Comput Methods Programs Biomed - Application of clustering analyses to the diagnosis of Huntington disease in mice and other diseases with well-defined group boundaries.

Tópicos

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{ patient(2837) hospit(1953) medic(668) }
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{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
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{ survey(1388) particip(1329) question(1065) }
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{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
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Resumo

Nuclear magnetic resonance (NMR) spectroscopy has emerged as a technology that can provide metabolite information within organ systems in vivo. In this study, we introduced a new method of employing a clustering algorithm to develop a diagnostic model that can differentially diagnose a single unknown subject in a disease with well-defined group boundaries. We used three tests to assess the suitability and the accuracy required for diagnostic purposes of the four clustering algorithms we investigated (K-means, Fuzzy, Hierarchical, and Medoid Partitioning). To accomplish this goal, we studied the striatal metabolomic profile of R6/2 Huntington disease (HD) transgenic mice and that of wild type (WT) mice using high field in vivo proton NMR spectroscopy (9.4T). We tested all four clustering algorithms (1) with the original R6/2 HD mice and WT mice, (2) with unknown mice, whose status had been determined via genotyping, and (3) with the ability to separate the original R6/2 mice into the two age subgroups (8 and 12 weeks old). Only our diagnostic models that employed ROC-supervised Fuzzy, unsupervised Fuzzy, and ROC-supervised K-means Clustering passed all three stringent tests with 100% accuracy, indicating that they may be used for diagnostic purposes.

Resumo Limpo

nuclear magnet reson nmr spectroscopi emerg technolog can provid metabolit inform within organ system vivo studi introduc new method employ cluster algorithm develop diagnost model can differenti diagnos singl unknown subject diseas welldefin group boundari use three test assess suitabl accuraci requir diagnost purpos four cluster algorithm investig kmean fuzzi hierarch medoid partit accomplish goal studi striatal metabolom profil r huntington diseas hd transgen mice wild type wt mice use high field vivo proton nmr spectroscopi t test four cluster algorithm origin r hd mice wt mice unknown mice whose status determin via genotyp abil separ origin r mice two age subgroup week old diagnost model employ rocsupervis fuzzi unsupervis fuzzi rocsupervis kmean cluster pass three stringent test accuraci indic may use diagnost purpos

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