Cancer Characteristic Gene Selection via Sample Learning Based on Deep Sparse Filtering

Abstract

Identification of characteristic genes associated with specific biological processes of different cancers could provide insights into the underlying cancer genetics and cancer prognostic assessment. It is of critical importance to select such characteristic genes effectively. In this paper, a novel unsupervised characteristic gene selection method based on sample learning and sparse filtering, Sample Learning based on Deep Sparse Filtering (SLDSF), is proposed. With sample learning, the proposed SLDSF can better represent the gene expression level by the transformed sample space. Most unsupervised characteristic gene selection methods did not consider deep structures, while a multilayer structure may learn more meaningful representations than a single layer, therefore deep sparse filtering is investigated here to implement sample learning in the proposed SLDSF. Experimental studies on several microarray and RNA-Seq datasets demonstrate that the proposed SLDSF is more effective than several representative characteristic gene selection methods (e.g., RGNMF, GNMF, RPCA and PMD) for selecting cancer characteristic genes.

A Hybrid Gene Selection Method Based on ReliefF and Ant Colony Optimization Algorithm for Tumor Classification

“…14 Lung12600203(Adeno(139), NORM(17), Squamous(21), COID(20), SMCL(6))5Liu et al . 6 Brain1200050(Tumour (20), Normal (30))2Li et al . 40 SRBCT230883(EWS(29), BL(11), NB(18), RMS(25))4Lu et al .…”

“…These studies are of tremendous importance for accurate cancer diagnosis and subtype recognition. Because of the limited availability of effective samples compared to thousands or even tens of thousands of genes in microarray data, many computational methods fail to identify a small portion of important genes, and it increases learning costs and deteriorates learning performance 6,7 . In general, cancer classification for microarray data involves data collection, preprocessing, gene selection, and so on.…”

Identification of key modules and hub genes for small-cell lung carcinoma and large-cell neuroendocrine lung carcinoma by weighted gene co-expression network analysis of clinical tissue-proteomes

“…The co-translational targeting of proteins by SRP was found to be differentially activated in LCNEC. Recently, the novel unsupervised deep learning approach namely, Sample Learning based on Deep Sparse Filtering (SLDSF) [52], was applied for the selection of genes characteristic to the lung cancer dataset including 12,600 genes from 203 lung cancer tissue samples (Bhattacharjee et al [53]). Nuclear-transcribed mRNA catabolic processes, nonsense-mediated decay, SRP-dependent co-translational protein targeting to membrane, and translational termination, all of which are closely related to lung cancer, were reported as highly significant GO terms (top 10 p -values) corresponding to the selected characteristic genes.…”

Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions

“…It is a combination of kernel approach and spectrum-based feature evaluation. Also, Liu et al (2018), developed a Deep Sparse Filtering model considering the deep structures, enhancing the results. Many studies on natureinspired gene selection and the (Guo et al, 2017) implemented the MGSACO to minimize redundancy, thereby increasing the dataset's relevancy.…”