IEEE J Biomed Health Inform - Exploring robust diagnostic signatures for cutaneous melanoma utilizing genetic and imaging data.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ perform(1367) use(1326) method(1137) }
{ featur(3375) classif(2383) classifi(1994) }
{ process(1125) use(805) approach(778) }
{ imag(2675) segment(2577) method(1081) }
{ care(1570) inform(1187) nurs(1089) }
{ patient(2315) diseas(1263) diabet(1191) }
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{ state(1844) use(1261) util(961) }
{ survey(1388) particip(1329) question(1065) }
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{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
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{ data(2317) use(1299) case(1017) }
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{ group(2977) signific(1463) compar(1072) }
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{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
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{ activ(1452) weight(1219) physic(1104) }
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Resumo

Multimodal data combined in an integrated dataset can be used to aim the identification of instrumental biological actions that trigger the development of a disease. In this paper, we use an integrated dataset related to cutaneous melanoma that fuses two separate sets providing complementary information (gene expression profiling and imaging). Our first goal is to select a subset of genes that comprise candidate genetic biomarkers. The derived gene signature is then utilized in order to select imaging features, which characterize disease at a macroscopic level, presenting the highest, mutual information content to the selected genes. Using information gain ratio measurements and exploration of the gene ontology tree, we identified a set of 32 uncorrelated genes with a pivotal role as regards molecular regulation of melanoma, which expression across samples correlates highly with the different pathological states. These genes steered the selection of a subset of uncorrelated imaging features based on their ranking according to mutual information measurements to the selected gene expression values. Selected genes and imaging features were used to train various classifiers that could generalize well when discriminating malignant from benign melanoma samples. Results on the selection on imaging features and classification were compared to feature selection based on a straight forward statistical selection and a stochastic-based methodology. Genes in the backstage of low-level biological processes showed to carry higher information content than the macroscopic imaging features.

Resumo Limpo

multimod data combin integr dataset can use aim identif instrument biolog action trigger develop diseas paper use integr dataset relat cutan melanoma fuse two separ set provid complementari inform gene express profil imag first goal select subset gene compris candid genet biomark deriv gene signatur util order select imag featur character diseas macroscop level present highest mutual inform content select gene use inform gain ratio measur explor gene ontolog tree identifi set uncorrel gene pivot role regard molecular regul melanoma express across sampl correl high differ patholog state gene steer select subset uncorrel imag featur base rank accord mutual inform measur select gene express valu select gene imag featur use train various classifi general well discrimin malign benign melanoma sampl result select imag featur classif compar featur select base straight forward statist select stochasticbas methodolog gene backstag lowlevel biolog process show carri higher inform content macroscop imag featur

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