Wx: a neural network-based feature selection algorithm for transcriptomic data

Abstract

Next-generation sequencing (NGS), which allows the simultaneous sequencing of billions of DNA fragments simultaneously, has revolutionized how we study genomics and molecular biology by generating genome-wide molecular maps of molecules of interest. However, the amount of information produced by NGS has made it difficult for researchers to choose the optimal set of genes. We have sought to resolve this issue by developing a neural network-based feature (gene) selection algorithm called Wx. The Wx algorithm ranks genes based on the discriminative index (DI) score that represents the classification power for distinguishing given groups. With a gene list ranked by DI score, researchers can institutively select the optimal set of genes from the highest-ranking ones. We applied the Wx algorithm to a TCGA pan-cancer gene-expression cohort to identify an optimal set of gene-expression biomarker candidates that can distinguish cancer samples from normal samples for 12 different types of cancer. The 14 gene-expression biomarker candidates identified by Wx were comparable to or outperformed previously reported universal gene expression biomarkers, highlighting the usefulness of the Wx algorithm for next-generation sequencing data. Thus, we anticipate that the Wx algorithm can complement current state-of-the-art analytical applications for the identification of biomarker candidates as an alternative method. The stand-alone and web versions of the Wx algorithm are available at https://github.com/deargen/DearWXpub and https://wx.deargendev.me/ , respectively.

Identification of Novel microRNA Prognostic Markers Using Cascaded Wx, a Neural Network-Based Framework, in Lung Adenocarcinoma Patients

“…Machine learning algorithms are useful for analyzing large volumes of data, such as genetic information produced by next-generation sequencing (NGS) technologies. Support vector machines [26], decision trees [27], and random forest [28] algorithms have been frequently adopted to extract prognostic features from high-throughput NGS profiling data [29][30][31][32]. Recently, we proposed that the CWx framework demonstrated enhanced feature-selection efficiency and increased accuracy in prognostic predictability as compared with previous algorithms [6].…”

Expression of EEF1A1 Is Associated with Prognosis of Patients with Colon Adenocarcinoma

“…The Wx algorithm ranks genes based on the discriminative index score, which reflects the classification power of distinction between groups (e.g., cancer vs. normal). The detailed method has been described previously [4].…”

“…To identify accurate and definitive molecular biomarkers for human malignancies, we previously developed a neural network-based model, Wx, which is optimized to select gene sets based on the discriminative index between combination pairs of K class [4]. In our previous report, we applied our Wx model to a pan-cancer cohort from The Cancer Genome Atlas (TCGA) RNA-Seq dataset, consisting of 12 different types of cancer and normal samples, and obtained 14 genes distinguishing cancer tissues from normal tissues with high accuracy.…”

The identification of co-expressed gene modules in Streptococcus pneumonia from colonization to infection to predict novel potential virulence genes

“…Although other approaches such as differentially coexpressed module identification [26] and Atomic Regulons can be used [27], the SPD algorithm can compare multiple niches simultaneously. Though the feature selection algorithms [28] can detect gene alterations in multiple conditions, they ignore gene-gene interactions and thus were not suitable for our study.…”