LaCOme: Learning the latent convolutional patterns among transcriptomic features to improve classifications.

ABSTRACT

OMIC is a novel approach that analyses entire genetic or molecular profiles in humans and other organisms. It involves identifying and quantifying biological molecules that contribute to a species' structure, function, and dynamics. Finding the secrets of OMIC is like deciphering the biochemical code, but building data-driven models to mine the hidden phenotypic trait information has been a research hotspot. Transcriptome analysis is a popular biological technology for characterizing living systems' overall health, including cells and tissues. Individual transcript expression levels are known to be correlated with those of other transcripts. Nevertheless, most computational studies do not fully exploit these inter-feature correlations. Differential expression analyses, for example, assume that the expression levels of the transcripts are independent. Thus, we propose extracting these inter-feature correlations using the convolutional neural network (CNN) and transforming the transcriptomic features into a new space of convolutional transcriptomic (LaCOme) features. On most transcriptomic datasets in use, a series of comprehensive experiments have demonstrated that engineered LaCOme features outperform the original transcriptomic features in classification performances. Based on experimental results, OMIC data from biological samples could be further enriched using CNN to enhance computational analysis results. Also, feature rough screening can be used to extract valuable information from OMIC, regardless of the algorithm used to select features. It may always be better to create a novel feature than to keep the original. Furthermore, we investigated the feasibility of the feature construction method through cross-validation and independent verification, hoping to develop a more efficient and effective method.