Abstract：
BACKGROUND:Deciphering the meaning of the human DNA is an outstanding goal which would revolutionize medicine and our way for treating diseases. In recent years, non-coding RNAs have attracted much attention and shown to be functional in part. Yet the importance of these RNAs especially for higher biological functions remains under investigation. METHODS:In this paper, we analyze RNA-seq data, including non-coding and protein coding RNAs, from lung adenocarcinoma patients, a histologic subtype of non-small-cell lung cancer, with deep learning neural networks and other state-of-the-art classification methods. The purpose of our paper is three-fold. First, we compare the classification performance of different versions of deep belief networks with SVMs, decision trees and random forests. Second, we compare the classification capabilities of protein coding and non-coding RNAs. Third, we study the influence of feature selection on the classification performance. RESULTS:As a result, we find that deep belief networks perform at least competitively to other state-of-the-art classifiers. Second, data from non-coding RNAs perform better than coding RNAs across a number of different classification methods. This demonstrates the equivalence of predictive information as captured by non-coding RNAs compared to protein coding RNAs, conventionally used in computational diagnostics tasks. Third, we find that feature selection has in general a negative effect on the classification performance which means that unfiltered data with all features give the best classification results. CONCLUSIONS:Our study is the first to use ncRNAs beyond miRNAs for the computational classification of cancer and for performing a direct comparison of the classification capabilities of protein coding RNAs and non-coding RNAs.
journal_name
BMC Cancerjournal_title
BMC cancerauthors
Smolander J,Stupnikov A,Glazko G,Dehmer M,Emmert-Streib Fdoi
10.1186/s12885-019-6338-1subject
Has Abstractpub_date
2019-12-03 00:00:00pages
1176issue
1issn
1471-2407pii
10.1186/s12885-019-6338-1journal_volume
19pub_type
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