Feature Selection and Classification Reveal Key lncRNAs for Multiple Cancers Long noncoding RNA (lncRNA) plays key roles in tumorigenesis. Misexpression of lncRNA can lead to changes in expression profiles of various target genes, which are involved in cancer initiation and progression. So, identifying key lncRNAs for a cancer would help develop the cancer therapy. Usually, to identify key lncRNAs for a cancer, expression profiles of lncRNAs for normal and cancer samples are required. But, this kind of data are not available for all cancers. In the present study, a computational framework is developed to identify cancer specific key lncRNAs using the lncRNA expression of cancer patients only. The framework consists of two stateoftheart feature selection techniques  Recursive Feature Elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO); and five machine learning models  Naive Bayes, KNearest Neighbor, Random Forest, Support Vector Machine, and Deep Neural Network. For experiment, expression values of lncRNAs for 8 cancers  BLCA, CESC, COAD, HNSC, KIRP, LGG, LIHC, and LUAD  from TCGA are used. The combined dataset consists of 3,656 patients with expression values of 12,309 lncRNAs. Important features or key lncRNAs are identified by using feature selection algorithms RFE and LASSO. Capability of these key lncRNAs in classifying 8 different cancers is checked by the performance of five classification models. This study identified 37 key lncRNAs that can classify 8 different cancer types with an accuracy ranging from 94% to 97%. Finally, survival analysis supports that the discovered key lncRNAs are capable of differentiating between highrisk and lowrisk patients.

LncRNAxe2x80x93disease association prediction through combining linear and nonlinear features with matrix factorization and deep learning techniques Long noncoding RNAs (lncRNAs) are the foundation for understanding mechanisms of many human diseases. Considering the limited number of known experimentally verified associations between lncRNAs and diseases, it is appealing to develop accurate and effective computational methods to identify lncRNAdisease associations. Conventional matrix factorizationbased methods cannot model complicated associations between lncRNAs and diseases. In this study, we propose a novel computational framework, through combining linear and nonlinear features, which is used for lncRNAdisease association prediction. In our model, a conventional matrix factorization method is applied to extract linear features between lncRNAs and diseases. Deep learning techniques (fully connected layers) are applied to extract nonlinear features between lncRNAs and diseases. Finally, linear and nonlinear features are fused to improve predictive performance. Compared to previous studies, our model can take advantages of the combination of linear and nonlinear features between lncRNAs and diseases, and thus can effectively identify potential lncRNAdisease associations. The results show that our method achieves stateoftheart performance in the leaveoneout crossvalidation. The source codes of our method can be found at https:github.comCSUBioGroupDMFLDA2.