@article{ShaoBjaanaesHellandetal., author = {Shao, Borong and Bjaanaes, Maria and Helland, Aslaug and Sch{\"u}tte, Christof and Conrad, Tim}, title = {EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma}, series = {PLOS ONE}, volume = {14}, journal = {PLOS ONE}, number = {1}, doi = {10.1371/journal.pone.0204186}, abstract = {Various feature selection algorithms have been proposed to identify cancer prognostic biomarkers. In recent years, however, their reproducibility is criticized. The performance of feature selection algorithms is shown to be affected by the datasets, underlying networks and evaluation metrics. One of the causes is the curse of dimensionality, which makes it hard to select the features that generalize well on independent data. Even the integration of biological networks does not mitigate this issue because the networks are large and many of their components are not relevant for the phenotype of interest. With the availability of multi-omics data, integrative approaches are being developed to build more robust predictive models. In this scenario, the higher data dimensions create greater challenges. We proposed a phenotype relevant network-based feature selection (PRNFS) framework and demonstrated its advantages in lung cancer prognosis prediction. We constructed cancer prognosis relevant networks based on epithelial mesenchymal transition (EMT) and integrated them with different types of omics data for feature selection. With less than 2.5\% of the total dimensionality, we obtained EMT prognostic signatures that achieved remarkable prediction performance (average AUC values above 0.8), very significant sample stratifications, and meaningful biological interpretations. In addition to finding EMT signatures from different omics data levels, we combined these single-omics signatures into multi-omics signatures, which improved sample stratifications significantly. Both single- and multi-omics EMT signatures were tested on independent multi-omics lung cancer datasets and significant sample stratifications were obtained.}, language = {en} } @inproceedings{ShaoConrad, author = {Shao, Borong and Conrad, Tim}, title = {Epithelial Mesenchymal Transition Regulatory Network-based Feature Selection in Lung Cancer Prognosis Prediction}, series = {Lecture Notes in Computer Science (LNCS)}, volume = {9656}, booktitle = {Lecture Notes in Computer Science (LNCS)}, doi = {10.1007/978-3-319-31744-1_13}, pages = {1235 -- 146}, abstract = {Feature selection technique is often applied in identifying cancer prognosis biomarkers. However, many feature selection methods are prone to over-fitting or poor biological interpretation when applied on biological high-dimensional data. Network-based feature selection and data integration approaches are proposed to identify more robust biomarkers. We conducted experiments to investigate the advantages of the two approaches using epithelial mesenchymal transition regulatory network, which is demonstrated as highly relevant to cancer prognosis. We obtained data from The Cancer Genome Atlas. Prognosis prediction was made using Support Vector Machine. Under our experimental settings, the results showed that network-based features gave significantly more accurate predictions than individual molecular features, and features selected from integrated data (RNA-Seq and micro-RNA data) gave significantly more accurate predictions than features selected from single source data (RNA-Seq data). Our study indicated that biological network-based feature transformation and data integration are two useful approaches to identify robust cancer biomarkers.}, language = {en} } @article{ShaoCannistraciConrad, author = {Shao, Borong and Cannistraci, Carlo Vittorio and Conrad, Tim}, title = {Epithelial Mesenchymal Transition Network-Based Feature Engineering in Lung Adenocarcinoma Prognosis Prediction Using Multiple Omic Data}, series = {Genomics and Computational Biology}, volume = {3}, journal = {Genomics and Computational Biology}, number = {3}, doi = {10.18547/gcb.2017.vol3.iss3.e57}, pages = {e57}, abstract = {Epithelial mesenchymal transition (EMT) process has been shown as highly relevant to cancer prognosis. However, although different biological network-based biomarker identification methods have been proposed to predict cancer prognosis, EMT network has not been directly used for this purpose. In this study, we constructed an EMT regulatory network consisting of 87 molecules and tried to select features that are useful for prognosis prediction in Lung Adenocarcinoma (LUAD). To incorporate multiple molecular profiles, we obtained four types of molecular data including mRNA-Seq, copy number alteration (CNA), DNA methylation, and miRNA-Seq data from The Cancer Genome Atlas. The data were mapped to the EMT network in three alternative ways: mRNA-Seq and miRNA-Seq, DNA methylation, and CNA and miRNA-Seq. Each mapping was employed to extract five different sets of features using discretization and network-based biomarker identification methods. Each feature set was then used to predict prognosis with SVM and logistic regression classifiers. We measured the prediction accuracy with AUC and AUPR values using 10 times 10-fold cross validation. For a more comprehensive evaluation, we also measured the prediction accuracies of clinical features, EMT plus clinical features, randomly picked 87 molecules from each data mapping, and using all molecules from each data type. Counter-intuitively, EMT features do not always outperform randomly selected features and the prediction accuracies of the five feature sets are mostly not significantly different. Clinical features are shown to give the highest prediction accuracies. In addition, the prediction accuracies of both EMT features and random features are comparable as using all features (more than 17,000) from each data type.}, language = {en} } @article{ConradShao, author = {Conrad, Tim and Shao, Borong}, title = {Are NoSQL data stores useful for bioinformatics researchers?}, series = {International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC)}, volume = {3}, journal = {International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC)}, number = {3}, doi = {10.17762/ijritcc2321-8169.1503176}, pages = {1704 -- 1708}, language = {en} } @article{BorongCannistraciConrad, author = {Borong, Shao and Cannistraci, Carlo and Conrad, Tim}, title = {Epithelial Mesenchymal Transition Network-based Feature Engineering in Lung Adenocarcinoma Prognosis Prediction Using Multiple Omic Data}, series = {Genomics and Computational Biology}, volume = {3}, journal = {Genomics and Computational Biology}, number = {3}, doi = {http://dx.doi.org/10.18547/gcb.2017.vol3.iss3.e57}, pages = {1 -- 13}, abstract = {Epithelial mesenchymal transition (EMT) process has been shown as highly relevant to cancer prognosis. However, although different biological network-based biomarker identification methods have been proposed to predict cancer prognosis, EMT network has not been directly used for this purpose. In this study, we constructed an EMT regulatory network consisting of 87 molecules and tried to select features that are useful for prognosis prediction in Lung Adenocarcinoma (LUAD). To incorporate multiple molecular profiles, we obtained four types of molecular data including mRNA-Seq, copy number alteration (CNA), DNA methylation, and miRNA-Seq data from The Cancer Genome Atlas. The data were mapped to the EMT network in three alternative ways: mRNA-Seq and miRNA-Seq, DNA methylation, and CNA and miRNA-Seq. Each mapping was employed to extract five different sets of features using discretization and network-based biomarker identification methods. Each feature set was then used to predict prognosis with SVM and logistic regression classifiers. We measured the prediction accuracy with AUC and AUPR values using 10 times 10-fold cross validation. For a more comprehensive evaluation, we also measured the prediction accuracies of clinical features, EMT plus clinical features, randomly picked 87 molecules from each data mapping, and using all molecules from each data type. Counter-intuitively, EMT features do not always outperform randomly selected features and the prediction accuracies of the five feature sets are mostly not significantly different. Clinical features are shown to give the highest prediction accuracies. In addition, the prediction accuracies of both EMT features and random features are comparable as using all features (more than 17,000) from each data type.}, language = {en} }