Integration of High-Volume Molecular and Imaging Data for Composite Biomarker Discovery in the Study of Melanoma

Abstract

In this work the effects of simple imputations are studied, regarding the integration of multimodal data originating from different patients. Two separate datasets of cutaneous melanoma are used, an image analysis (dermoscopy) dataset together with a transcriptomic one, specifically DNA microarrays. Each modality is related to a different set of patients, and four imputation methods are employed to the formation of a unified, integrative dataset. The application of backward selection together with ensemble classifiers (random forests), followed by principal components analysis and linear discriminant analysis, illustrates the implication of the imputations on feature selection and dimensionality reduction methods. The results suggest that the expansion of the feature space through the data integration, achieved by the exploitation of imputation schemes in general, aids the classification task, imparting stability as regards the derivation of putative classifiers. In particular, although the biased imputation methods increase significantly the predictive performance and the class discrimination of the datasets, they still contribute to the study of prominent features and their relations. The fusion of separate datasets, which provide a multimodal description of the same pathology, represents an innovative, promising avenue, enhancing robust composite biomarker derivation and promoting the interpretation of the biomedical problem studied.

A post-method condition analysis of using ensemble machine learning for cancer prognosis and diagnosis: a systematic review

“…Two approaches including a combination of data (COD) and the combination of interpretations (COI) were exploited for feature integration. COD is applied before classification, and it aggregates features from each source for producing a single feature vector, but in COI, 9 independent classifications are done based on the individual feature subsets using a proper voting mechanism [34], and involve aggregating outputs, so it uses algebraic combiners for decision-making strategy. In another study that was done in related to prostate cancer has been told that COD methods are more optimal [35].…”

A Post-Method Condition Analysis of Using Ensemble Machine Learning for Cancer Prognosis and Diagnosis: a systematic review

“…Two approaches including a combination of data (COD) and the combination of interpretations (COI) were exploited for feature integration. COD is applied before classification, and it aggregates features from each source for producing a single feature vector, but in COI, independent classifications are done based on the individual feature subsets using a proper voting mechanism [34], and involve aggregating outputs, so it uses algebraic combiners for decision-making strategy. In another study that was done in related to prostate cancer has been told that COD methods are more optimal [35].…”

Integrative Bioinformatic Analysis of a Greek Epidemiological Cohort Provides Insight into the Pathogenesis of Primary Cutaneous Melanoma

“…Transcriptomic analyses among different groups allow the exploration and identification of alterations in gene expression profiles between them. The data used in this section were previously analyzed in [16]. Briefly, the microarray dataset was taken from the Gene Expression Omnibus (GEO) [17], with accession number GDS1375.…”

“…The transcriptomic analysis from [16] revealed 1425 unique differentially expressed genes. Enrichment analysis showed 36 statistically significant biological processes (p-value < 0.05), which are presented in Table 4.…”