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Titolo:
Identifying marker genes in transcription profiling data using a mixture of feature relevance experts
Autore:
Chow, ML; Moler, EJ; Mian, IS;
Indirizzi:
Univ Calif Berkeley, Lawrence Berkeley Lab, Div Cell & Mol Biol, Div Life Sci,Radiat Biol & Environm Toxicol Grp, Berkeley, CA 94720 USA Univ Calif Berkeley Berkeley CA USA 94720 col Grp, Berkeley, CA 94720 USA Chiron Corp, Emeryville, CA 94608 USA Chiron Corp Emeryville CA USA 94608Chiron Corp, Emeryville, CA 94608 USA Gene Log Inc, Berkeley, CA 94704 USA Gene Log Inc Berkeley CA USA 94704Gene Log Inc, Berkeley, CA 94704 USA
Titolo Testata:
PHYSIOLOGICAL GENOMICS
fascicolo: 2, volume: 5, anno: 2001,
pagine: 99 - 111
SICI:
1094-8341(20010308)5:2<99:IMGITP>2.0.ZU;2-G
Fonte:
ISI
Lingua:
ENG
Soggetto:
SELF-ORGANIZING MAPS; EXPRESSION DATA; PATTERNS; DIFFERENTIATION; SELENOPROTEINS;
Keywords:
marker genes; mixture of experts; support vector machines; adipsin; cystatin C; azurocidin;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Citazioni:
24
Recensione:
Indirizzi per estratti:
Indirizzo: Mian, IS Univ Calif Berkeley, Lawrence Berkeley Lab, Div Cell & Mol Biol, Div Life Sci,Radiat Biol & Environm Toxicol Grp, MS 74-197,1 Cyclotron Rd, Berkeley,CA 94720 USA Univ Calif Berkeley MS 74-197,1 Cyclotron Rd BerkeleyCA USA 94720
Citazione:
M.L. Chow et al., "Identifying marker genes in transcription profiling data using a mixture of feature relevance experts", PHYSIOL GEN, 5(2), 2001, pp. 99-111

Abstract

Transcription profiling experiments permit the expression levels of many genes to be measured simultaneously. Given profiling data from two types of samples, genes that most distinguish the samples (marker genes) are good candidates for subsequent in-depth experimental studies and developing decision support systems for diagnosis, prognosis, and monitoring. This work proposes a mixture of feature relevance experts as a method for identifying marker genes and illustrates the idea using published data from samples labeled as acute lymphoblastic and myeloid leukemia (ALL, AML). A feature relevance expert implements an algorithm that calculates how well a gene distinguishes samples, reorders genes according to this relevance measure, and uses a supervised learning method [here, support vector machines (SVMs)] to determine the generalization performances of different nested gene subsets. Themixture of three feature relevance experts examined implement two existingand one novel feature relevance measures. For each expert, a gene subset consisting of the top 50 genes distinguished ALL from AML samples as completely as all 7,070 genes. The 125 genes at the union of the top 50s are plausible markers for a prototype decision support system. Chromosomal aberration and other data support the prediction that the three genes at the intersection of the top 50s, cystatin C, azurocidin, and adipsin, are good targetsfor investigating the basic biology of ALL/AML. The same data were employed to identify markers that distinguish samples based on their labels of T cell/B cell, peripheral blood/bone marrow, and male/female. Selenoprotein W may discriminate T cells from B cells. Results from analysis of transcription profiling data from tumor/nontumor colon adenocarcinoma samples support the general utility of the aforementioned approach. Theoretical issues suchas choosing SVM kernels and their parameters, training and evaluating feature relevance experts, and the impact of potentially mislabeled samples on marker identification (feature selection) are discussed.

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Documento generato il 07/04/20 alle ore 22:48:47