TY - GEN
T1 - Classification of protein profiles using fuzzy clustering techniques
T2 - 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
AU - Karemore, Gopal
AU - Mullick, Jhinuk B.
AU - Sujatha, R.
AU - Nielsen, Mads
AU - Santhosh, C.
PY - 2010/12/1
Y1 - 2010/12/1
N2 - Present study has brought out a comparison of PCA and fuzzy clustering techniques in classifying protein profiles (chromatogram) of homogenates of different tissue origins: Ovarian, Cervix, Oral cancers, which were acquired using HPLC-LIF (High Performance Liquid Chromatography-Laser Induced Fluorescence) method developed in our laboratory. Study includes 11 chromatogram spectra each from oral, cervical, ovarian cancers as well as healthy volunteers. Generally multivariate analysis like PCA demands clear data that is devoid of day-to-day variation, artifacts due to experimental strategies, inherent uncertainty in pumping procedure which is very common activities during HPLC-LIF experiment. Under these circumstances we demonstrate how fuzzy clustering algorithm like Gath Geva followed by Sammon mapping outperform PCA mapping in classifying various cancers from healthy spectra with classification rate up to 95 % from 60%. Methods are validated using various clustering indexes and shows promising improvement in developing optical pathology like HPLC-LIF for early detection of various cancers in all uncertain conditions with high sensitivity and specificity.
AB - Present study has brought out a comparison of PCA and fuzzy clustering techniques in classifying protein profiles (chromatogram) of homogenates of different tissue origins: Ovarian, Cervix, Oral cancers, which were acquired using HPLC-LIF (High Performance Liquid Chromatography-Laser Induced Fluorescence) method developed in our laboratory. Study includes 11 chromatogram spectra each from oral, cervical, ovarian cancers as well as healthy volunteers. Generally multivariate analysis like PCA demands clear data that is devoid of day-to-day variation, artifacts due to experimental strategies, inherent uncertainty in pumping procedure which is very common activities during HPLC-LIF experiment. Under these circumstances we demonstrate how fuzzy clustering algorithm like Gath Geva followed by Sammon mapping outperform PCA mapping in classifying various cancers from healthy spectra with classification rate up to 95 % from 60%. Methods are validated using various clustering indexes and shows promising improvement in developing optical pathology like HPLC-LIF for early detection of various cancers in all uncertain conditions with high sensitivity and specificity.
UR - http://www.scopus.com/inward/record.url?scp=78650837619&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650837619&partnerID=8YFLogxK
U2 - 10.1109/IEMBS.2010.5627292
DO - 10.1109/IEMBS.2010.5627292
M3 - Conference contribution
AN - SCOPUS:78650837619
SN - 9781424441235
T3 - 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
SP - 6361
EP - 6364
BT - 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Y2 - 31 August 2010 through 4 September 2010
ER -
