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  MICROARRAY MEETING 2002: NEW DEVELOPMENTS IN MUTATION DETECTION AND GENE EXPRESSION
Segrate, MI (Italy), April 12, 2002


Minerva Biotecnologica 2002 Dicembre;14(3-4):281-90

lingua: Inglese

Dis­joint PCA mod­els for mark­er iden­tifi­ca­tion and clas­sifi­ca­tion of can­cer ­types ­using ­gene expres­sion ­data

Bicciato S., Luchini A., Di Bello C.

Depart­ment of Chem­i­cal Pro­cess Engi­neer­ing, Uni­ver­sity of Pado­va, Pado­va


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Back­ground. The par­allel mon­i­tor­ing of the expres­sion pro­files of thou­sands of ­genes ­seems par­tic­u­lar­ly prom­is­ing for a deep­er under­stand­ing of can­cer biol­o­gy and to iden­ti­fy molec­u­lar sig­na­tures sup­port­ing the his­to­log­i­cal clas­sifi­ca­tion ­schemes of neo­plas­tic spec­i­mens. How­ev­er, molec­u­lar diag­nos­tic ­based on micro­ar­ray ­data ­presents ­major chal­leng­es due to the com­plex, mul­ti­class ­nature and to the over­whelm­ing num­ber of var­i­ables char­ac­ter­iz­ing ­gene expres­sion data­bas­es of mul­ti­ple ­tumor sam­ples. ­Thus, the devel­op­ment of mul­ti­class clas­sifi­ca­tion ­schemes and of mark­er selec­tion meth­ods, ­that ­allow the simul­ta­ne­ous clas­sifi­ca­tion of mul­ti­ple ­tumor ­types and the iden­tifi­ca­tion of ­those ­genes ­that are ­most like­ly to con­fer ­high clas­sifi­ca­tion accu­ra­cy, is of par­a­mount impor­tance.
Meth­ods. A com­pu­ta­tion­al pro­ce­dure for mark­er iden­tifi­ca­tion and clas­sifi­ca­tion of mul­ti­class ­gene expres­sion ­data ­through the appli­ca­tion of dis­joint prin­ci­pal com­po­nent mod­els, ­based on the ­Soft Inde­pen­dent Mod­el­ing of ­Class Anal­o­gy ­approach (SIM­CA), is ­described. The iden­ti­fied fea­tures rep­re­sent a ration­al and dimen­sion­al­ly ­reduced ­base for under­stand­ing the ­basic biol­o­gy of dis­eas­es, defin­ing tar­gets of ther­a­peu­tic inter­ven­tion, and devel­op­ing diag­nos­tic ­tools for the iden­tifi­ca­tion and clas­sifi­ca­tion of mul­ti­ple path­o­log­i­cal ­states.
­Results. The meth­od has ­been test­ed on 2 dif­fer­ent micro­ar­ray ­data ­sets ­obtained ­from var­i­ous ­human ­tumor sam­ples: i) ­acute leu­ke­mi­as, and ii) ­small ­round ­blue-­cell ­tumors.
Con­clu­sions. The ­results dem­on­strate ­that the dis­joint PCA mod­el­ing pro­ce­dure ­allows the iden­tifi­ca­tion of spe­cif­ic phe­no­type mark­ers and pro­vides the assign­ment to mul­ti­ple class­es for pre­vi­ous­ly ­unseen instanc­es.

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