02/2008

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Analysis of protein profiles using fuzzy clustering methods: case of malignant and normal cervical tissues . / Karemore, Gopal Raghunath; Ukendt, Sujatha; Rai, Lavanya et al.
2008. Poster session presented at The 27 Annual Convention of the IACR Networking Research to Applications & International Symposium on Frontiers in Functional Genomics, Ahmadabad, India.

Research output: Contribution to conference › Poster › Research

@conference{bfa61c10e18411ddb5fc000ea68e967b,

title = "Analysis of protein profiles using fuzzy clustering methods: case of malignant and normal cervical tissues ",

abstract = "The tissue protein profiles of healthy volunteers and volunteers with cervical cancer wererecorded using High Performance Liquid Chromatography combined with Laser InducedFluorescence technique (HPLC-LIF) developed in our lab. We analyzed the proteinprofile data using different clustering methods for their classification followed by variousvalidation measures. The clustering algorithms used for the study were K- means, K-medoid, Fuzzy C-means, Gustafson-Kessel, and Gath-Geva. The results presented in thisstudy conclude that the protein profiles of tissue samples recorded by using the HPLC-LIF system and the data analyzed by clustering algorithms quite successfully classifiesthem as belonging from normal and malignant conditions.",

author = "Karemore, {Gopal Raghunath} and Sujatha Ukendt and Lavanya Rai and V.B Kartha and Santhosh C",

year = "2008",

language = "English",

note = "The 27 Annual Convention of the IACR Networking Research to Applications & International Symposium on Frontiers in Functional Genomics ; Conference date: 07-02-2008 Through 09-02-2008",

}

TY - CONF

T1 - Analysis of protein profiles using fuzzy clustering methods

T2 - The 27 Annual Convention of the IACR Networking Research to Applications & International Symposium on Frontiers in Functional Genomics

AU - Karemore, Gopal Raghunath

AU - Ukendt, Sujatha

AU - Rai, Lavanya

AU - Kartha, V.B

AU - C, Santhosh

N1 - Conference code: 27

PY - 2008

Y1 - 2008

N2 - The tissue protein profiles of healthy volunteers and volunteers with cervical cancer wererecorded using High Performance Liquid Chromatography combined with Laser InducedFluorescence technique (HPLC-LIF) developed in our lab. We analyzed the proteinprofile data using different clustering methods for their classification followed by variousvalidation measures. The clustering algorithms used for the study were K- means, K-medoid, Fuzzy C-means, Gustafson-Kessel, and Gath-Geva. The results presented in thisstudy conclude that the protein profiles of tissue samples recorded by using the HPLC-LIF system and the data analyzed by clustering algorithms quite successfully classifiesthem as belonging from normal and malignant conditions.

AB - The tissue protein profiles of healthy volunteers and volunteers with cervical cancer wererecorded using High Performance Liquid Chromatography combined with Laser InducedFluorescence technique (HPLC-LIF) developed in our lab. We analyzed the proteinprofile data using different clustering methods for their classification followed by variousvalidation measures. The clustering algorithms used for the study were K- means, K-medoid, Fuzzy C-means, Gustafson-Kessel, and Gath-Geva. The results presented in thisstudy conclude that the protein profiles of tissue samples recorded by using the HPLC-LIF system and the data analyzed by clustering algorithms quite successfully classifiesthem as belonging from normal and malignant conditions.

M3 - Poster

Y2 - 7 February 2008 through 9 February 2008

ER -

Analysis of protein profiles using fuzzy clustering methods: case of malignant and normal cervical tissues

Abstract

Conference

UN SDGs

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